HYPOTHESIS SCORING METHOD BASED ON CAUSAL RELATIONSHIP

§101 §112

DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Style In this action unitalicized bold is used for claim language, while italicized bold is used for emphasis. Applicant Reply “The claims may be amended by canceling particular claims, by presenting new claims, or by rewriting particular claims as indicated in 37 CFR 1.121(c). The requirements of 37 CFR 1.111(b) must be complied with by pointing out the specific distinctions believed to render the claims patentable over the references in presenting arguments in support of new claims and amendments. . . . The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. . . . An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” MPEP § 714.02. Generic statements or listing of numerous paragraphs do not “specifically point out the support for” claim amendments. “With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4, 83 USPQ2d 1373, 1376, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a ‘simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.’)” MPEP § 2163(II)(A). Non-Obvious Subject Matter under 35 U.S.C. § 103 Claims 1-20 are non-obvious in view of the prior art. This is not an indication of allowable subject matter under any other section. The following is a list of the closest prior art: Azer (Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses; 2020) teaches using graphs to evaluate hypotheses based on natural language. But Azer generally fails to teach the combination of operations including “generating a causal relationship model utilizing a plurality of hypotheses and a causal relationship between each hypothesis of two or more pairs of hypotheses, wherein the plurality of hypotheses comprises the two or more pairs of hypotheses; determining, by applying a scoring function to news articles, a specific pro sentiment score, a general pro sentiment score, a specific con sentiment score, and a general con sentiment score for each hypothesis of the plurality of hypotheses, wherein the determining of the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score includes determining a weight for a respective hypothesis of the plurality of hypotheses based on a number of the news articles associated with the respective hypothesis; determining, based on the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score, a total pro sentiment score and a total con sentiment score for each hypothesis of the plurality of hypotheses; assigning the total pro sentiment score and the total con sentiment score to each hypothesis of the plurality of hypotheses; propagating, based on axioms for causation types, the total pro sentiment score and the total con sentiment score from leaf hypotheses to a root hypothesis to test the total pro sentiment score and the total con sentiment score for reasonableness, wherein the plurality of hypotheses includes the leaf hypotheses and the root hypothesis, the total pro sentiment score and the total con sentiment score are reset to 50/50 based on a violation of a supervenience axiom from the axioms; the plurality of hypotheses includes a first hypothesis, a second hypothesis, a third hypothesis, a fourth hypothesis, and a fifth hypothesis, the total pro sentiment score and the total con sentiment score of the first hypothesis and the total pro sentiment score and the total con sentiment score of the second hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the third hypothesis by: determining a first value by adding a result of subtracting the total con sentiment score of the first hypothesis from the total pro sentiment score of the first hypothesis to a result of subtracting the total con sentiment score of the second hypothesis from the total pro sentiment score of the second hypothesis; determining a second value by adding the total pro sentiment score of the third hypothesis to the total con sentiment score of the third hypothesis; determining a propagated pro sentiment score of the third hypothesis by dividing a result of adding the total pro sentiment score of the third hypothesis to the determined first value by a result of adding the determined second value to the determined first value; and determining a propagated con sentiment score of the third hypothesis by dividing the total con sentiment score of the third hypothesis by the result of adding the determined second value to the determined first value, each of the first hypothesis and the second hypothesis has the causal relationship of a positive contribution with the third hypothesis, a propagated pro sentiment score and a propagated con sentiment score of the fourth hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the fifth hypothesis, and the fourth hypothesis has the causal relationship of a negative contribution with the fifth hypothesis; and presenting, based on the propagating, final pro sentiment score and final con sentiment score representing a prediction of the plurality of hypotheses to a user,” as substantially recited in all independent claims. Baum (2015/0363702) teaches “[0128] The motivation for supporting the axiomatize feature is that users may have irreconcilable differences over the truth of some statements. Axiomatization allows them to define a variable representing the truth of a node, and to rate the rest of the graph under the