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 .
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process. This judicial exception is not integrated into a practical application nor amount to significantly more because the additional elements are mere generic computer hardware in combination with extra-solution activity. See below for further claim analysis details.
Claim 1
Step 1: The claim recites a method, therefore, it falls into the statutory category of a method.
Step 2A Prong 1: The claim recites, inter alia:
a model construction step of constructing a classification and prediction model having a tree structure for classifying and predicting the event based on the feature amount vector; (This amounts to a mental step of observation, judgment and evaluation wherein a user creates a decision tree, can be done with the aid of pen and paper.)
an interaction score calculation step of calculating an interaction score in which a degree of association of an interaction between the feature amounts with the event is scored based on a position of the feature amount appearing in a node constituting the classification and prediction model, and a position of the feature amount in the classification and prediction model in which the position of the feature amount appearing in the node has been shuffled; (This is a mental process of observation, evaluation and judgement. It comprises a user looking at decision tree and counting the number of times to features (nodes) appear in the same branch of a decision tree. The position nodes are tree are shuffled or mixed up, and the co-occurrence of features are counted gain and summed up. Can be done with the aid of pen and paper wherein use shuffles nodes in decision tree, counts co-occurrence, and calculate interaction score from them.)
wherein the calculated interaction score is further based on information associated with interaction among a plurality of combinations of the feature amounts associated with the event (This is a mental process of observation, evaluation and judgement wherein a user calculates how many times features amount together, can be done with the aid of pen and paper. Using equation 1.)
wherein the interaction score calculation step further comprises a shuffle step of shuffling the feature amounts appearing in the decision tree according to the plurality of shuffle rules comprising a first shuffle rule of performing the shuffle for each decision tree and a second shuffle rule of performing the shuffle for the feature amounts that appear in each of the plurality of branch nodes of a target decision tree. (This is a mental process of observation, evaluation and judgement wherein a user creates decision tress and shuffle each tree and the nodes in the decision trees to create all the different possibilities and combinations, and counting them. Can do with aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
an arithmetic device including a processor (This is cited a high-level of generality and results in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
acquiring data including a feature amount vector which is a set of numerical values of feature amounts and is an explanatory variable, and information of an event which is an objective variable, and (This limitation amounts to data gather or receiving data, which is extra-solution activity, see MPEP 2106.05(g).)
an output step of outputting the calculated interaction score to an output unit.
(This amount to sending data, which is transmitting data and as such extra-solution activity, see MPEP 2106.05(g). The output unit is cited a high level of generality and results in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above”
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “acquiring data including a feature amount vector which is a set of numerical values of feature amounts and is an explanatory variable, and information of an event which is an objective variable,” and “an output step of outputting the calculated interaction score” amounts to transmitting data and is well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The using of an arithmetic device comprising a process and an output unit amounts to using generic hardware as a tool to apply an abstract idea, see MPEP 2106.05(f). When viewing the claim as a whole it does not amount to significantly more than the abstract idea.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 2
Step 1: The claim recites a method, therefore, it falls into the statutory category of a method.
Step 2A Prong 1: The claim recites, inter alia:
in the interaction score calculation step, the following steps are executed: a first search branch number calculation step of calculating the number of search branches, which are routes to a branch node where all of the target feature amounts appear following a route from a root node to a downstream in each of decision trees generated by the random forest, (This is mental step of observation, judgement and evaluation wherein a user considers the branches or paths of a decision tree that have nodes or features the user is interested in. For example, a user could only consider branches that contain nodes A and F. So, the user would count all the paths have both nodes A and F.)
a first addition step in which the number of search branches is added for all the decision trees, (This is a mental step of adding the number of paths containing the nodes of features of interest up for all the decision trees.)
a shuffle step of shuffling the feature amounts appearing in the decision tree for each decision tree, (This is a mental step of observation, judgement and evaluation wherein a user shuffles or mixes the features or nodes of the decision trees. For example, in tree 1 node A is the root node, but after the user shuffles it node B is now the root node of tree 1 with node A being a child node. This can be with aid of pen and paper.)