assumption the axiomatized node is true or TE, and also to work out the rating consequences under the assumption it is false or NOT-TE. . . . [0132] The system may further maintain an additional datum “True” or “False” for each TE C node according to whether its condition would be true under the assumption that all the axiomatized nodes are true or respectively is false under this assumption that all the axiomatized nodes are true. As entries are made and edited in the data structure, the system may update the rating of nodes according to an algorithm. . . . [0343] An edge may be classified as Pro if it is an argument or assumption for a Pro node or if it is a challenge for a Con node. An edge may be classified as Con if it is an argument or assumption for a Con node or a challenge for a Pro node.” Baum ¶¶128, 132, 343. This teaches using trees to determine whether or not statements are true and references a pro and con arguments. But this generally fails to teach the combination of operations including “generating a causal relationship model utilizing a plurality of hypotheses and a causal relationship between each hypothesis of two or more pairs of hypotheses, wherein the plurality of hypotheses comprises the two or more pairs of hypotheses; determining, by applying a scoring function to news articles, a specific pro sentiment score, a general pro sentiment score, a specific con sentiment score, and a general con sentiment score for each hypothesis of the plurality of hypotheses, wherein the determining of the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score includes determining a weight for a respective hypothesis of the plurality of hypotheses based on a number of the news articles associated with the respective hypothesis; determining, based on the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score, a total pro sentiment score and a total con sentiment score for each hypothesis of the plurality of hypotheses; assigning the total pro sentiment score and the total con sentiment score to each hypothesis of the plurality of hypotheses; propagating, based on axioms for causation types, the total pro sentiment score and the total con sentiment score from leaf hypotheses to a root hypothesis to test the total pro sentiment score and the total con sentiment score for reasonableness, wherein the plurality of hypotheses includes the leaf hypotheses and the root hypothesis, the total pro sentiment score and the total con sentiment score are reset to 50/50 based on a violation of a supervenience axiom from the axioms; the plurality of hypotheses includes a first hypothesis, a second hypothesis, a third hypothesis, a fourth hypothesis, and a fifth hypothesis, the total pro sentiment score and the total con sentiment score of the first hypothesis and the total pro sentiment score and the total con sentiment score of the second hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the third hypothesis by: determining a first value by adding a result of subtracting the total con sentiment score of the first hypothesis from the total pro sentiment score of the first hypothesis to a result of subtracting the total con sentiment score of the second hypothesis from the total pro sentiment score of the second hypothesis; determining a second value by adding the total pro sentiment score of the third hypothesis to the total con sentiment score of the third hypothesis; determining a propagated pro sentiment score of the third hypothesis by dividing a result of adding the total pro sentiment score of the third hypothesis to the determined first value by a result of adding the determined second value to the determined first value; and determining a propagated con sentiment score of the third hypothesis by dividing the total con sentiment score of the third hypothesis by the result of adding the determined second value to the determined first value, each of the first hypothesis and the second hypothesis has the causal relationship of a positive contribution with the third hypothesis, a propagated pro sentiment score and a propagated con sentiment score of the fourth hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the fifth hypothesis, and the fourth hypothesis has the causal relationship of a negative contribution with the fifth hypothesis; and presenting, based on the propagating, final pro sentiment score and final con sentiment score representing a prediction of the plurality of hypotheses to a user,” as substantially recited in all independent claims. Frank (2016/0300252) teaches using trees to assign risk, but generally fails to teach the combination of operations including “generating a causal relationship model utilizing a plurality of hypotheses and a causal relationship between each hypothesis of two or more pairs of hypotheses, wherein the plurality of hypotheses comprises the two or more pairs of hypotheses; determining, by applying a scoring function to news articles, a specific pro sentiment score, a general pro sentiment score, a specific con sentiment score, and a general con sentiment score for each hypothesis of the plurality of hypotheses, wherein the determining of the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score includes determining a weight for a respective hypothesis of the plurality of hypotheses based on a number of the news articles associated with the respective hypothesis; determining, based on the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score, a total pro sentiment score and a total con sentiment score for each hypothesis of the plurality of hypotheses; assigning the total pro sentiment score and the total con sentiment score to each hypothesis of the plurality of hypotheses; propagating, based on axioms for causation types, the total pro sentiment score and the total con sentiment score from leaf hypotheses to a root hypothesis to test the total pro sentiment score and the total con sentiment score for reasonableness, wherein the plurality of hypotheses includes the leaf hypotheses and the root hypothesis, the total pro sentiment score and the total con sentiment score are reset to 50/50 based on a violation of a supervenience axiom from the axioms; the plurality of hypotheses includes a first hypothesis, a second hypothesis, a third hypothesis, a fourth hypothesis, and a fifth hypothesis, the total pro sentiment score and the total con sentiment score of the first hypothesis and the total pro sentiment score and the total con sentiment score of the second hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the third hypothesis by: determining a first value by adding a result of subtracting the total con sentiment score of the first hypothesis from the total pro sentiment score of the first hypothesis to a result of subtracting the total con sentiment score of the second hypothesis from the total pro sentiment score of the second hypothesis; determining a second value by adding the total pro sentiment score of the third hypothesis to the total con sentiment score of the third hypothesis; determining a propagated pro sentiment score of the third hypothesis by dividing a result of adding the total pro sentiment score of the third hypothesis to the determined first value by a result of adding the determined second value to the determined first value; and determining a propagated con sentiment score of the third hypothesis by dividing the total con sentiment score of the third hypothesis by the result of adding the determined second value to the determined first value, each of the first hypothesis and the second hypothesis has the causal relationship of a positive contribution with the third hypothesis, a propagated pro sentiment score and a propagated con sentiment score of the fourth hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the fifth hypothesis, and the fourth hypothesis has the causal relationship of a negative contribution with the fifth hypothesis; and presenting, based on the propagating, final pro sentiment score and final con sentiment score representing a prediction of the plurality of hypotheses to a user,” as substantially recited in all independent claims. Textor (Robust causal inference using directed acyclic graphs: the R package ‘dagitty’, 2017) teaches using graphs to evaluate hypotheses. But Textor generally fails to teach the combination of operations including “generating a causal relationship model utilizing a plurality of hypotheses and a causal relationship between each hypothesis of two or more pairs of hypotheses, wherein the plurality of hypotheses comprises the two or more pairs of hypotheses; determining, by applying a scoring function to news articles, a specific pro sentiment score, a general pro sentiment score, a specific con sentiment score, and a general con sentiment score for each hypothesis of the plurality of hypotheses, wherein the determining of the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score includes determining a weight for a respective hypothesis of the plurality of hypotheses based on a number of the news articles associated with the respective hypothesis; determining, based on the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score, a total pro sentiment score and a total con sentiment score for each hypothesis of the plurality of hypotheses; assigning the total pro sentiment score and the total con sentiment score to each hypothesis of the plurality of hypotheses; propagating, based on axioms for causation types, the total pro sentiment score and the total con sentiment score from leaf hypotheses to a root hypothesis to test the total pro sentiment score and the total con sentiment score for reasonableness, wherein the plurality of hypotheses includes the leaf hypotheses and the root hypothesis, the total pro sentiment score and the total con sentiment score are reset to 50/50 based on a violation of a supervenience axiom from the axioms; the plurality of hypotheses includes a first hypothesis, a second hypothesis, a third hypothesis, a fourth hypothesis, and a fifth hypothesis, the total pro sentiment score and the total con sentiment score of the first hypothesis and the total pro sentiment score and the total con sentiment score of the second hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the third hypothesis by: determining a first value by adding a result of subtracting the total con sentiment score of the first hypothesis from the total pro sentiment score of the first hypothesis to a result of subtracting the total con sentiment score of the second hypothesis from the total pro sentiment score of the second hypothesis; determining a second value by adding the total pro sentiment score of the third hypothesis