a second search branch number calculation step of calculating the number of search branches for each decision tree for which the shuffle was performed, (This is mental step of observation, judgement and evaluation wherein a user considers the branches or paths of a decision tree that have nodes or features the user is interested in. For example, a user could only consider branches that contain nodes A and F. So, the user would count all the paths have both nodes A and F.)
a second addition step in which the number of search branches calculated in the second search branch number calculation step is added for all the decision trees, (This is a mental step of adding the number of paths containing the nodes of features of interest up for all the decision trees.)
a mean value calculation step of repeating from the shuffle step to the second addition step a plurality of times and calculating a mean value of the results of the second addition step based on the result of the second addition step, and a subtraction step of subtracting the result of the mean value calculation step from the result of the first addition step. (This is mental step wherein a user observation, evaluation and judgement wherein a user repeats shuffling of nodes or features of a decision tree, counting the number co-occurrence of nodes or feature in a path of a decision tree, adding the paths up and calculating and mean and performing a subtraction. Can be done with aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
Repeating the shuffle step to the second addition step (This step amounts to extra solution activity and performing repetitive calculations.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “Repeating the shuffle step to the second addition step” amounts performing repetitive calculations and is well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199”. When viewing the claim as a whole it does not amount to significantly more than the abstract idea.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 3
Step 1: The claim recites a method, therefore, it falls into the statutory category of a method.
Step 2A Prong 1: The claim recites, inter alia:
The method for calculating an interaction between feature amounts according to claim 2, wherein a division step of calculating a standard deviation with respect to the result of the second addition step based on the results of the second addition step and the mean value calculation step, and dividing the result of the subtraction step by the standard deviation is executed. (This claim amounts to a mental process wherein a user performs math calculations, can be done with the aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, there are no additional elements.
Step 2B:
The claim does not cite an additional elements and thus does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 4
Step 1: The claim recites a method, therefore, it falls into the statutory category of a method.
Step 2A Prong 1: The claim is dependent on claim 1 and includes the abstract ideas cited in claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites “wherein the event can be classified into a predetermined category by a qualitative variable.” (This limitation amounts to a particular type of data to be used or manipulated, and thus is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites “wherein the event can be classified into a predetermined category by a qualitative variable.” (This limitation amounts to a particular type of data to be used or manipulated, and thus is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 5
Step 1: The claim recites a method, therefore, it falls into the statutory category of a method.
Step 2A Prong 1: The claim is dependent on claim 1 and includes the abstract ideas cited in claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites “The method for calculating an interaction between feature amounts according to claim 1, wherein the event has a numerical value.”. (This limitation amounts to a particular type of data to be used or manipulated, and thus is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites “The method for calculating an interaction between feature amounts according to claim 1, wherein the event has a numerical value.”. (This limitation amounts to a particular type of data to be used or manipulated, and thus is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 6
Step 1: The claim recites a method, therefore, it falls into the statutory category of a method.
Step 2A Prong 1: The claim recites:
“evaluating a result to determine whether the interaction between the feature amounts associated with the event is positive or negative” (This limitation amounts to a mental process of observation, evaluation and judgment. It is a user considering there a result and deciding if the result is positive or negative.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites “applying the feature amount to regression analysis”. (This limitation is cited at a high level of generality and results in using a regression model as tool to implement the abstract idea , see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites ““applying the feature amount to regression analysis”. (This limitation is cited at a high level of generality and results in using a regression model as tool to implement the abstract idea , see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 7
Step 1: The claim recites a method, therefore, it falls into the statutory category of a method.
Step 2A Prong 1: The claim is dependent on claim 1 and includes the abstract ideas cited in claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites “wherein the feature amount has a flora structure of gut microbiota and at least one of ingested nutrients and health information as a feature amount.”. (This limitation amounts to a particular type of data to be used or manipulated, and thus is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites “wherein the feature amount has a flora structure of gut microbiota and at least one of ingested nutrients and health information as a feature amount.”. (This limitation amounts to a particular type of data to be used or manipulated, and thus is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 8
Step 1: The claim recites a method, therefore, it falls into the statutory category of a method.