to the total con sentiment score of the third hypothesis; determining a propagated pro sentiment score of the third hypothesis by dividing a result of adding the total pro sentiment score of the third hypothesis to the determined first value by a result of adding the determined second value to the determined first value; and determining a propagated con sentiment score of the third hypothesis by dividing the total con sentiment score of the third hypothesis by the result of adding the determined second value to the determined first value, each of the first hypothesis and the second hypothesis has the causal relationship of a positive contribution with the third hypothesis, a propagated pro sentiment score and a propagated con sentiment score of the fourth hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the fifth hypothesis, and the fourth hypothesis has the causal relationship of a negative contribution with the fifth hypothesis; and presenting, based on the propagating, final pro sentiment score and final con sentiment score representing a prediction of the plurality of hypotheses to a user,” as substantially recited in all independent claims. Cao (A Bottom-Up DAG Structure Extraction Model for Math Word Problems, 2021) teaches using directed acyclic graphs to convert natural language into equations. But Cao generally fails to teach the combination of operations including “generating a causal relationship model utilizing a plurality of hypotheses and a causal relationship between each hypothesis of two or more pairs of hypotheses, wherein the plurality of hypotheses comprises the two or more pairs of hypotheses; determining, by applying a scoring function to news articles, a specific pro sentiment score, a general pro sentiment score, a specific con sentiment score, and a general con sentiment score for each hypothesis of the plurality of hypotheses, wherein the determining of the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score includes determining a weight for a respective hypothesis of the plurality of hypotheses based on a number of the news articles associated with the respective hypothesis; determining, based on the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score, a total pro sentiment score and a total con sentiment score for each hypothesis of the plurality of hypotheses; assigning the total pro sentiment score and the total con sentiment score to each hypothesis of the plurality of hypotheses; propagating, based on axioms for causation types, the total pro sentiment score and the total con sentiment score from leaf hypotheses to a root hypothesis to test the total pro sentiment score and the total con sentiment score for reasonableness, wherein the plurality of hypotheses includes the leaf hypotheses and the root hypothesis, the total pro sentiment score and the total con sentiment score are reset to 50/50 based on a violation of a supervenience axiom from the axioms; the plurality of hypotheses includes a first hypothesis, a second hypothesis, a third hypothesis, a fourth hypothesis, and a fifth hypothesis, the total pro sentiment score and the total con sentiment score of the first hypothesis and the total pro sentiment score and the total con sentiment score of the second hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the third hypothesis by: determining a first value by adding a result of subtracting the total con sentiment score of the first hypothesis from the total pro sentiment score of the first hypothesis to a result of subtracting the total con sentiment score of the second hypothesis from the total pro sentiment score of the second hypothesis; determining a second value by adding the total pro sentiment score of the third hypothesis to the total con sentiment score of the third hypothesis; determining a propagated pro sentiment score of the third hypothesis by dividing a result of adding the total pro sentiment score of the third hypothesis to the determined first value by a result of adding the determined second value to the determined first value; and determining a propagated con sentiment score of the third hypothesis by dividing the total con sentiment score of the third hypothesis by the result of adding the determined second value to the determined first value, each of the first hypothesis and the second hypothesis has the causal relationship of a positive contribution with the third hypothesis, a propagated pro sentiment score and a propagated con sentiment score of the fourth hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the fifth hypothesis, and the fourth hypothesis has the causal relationship of a negative contribution with the fifth hypothesis; and presenting, based on the propagating, final pro sentiment score and final con sentiment score representing a prediction of the plurality of hypotheses to a user,” as substantially recited in all independent claims. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) and the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? All claims are found to be directed to one of the four statutory categories, unless otherwise indicated in this action. With respect to claims 15-20, see Spec. ¶127. Step 2A Prongs One and Two (Alice Step 1): According to Office guidance, claims that read on math do not recite an abstract idea at step 2A1, when the claims fail to refer to the math by name.1 The MPEP also equates “recit[ing] a judicial exception” with “state[ing]” or “describ[ing]” an abstract idea in the claims.2 Consistent with this guidance, an abstract idea may be first recited in a dependent claim even though the independent claims read on that abstract idea. Claim limitations which recite any of the abstract idea groupings set forth in the manual are found to be directed, as a whole, to an abstract idea unless otherwise indicated.3 The claims do not recite additional elements that integrate the abstract ideas into a practical application.4 To confer patent eligibility to an otherwise abstract idea, claims may recite a specific means or method of solving a specific problem in a technological field.5 Independent Claims 1. A computer-implemented method of hypothesis scoring based on causal relationships, the computer-implemented method comprising: (The claim as a whole is directed to a combination of mental and mathematical processes, with no clear technological improvement. This limitation is merely in instruction to apply abstract ideas recited below using generic computer components.) generating a causal relationship model utilizing a plurality of hypotheses and a causal relationship between each hypothesis of two or more pairs of hypotheses wherein the plurality of hypotheses comprises the two or more pairs of hypotheses; determining, by applying a scoring function to news articles, a specific pro sentiment score, a general pro sentiment score, a specific con sentiment score, and a general con sentiment score for each hypothesis of the plurality of hypotheses, wherein the determining of the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score includes determining a weight for a respective hypothesis of the plurality of hypotheses based on a number of the news articles associated with the respective hypothesis; (This reads on both math and on a mental process. Note that the Specification describes a user creating causal relationship model 110, including implementation as a DAG including various aspects of the graph. See Spec. ¶24 (emphasis added) (“In one or more embodiments, a user can create a causal relationship model 100 with hypotheses. The causal relationship models can be expressed as directed acyclic graphs (DAGs) consisting of nodes110, with each node 110 associated with one of the hypothesis statements 121, 122, 123, 124, 125, 126, 127, 128, 129 (collectively referred to as "12X"), and edges 130 relating to the causations between hypotheses 12X, where three types of causation are introduced for expressing fine-grain causalities. The user can express causal relationship knowledge in the causal relationship model 100 by using three types of causations. Axioms can be used to formally express the degree of reasonable support for the hypotheses, as related by the three types of causation, where the axioms can be incorporated into the edges 130. External sentiment scores can be constrained to satisfy the axioms.”) Limiting the data used to news articles merely limits to a data environment associated with a field of use.) determining, based on the specific pro sentiment score, the general pro sentiment score, the specific con sentiment score, and the general con sentiment score, a total pro sentiment score and a total con sentiment score for each hypothesis of the plurality of hypotheses; (This reads on both math, and on a mental process.) assigning the total pro sentiment score and the total con sentiment score to each hypothesis of the plurality of hypotheses; (This reads on both math, and on a mental process.) propagating, based on axioms for causation types, the total pro sentiment score and the total con sentiment score from leaf hypotheses to a root hypothesis to test the total pro sentiment score and the total con sentiment score for reasonableness, wherein the plurality of hypotheses includes the leaf hypotheses and the root hypothesis, the total pro sentiment score and the total con sentiment score are reset to 50/50 based on a violation of a supervenience axiom from the axioms (This reads on implementing mental/mathematical processus using generic computer components.) the plurality of hypotheses includes a first hypothesis, a second hypothesis, a third hypothesis, a fourth hypothesis, and a fifth hypothesis, the total pro sentiment score and the total con sentiment score of the first hypothesis and the total pro sentiment score and the total con sentiment score of the second hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the third hypothesis by: (Propagation is interpreted as referring to mathematical operations that relate the various claim elements. This is consistent with the descriptions of the operations below. However, if propagating were interpreted to require transmission of data, note that this would be extra-solution activity.) determining a first value by adding a result of subtracting the total con sentiment score of the first hypothesis from the total pro sentiment score of the first hypothesis to a result of subtracting the total con sentiment score of the second hypothesis from the total pro sentiment score of the second hypothesis; (This reads on implementing mental/mathematical processus using generic computer components.) determining a second value by adding the total pro sentiment score