Step 2A Prong 1: The claim is dependent on claim 1 and includes the abstract ideas cited in claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites “wherein the event is information on a predetermined disease”. (This limitation amounts to a particular type of data to be used or manipulated, and thus is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim cites “wherein the event is information on a predetermined disease”. (This limitation amounts to a particular type of data to be used or manipulated, and thus is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 9
Step 1: The claim recites a system, therefore, it falls into the statutory category of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
Perform model steps for constructing a classification and prediction model having a tree structure for classifying and predicting the event based on the feature amount vector; (This amounts to a mental step of observation, judgment and evaluation wherein a user creates a decision tree, can be done with the aid of pen and paper.)
calculating an interaction score in which a degree of association of an interaction between the feature amounts with the event is scored based on a position of the feature amount appearing in a node constituting the classification and prediction model, and a position of the feature amount in the classification and prediction model in which the position of the feature amount appearing in the node has been shuffled; (This is a mental process of observation, evaluation and judgement. It comprises a user looking at decision tree and counting the number of times to features (nodes) appear in the same branch of a decision tree. The position nodes are tree are shuffled or mixed up, and the co-occurrence of features are counted gain and summed up. Can be done with the aid of pen and paper wherein use shuffles nodes in decision tree, counts co-occurrence, and calculate interaction score from them.)
wherein the step of calculating the interaction score if further based on information associated with interaction among a plurality of combinations of the feature amounts associated with the event, and (This is a mental process of observation, evaluation and judgement wherein a user calculates how many times features amount together, can be done with the aid of pen and paper. Using equation 1.)
wherein the step of calculating the interaction score further comprises performing a shuffle step of shuffling the feature amounts appearing in the decision tree according to a plurality of shuffle rules comprising a first shuffle rule of performing the shuffle for each decision tree and a second shuffle rule of performing the shuffle for the feature amounts that appear in each of the plurality of nodes of a target decision tree. (This is a mental process of observation, evaluation and judgement wherein a user creates decision tress and shuffle each tree and the nodes in the decision trees to create all the different possibilities and combinations, and counting them. Can do with aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
A processor and display device; (These are cited a high-level of generality and results in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
acquiring data including a feature amount vector which is a set of numerical values of feature amounts and is an explanatory variable, and information of an event which is an objective variable, and outputting the calculated interaction score to an output unit (These limitations amounts to data gather or receiving data, and sending data, both of which are transmitting data which is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “acquiring data including a feature amount vector which is a set of numerical values of feature amounts and is an explanatory variable, and information of an event which is an objective variable, and outputting the calculated interaction score to an output unit” amounts to transmitting data and is well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The using of “process and display” amounts to using generic hardware as a tool to apply an abstract idea, see MPEP 2106.05(f). When viewing the claim as a whole it does not amount to significantly more than the abstract idea. Also, the step of display output is well-understood, routine and conventional, see MPEP 2106.06(d)(II)(iv) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5 and 8-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yoshida et al. (“SNPInterForest: A New Method For Detecting Epistatic Interactions” – hereinafter Yoshida).
In regards to claim 1, Yoshida et al. discloses a method for calculating an interaction between feature amounts, wherein: (Yoshida abstract cites “We have developed a new method, named SNPInterForest, for identifying epistatic interactions by extending an ensemble learning technique called random forest.” The epistatic interaction is the interaction between feature amounts (SNPs).)
an arithmetic device including a processor is configured to execute the method comprising: (Yoshida page 6 section “Analysis using real GWAS data” cites “It took about 98 hours for SNPInterForest to handle it on a Linux system with a single CPU (CPU: Intel Xeon 2.67 GHz) and a memory of 6 GB.”) executes a model construction step of acquiring data including a feature amount vector which is a set of numerical values of feature amounts and is an explanatory variable, and information of an event which is an objective variable, and ((Yoshida page 7 “Random Forest” section first paragraph cites “The random forest technique is an ensemble learning technique for conducting classification analyses.” and section “SNPInterForest: extention of random forest for detecting epistatic interactions” first paragraph cites “First, the random forest is constructed on case-control data as a classifier to discriminate between cases and controls with SNPs as categorical variables that have three possible values.” This teaches constructing a classification and prediction model (Random forest) based on feature vectors (SNPs) and event labels (case/control disease status). Also, the feature vectors and amounts is disclosed on page 7 section “random forest” last sentence wherein x represent the vector variable values with the value of variable A.)