of the third hypothesis to the total con sentiment score of the third hypothesis; (This reads on implementing mental/mathematical processus using generic computer components.) determining a propagated pro sentiment score of the third hypothesis by dividing a result of adding the total pro sentiment score of the third hypothesis to the determined first value by a result of adding the determined second value to the determined first value; (This reads on implementing mental/mathematical processus using generic computer components.) and determining a propagated con sentiment score of the third hypothesis by dividing the total con sentiment score of the third hypothesis by the result of adding the determined second value to the determined first value, each of the first hypothesis and the second hypothesis has the causal relationship of a positive contribution with the third hypothesis, a propagated pro sentiment score and a propagated con sentiment score of the fourth hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the fifth hypothesis, and the fourth hypothesis has the causal relationship of a negative contribution with the fifth hypothesis; (This reads on implementing mental/mathematical processus using generic computer components.) and presenting, based on the propagating, final pro sentiment score and final con sentiment score representing a prediction of the plurality of hypotheses to a user. (Outputting of the result of the claimed mental processes is mere extra-solution activity.) Independent claim 8 is rejected for the reasons given in the rejection of claim 1. The claim also recites “[a] computer system implementing a hypothesis scoring method based on causal relationships, the computer system comprising: one or more processors; a display operationally coupled to the one or more processors; and a computer memory operationally coupled to the one or more processors, wherein a hypothesis scoring tool is stored in the computer memory and the hypothesis scoring tool is configured to” carry out the operations recited in claim 1. This is merely an instruction to apply the judicial exception using generic computer components. Independent claim 15 is rejected for the reasons given in the rejection of claim 1. The claim also recites “[a] computer program product for implementing a hypothesis scoring method based on causal relationships, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions are executable by a processor to cause a computer to” carry out the operations recited in claim 1. This is merely an instruction to apply the judicial exception using generic computer components. Step 2B (Alice Step 2): The rejected claims do not recite additional elements that amount to significantly more than the judicial exception. All additional limitations that do not integrate the claimed judicial exception into a practical application also fail to amount to significantly more, for the reasons given at step 2A2. All limitations found to be extra-solution activity at step 2A2 are found to be WURC, including limitations that read on mere data gathering, data storage, and data input/output/transfer. Claims 1, 8, and 15 substantially recite “presenting, based on the propagating, final pro sentiment score and final con sentiment score representing a prediction of the plurality of hypotheses to a user” and “the total con sentiment score of the second hypothesis are propagated to the total pro sentiment score and the total con sentiment score of the third hypothesis[.]” Outputting and transmission of information is WURC as explained in this paragraph. Should any other claim limitations be rejected at step 2A1 as extra-solution activity but omitted in the section directly above, it should be understood that such limitations are also found to be WURC at this step. Generic data input/output, storage, repetitive processing operations, and generic display of information and have been found to be generic WURC operations that do not transform the abstract idea into patent eligible subject matter, at the Alice step two analysis.6 Other aspects of generic computing have also been found to be WURC.7 Further, the description itself may provide support for a finding that claim elements are WURC. The analysis under § 112(a) as to whether a claim element is “so well-known that it need not be described in detail in the patent specification” is the same as the analysis as to whether the claim element is widely prevalent or in common use.8 Similarly, generic descriptions in the Specification of claimed components and features has been found to support a conclusion that the claimed components were conventional.9 Improvements to the relevant technology may support a finding that the claims include a patent eligible inventive concept. But some mechanism that results in any asserted improvements must be recited in the claim, and the Specification must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing the improvement.10 This applies to the dependent claims below. Dependent Claims: 2. The computer-implemented method of claim 1, wherein the causal relationship is a contribution, an implication, or a supervenience that determines a method of the propagating of the total pro sentiment and the total con score. (This merely limits the way the mental process or math is implemented, but does not recite something other than a mental process or mathematical operation.) 