an interaction score calculation step of calculating an interaction score in which a degree of association of an interaction between the feature amounts with the event is scored based on a position of the feature amount appearing in a node constituting the classification and prediction model, and a position of the feature amount in the classification and prediction model in which the position of the feature amount appearing in the node has been shuffled; and (Yoshida page 8 cites “Therefore, for each SNP combination, the number of simultaneous appearances in the same branches is counted over all trees in the random forest as a measure of its interaction strength.” This teaches an interaction score in which a degree of association of interaction between features amounts scored based on position of the feature amount appearing in a node of the tree as it teaches the features appearing at nodes in the same branch of a tree. Also see equation 4 on page 8 that disclose the equation for this. Yoshida also teaches interaction score of feature amounts when the feature amount of the nodes has been shuffled on page 9 left column first paragraph wherein it cites “First, the SNP positions in the trees in the random forest constructed are randomized with the numbers of the respective SNPs and the topology of the trees kept unchanged. Then, for each SNP combination, the number of simultaneous appearances in the same branches is counted in the same way as for the original random forest.” This is also seen in equation 5 on page 9.)
an output step of outputting the calculated interaction score to an output unit. (Yoshida page 6 left column last sentence cites “SNPInterForest identified two novel interactions from this dataset, which are summarized in Table 5. Information on genes related to the SNPs in these interactions is also provided in Table 6.”, which teaches outputting interaction scores, also see tables 5 and 6.)
wherein the calculated interaction score is further based on information associated with interaction among a plurality of combinations of the feature amounts associated with the event, (Yoshida page 8 cites “Therefore, for each SNP combination, the number of simultaneous appearances in the same branches is counted over all trees in the random forest as a measure of its interaction strength. Specifically, it is defined for a SNP combination G as the summation of the number of appearances of G in a branch b, nb(G), for all branches over all trees in the random forest…” also see equation 4. Also, page 4 right column second paragraph teaches finding interaction score involving more than 2 SNPs and doing for three SNP interaction tuples. Also see tables 1, 2 and 3 for interaction scores for various combinations based on feature amounts.) and wherein the step of calculating the interaction score further comprises performing a shuffle step of shuffling the feature amounts appearing in the decision tree according to a plurality of shuffle rules comprising a first shuffle rule of performing the shuffle for each decision tree and a second shuffle rule of performing the shuffle for the feature amounts that appear in each of the plurality of nodes of a target decision tree. (Yoshida page 9 cites “First, the SNP positions in the trees in the random forest constructed are randomized with the numbers of the respective SNPs and the topology of the trees kept unchanged. Then, for each SNP combination, the number of simultaneous appearances in the same branches is counted in the same way as for the original random forest. The sequence of these processes is repeated 100 times to develop statistical distributions of the baseline levels for respective SNP combinations.” This teaches that the SNP in trees (multiple tree) are randomized with number of SNPs keep unchanged. Thus, you have a number of trees with SNP positions and amount random. It then teaches doing this 100s of times, thus each tree and SNP feature position (feature amount) in each tree, thus satisfying the two rules of shuffling each tree in the forest and feature amount in the nodes of the tree. )
In regards to claim 2, Yoshida discloses the method for calculating an interaction between feature amounts according to claim 1, wherein the classification and prediction model having a tree structure is generated by a random forest, and in the interaction score calculation step, the following steps are executed: (Yoshida page 7 “Random Forest” section first paragraph cites “The random forest technique is an ensemble learning technique for conducting classification analyses.” and section “SNPInterForest: extention of random forest for detecting epistatic interactions” first paragraph cites “First, the random forest is constructed on case-control data as a classifier to discriminate between cases and controls with SNPs as categorical variables that have three possible values.” This teaches constructing a classification and prediction model as a random forest and interaction score is taught on page 8 right column last paragraph – page 9 left column.)
a first search branch number calculation step of calculating the number of search branches, which are routes to a branch node where all of the target feature amounts appear following a route from a root node to a downstream in each of decision trees generated by the random forest, a first addition step in which the number of search branches is added for all the decision trees, (Yoshida page 8 cites “Therefore, for each SNP combination, the number of simultaneous appearances in the same branches is counted over all trees in the random forest as a measure of its interaction strength.” This teaches an interaction score in which a degree of association of interaction between features amounts scored based on position of the feature amount appearing in a node of the tree as it teaches the features appearing at nodes in the same branch of a tree. Also see equation 4 on page 8 that disclose the equation for this. This adds up all the branches that a features amount appear together in a path from root to leaf in the trees of the random forest)
a shuffle step of shuffling the feature amounts appearing in the decision tree for each decision tree, a second search branch number calculation step of calculating the number of search branches for each decision tree for which the shuffle was performed, a second addition step in which the number of search branches calculated in the second search branch number calculation step is added for all the decision trees, ( Yoshida page 9 left column first paragraph cites “First, the SNP positions in the trees in the random forest constructed are randomized with the numbers of the respective SNPs and the topology of the trees kept unchanged. Then, for each SNP combination, the number of simultaneous appearances in the same branches is counted in the same way as for the original random forest.” This is also shown in equation 5 on page 9.)
a mean value calculation step of repeating from the shuffle step to the second addition step a plurality of times and calculating a mean value of the results of the second addition step based on the result of the second addition step, and a subtraction step of subtracting the result of the mean value calculation step from the result of the first addition step. (Yoshida page 9 left column teaches
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This disclose a mean value calculation step of repeating from the shuffle step to the second addition step a plurality of times and calculating a mean value of the results of the second addition step based on the result of the second addition step, and a subtraction step of subtracting the result of the mean value calculation step from the result of the first addition step.)
In regards to claim 3, Yoshida discloses the method for calculating an interaction between feature amounts according to claim 2, wherein a division step of calculating a standard deviation with respect to the result of the second addition step based on the results of the second addition step and the mean value calculation step, and dividing the result of the subtraction step by the standard deviation is executed. (See Yoshida page 9 right column first two paragraph and equation 5.)
In regards to claim 4, Yoshida discloses the method for calculating an interaction between feature amounts according to claim 1, wherein the event can be classified into a predetermined category by a qualitative variable. (Yoshida page 7 “Random Forest” section first paragraph cites “The random forest technique is an ensemble learning technique for conducting classification analyses.” and section “SNPInterForest: extention of random forest for detecting epistatic interactions” first paragraph cites “First, the random forest is constructed on case-control data as a classifier to discriminate between cases and controls with SNPs as categorical variables that have three possible values.”, this teaches using qualitative variables to classify events into a predetermined categories. Also see page 9 right column last paragraph that teaches datasets each containing 1000 SNP markers genotyped for 2000 cases and 200 controls.)
In regards to claim 5, Yoshida discloses the method for calculating an interaction between feature amounts according to claim 1, wherein the event has a numerical value. (See Yoshida page 9 equation 5 wherein the results of would be a numerical value as all the inputs are numerical values.)
In regards to claim 8, Yoshida discloses the method for calculating an interaction between feature amounts according to claim 1, wherein the event is information on a predetermined disease. (Yoshida page 6 section “Analysis using real GWAS data” teaches an event information on predetermined disease which is rheumatoid arthritis and it finds interaction between SNPs for it.)
In regards to claim 9, it is the system embodiment of claim 1 with similar limitations and thus rejected using the same reasoning found in claim 1.