3. The computer-implemented method of claim 2, wherein each of a contribution axiom, an implication axiom, and the supervenience axiom is applied to the total pro sentiment score and the total con sentiment score for each propagation and the axioms include the contribution axiom, the implication axiom, and the supervenience axiom. (This merely limits the way the mental process or math is implemented, but does not recite something other than a mental process or mathematical operation.) 4. The computer-implemented method of claim 3, wherein the causal relationship model includes one or more topic words including a specific name with each hypothesis of the plurality of hypotheses. (This limits the data used in the mental process, but the claim language merely limits to a field of use.) 5. The computer-implemented method of claim 4, wherein the total pro sentiment score and the total con sentiment score are calculated for each hypothesis of the plurality of hypotheses both with and without the specific name to produce g-scores and s-scores, and the g-scores and s-scores are merged. (This reads on a mental process, and on a mathematical operation.) 7. The computer-implemented method of claim 5, wherein the total pro sentiment score and the total con sentiment score are propagated by: subtracting smaller of the total pro sentiment score or the total con sentiment score from larger of the total pro sentiment score or total con sentiment score to obtain a difference value, and adding the difference value to a same total pro sentiment score or a same total con sentiment score of a down-stream hypothesis. (This reads on both a mathematical operation and on a mental process.) For Rejections of claims 9-12 and 14, see rejection of claims 2-5 and 7, respectively. For Rejections of claims 16-19, see rejection of claims 2-5, respectively. All dependent claims are rejected as containing the material of the claims from which they depend. Response to Arguments Applicant's arguments filed 08/12/2026 have been fully considered but they are not persuasive. Rejections under § 112: No specific arguments are offered. Rejections under § 101: Responses to Remarks directed to step 2A1. Applicant “submits that the above claimed features are directed to a computer technology and inextricably tied to a machine” and concludes that they “cannot be characterized as merely an abstract mathematical concept or mental process.” Rem. 15-16. The Remarks fail to provide any basis for this position. Responses to Remarks directed to step 2A1. Applicant submits that the technique of “propagating the total pro and con scores from an upstream hypothesis to a downstream hypothesis improves the determination of a plurality of hypotheses within a causal relationship model by allowing evidence associated with causally related events to influence one another in a manner consistent with their known relationships.” Rem. 17. Applicant distinguishes the technique of “adjusts the downstream hypothesis using the net support or opposition provided by upstream hypotheses that are known to contribute to or imply the downstream event” from “evaluating each hypothesis independently based only on its own externally generated score,” and asserts a reduction in “the effect of sparse, noisy, or inconsistent external data” leading to a more reliable overall determination of the plurality of hypotheses.” Rem. 17-18. The Remarks then quote language from within a range of roughly 100 paragraphs, stringing the language from various sections of the Specification using ellipses, in a format more consistent with quoting language from within contiguous paragraphs. Rem. 17-18. Applicant subsequently contends that the Specification describes a technical solution to a technological problem. Rem. 18-19. Applicant’s technological problem is “limited or noisy data” which result in “unreliable sentiment scores.” Rem. 18-19. Nothing in the remarks addresses the chasm between “limited or noisy data” and any particular technological problem. It must be noted here the “limited or noisy data” is what happens when one person asks a question and the other person makes up an answer. The solution is mentally evaluating the source of the answer. Similarly, people make due with “limited data” in virtually every decision we make. By itself, the problem of imperfect data is not remotely technological in nature. Applicant asserts a solution in “propagating” various “sentiment scores.” Rem. 19. But nothing in the remarks distinguishes this technique from merely giving different combined weights to various hypotheses. Assigning weights to hypotheses, even in combination, remains a mental process. Applicant suggests a “further technical advantage” based on resetting a score to 50/50 when it is internally inconsistent with another score. As best understood, this refers to resetting sentiment scores in response to determining that the score may be the result of imperfect data. Describing the invention using technical sounding language does not make a process technological. Applicant has not submitted any arguments specifically addressing step 2B (Alice step 2). For the foregoing reasons, the Remarks are not persuasive. Conclusion THIS ACTION IS MADE FINAL. 