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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yoshida et al. (“SNPInterForest: A New Method For Detecting Epistatic Interactions” – hereinafter Yoshida) as applied to claim 1 above, and further in view of McKinney et al. (“The Integration of Epistasis Network and Functional Interactions in a GWAS Implicates RXR Pathway Genes in the Immune Response to Smallpox Vaccine” – hereinafter Wang).
In regards to claim 6, Yoshida discloses the method for calculating an interaction between feature amounts according to claim 1, but does not explicitly disclose whether the interaction between the feature amounts associated with the event is positively or negatively associated with the event is evaluated using the result of applying the feature amount vector to regression analysis.
McKinney discloses whether the interaction between the feature amounts associated with the event is positively or negatively associated with the event is evaluated using the result of applying the feature amount vector to regression analysis. (McKinney page 1 introduction paragraph 1 teaches gene/SNP interactions, page 2 third paragraph and page 3 step 1 teaches applying regression analysis to data, page 4 step 3 teaches use of vectors and page 5 results teaches positive and negative epistasis which is the positive and negative association to events (vaccine response).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yoshida with that of McKinney in order to allow applying regression analysis to find positive or negative association as both references deal with SNP interaction and the benefit of doing so is it allows the user to determine if gene interactions are good (positive) or bad (negative) in a quick and efficient manner.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yoshida et al. (“SNPInterForest: A New Method For Detecting Epistatic Interactions” – hereinafter Yoshida) as applied to claim 1 above, and further in view of Zeevi et al. (“Personalized Nutrition by Prediction of Glycemic Responses” – hereinafter Zeevi).
In regards to claim 7, Yoshida discloses the method for calculating an interaction between feature amounts according to claim 1, wherein the feature amount has a flora structure of gut microbiota and at least one of ingested nutrients and health information as a feature amount.
Zeevi discloses wherein the feature amount has a flora structure of gut microbiota and at least one of ingested nutrients and health information as a feature amount. (Zeevi page 1080 fig. 1 A teaches a machine learning with feature amounts of flora structure of gut microbiota (gut microbiome), ingested nutrients (Food and standardized meals), and health information (Blood testing and medicine). This information used by the PPGR prediction system to make prediction for a user.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yoshida with that of Zeevi in order to allow for features amounts of gut and nutrients and health information of user when making prediction as all the references deal with classification or prediction machine learning system. The use the information allow for accurate prediction related to a user by using information specific to a user instead of general information.
Response to Arguments
Applicant's arguments filed 04 February 2026 have been fully considered but they are not persuasive. The applicant argues that amendment the abstract overcomes the objection and thus should be withdrawn. The examiner agrees and thus the objection to the abstract is withdrawn. The applicant argues that rejection under 35 USC 112(b) has been overcome in view of the amendment the claims. The examiner agrees and the rejection under 35 USC 112(b) has been withdrawn. The applicant argues that rejection under 35 USC 101 for being an abstract idea is improper and should be withdrawn. Applicant argues the claims do not include a mental process as the limitations cannot be practically performed in the mind, that the claims are similar to that of Ex Parte Desjardins and include an improvement to the technology and thus rejection under 35 USC 101 is improper. Applicant further argues that Yoshida does not disclose the amendments to claim 1 and 9.
The examiner respectfully traverses the applicant argues for the following reasons: Claims 1 and 9 does include mental processes that can be performed practically in the human mind. These limitations are:
a model construction step of constructing a classification and prediction model having a tree structure for classifying and predicting the event based on the feature amount vector; (This amounts to a mental step of observation, judgment and evaluation wherein a user creates a decision tree, can be done with the aid of pen and paper.)
an interaction score calculation step of calculating an interaction score in which a degree of association of an interaction between the feature amounts with the event is scored based on a position of the feature amount appearing in a node constituting the classification and prediction model, and a position of the feature amount in the classification and prediction model in which the position of the feature amount appearing in the node has been shuffled; (This is a mental process of observation, evaluation and judgement. It comprises a user looking at decision tree and counting the number of times to features (nodes) appear in the same branch of a decision tree. The position nodes are tree are shuffled or mixed up, and the co-occurrence of features are counted gain and summed up. Can be done with the aid of pen and paper wherein use shuffles nodes in decision tree, counts co-occurrence, and calculate interaction score from them.)