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL M KNIGHT whose telephone number is (571) 272-8646. The examiner can normally be reached Monday - Friday 9-5 ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. PAUL M. KNIGHTPrimary ExaminerArt Unit 2148 /PAUL M KNIGHT/ Primary Examiner, Art Unit 2148 1 This distinction between claims which read on math and claims which recite an abstract idea is based on official USPTO Guidance. The 2019 Subject Matter Eligibility (SME) Examples instructs examiners that a claim reciting “training the neural network” where the background describes training as “using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network” “does not recite any mathematical relationships, formulas, or calculations.” See 2019 SME Example 39, PP. 8-9 (emphasis added). In this example, the plain meaning of “training the neural network” read in light of the disclosure reads on backpropagation using the gradient of a mathematical loss function. See MPEP § 2111.01. In contrast, the 2024 SME Examples instructs examiners that a claim reciting “training, by the computer, the ANN . . . wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” does recite an abstract idea because “[t]he plain meaning of [backpropagation algorithm and gradient descent algorithm] are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.” 2024 PEG Example 47, PP. 4-6. The Memorandum of August 4, 2025; Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, P. 3 also directs examiners that “training the neural network” recited in Example 39 merely “involve[s] . . . mathematical concepts” and contrasts claim 2 of example 47 as “referring to [specific] mathematical calculations by name[.]” (Emphasis added.) 2 “For instance, the claims in Diehr . . . clearly stated a mathematical equation . . . and the claims in Mayo . . . clearly stated laws of nature . . . such that the claims ‘set forth’ an identifiable judicial exception. Alternatively, the claims in Alice Corp. . . . described the concept of intermediated settlement without ever explicitly using the words ‘intermediated’ or ‘settlement.’” MPEP § 2106.04(II)(A). 3 “By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. . . . If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One.” MPEP § 2106.04(a). See also MPEP 2104(a)(2). 4 Step 2A prongs one and two are evaluated individually, consistent with the framework in the MPEP. Evaluation of relationships between abstract ideas and additional elements in one location promotes clarity of the record. 5 “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. . . . It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” MPEP 2106.04(d)(1). See also Koninklijke KPN N.V. v. Gemalto M2M GmbH, 942 F.3d 1143, 1150-1152 (Fed. Cir. 2019). 6 See MPEP § 2106.05(d)(II) listing operations including “receiving or transmitting data,” “storing and retrieving data in memory,” and “performing repetitive calculations” as WURC. “The claims at issue do not require any nonconventional computer, network, or display components, or even a non-conventional and non-generic arrangement of known, conventional pieces, but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (emphasis added, internal quotes omitted). 7 “But ‘[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of 'well-understood, routine, [and] conventional activities previously known to the industry.’ Content Extraction, 776 F.3d at 1347-48 (quoting Alice, 134 S. Ct at 2359). Here, the server simply receives data, ‘extract[s] classification information . . . from the received data,’ and ‘stor[es] the digital images . . . taking into consideration the classification information.’ See ‘295 patent, col. 10 ll. 1-17 (Claim 17). . . . These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (‘Nearly every computer will include a 'communications controller' and a 'data storage unit' capable of performing the basic calculation, storage, and transmission functions required by the method claims.’); Content Extraction, 776 F.3d at 1345, 1348 (‘storing information’ into memory, and using a computer to ‘translate the shapes on a physical page into typeface characters,’ insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324-25 (generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’ fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a ‘database’ and ‘a communication medium’ ‘are all generic computer elements’); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (‘That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.’).” TLI Commc'ns LLC v. AV Auto., LLC, 823 F.3d 607, 614 (Fed. Cir. 2016), Emphasis Added. 8 “The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph)[.]” MPEP § 2106.05(d)(I). 9 “Similarly, claim elements or combinations of claim elements that are routine, conventional or well-understood cannot transform the claims. (Citing BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290-1291 (Fed. Cir. 2018)). When the patent's specification ‘describes the components and features listed in the claims generically,’ it ‘support[s] the conclusion that these components and features are conventional.’ Weisner v. Google LLC, 51 F.4th 1073, 1083-84 (Fed. Cir. 2022); see also Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1357-58 (Fed. Cir. 2024).” Broadband iTV, Inc. v. Amazon.com, Inc., 113 F.4th 1359 (Fed. Cir. 2024) 10 “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” MPEP § 2106.05(a).