wherein the calculated interaction score is further based on information associated with interaction among a plurality of combinations of the feature amounts associated with the event (This is a mental process of observation, evaluation and judgement wherein a user calculates how many times features amount together, can be done with the aid of pen and paper. Using equation 1.)
wherein the interaction score calculation step further comprises a shuffle step of shuffling the feature amounts appearing in the decision tree according to the plurality of shuffle rules comprising a first shuffle rule of performing the shuffle for each decision tree and a second shuffle rule of performing the shuffle for the feature amounts that appear in each of the plurality of branch nodes of a target decision tree. (This is a mental process of observation, evaluation and judgement wherein a user creates decision tress and shuffle each tree and the nodes in the decision trees to create all the different possibilities and combinations, and counting them. Can do with aid of pen and paper.)
As shown above the claims do indeed disclose mental processes. Additionally, the claims are not integrated in a practical applicant nor amount to significantly more as the additional limitations are in the claims using a processor and display, which is generic hardware used to execute the abstract idea, see MPEP 2106.05(f). The other additional limitation of “acquiring data including a feature amount vector which is a set of numerical values of feature amounts and is an explanatory variable, and information of an event which is an objective variable,” and “an output step of outputting the calculated interaction score to an output unit.” amounts to data gather or receiving data, and displaying data which is extra-solution activity, see MPEP 2106.05(g). Additionally, these are well-understood, routine and conventional. The additional elements of “acquiring data including a feature amount vector which is a set of numerical values of feature amounts and is an explanatory variable, and information of an event which is an objective variable, and outputting the calculated interaction score to an output unit” amounts to transmitting data and is well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The using of “process and display” amounts to using generic hardware as a tool to apply an abstract idea, see MPEP 2106.05(f). Also, the step of display output is well-understood, routine and conventional, see MPEP 2106.06(d)(II)(iv) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;”. These do not integrate the abstract idea into a practical application nor amount to significantly as they are mere generic hardware in combination with extra-solution activity that is well-understood, routine and conventional. Thus, the examiner maintains the claims have a mental process, are not integrated into a practical application and not amount to significantly more. Additionally, the examiner traverses the applicants argument that the claims are similar to that of Ex Parte Desjardins as the facts of the case and the claims are not similar. The EX Parte Desjardins claims deal with transfer learning wherein the prior trained model weights and parameters are frozen wherein the model is trained with new data, which allows for retraining the knowledge of previously trained model and expand it to new data. The instant application is not related to this, and thus is not similar to Ex Parte Desjardins. Additionally, the applicants argument that the claims are an improvement to the technology is traversed as the claims do have an improvement the technology but rather an improvement the mental process. Lastly the applicant argues that the discloses prior art reference, Yoshida does not disclose the amendments to claim 1 and 9. However the examiner respectfully traverses the applicant’s argument as the Yoshida does disclose calculation interaction score based feature amounts for combination of features and using shuffle rules that shuffle all trees and feature amounts in the nodes of the tree. Yoshida page 8 cites “Therefore, for each SNP combination, the number of simultaneous appearances in the same branches is counted over all trees in the random forest as a measure of its interaction strength. Specifically, it is defined for a SNP combination G as the summation of the number of appearances of G in a branch b, nb(G), for all branches over all trees in the random forest…”, and see equation 4. Also, page 4 right column second paragraph teaches finding interaction score involving more than 2 SNPs and doing for three SNP interaction tuples. Also see tables 1, 2 and 3 for interaction scores for various combinations based on feature amounts. These portions teaches getting interaction score based on feature amounts of a combination of features. Yoshida page 9 cites “First, the SNP positions in the trees in the random forest constructed are randomized with the numbers of the respective SNPs and the topology of the trees kept unchanged. Then, for each SNP combination, the number of simultaneous appearances in the same branches is counted in the same way as for the original random forest. The sequence of these processes is repeated 100 times to develop statistical distributions of the baseline levels for respective SNP combinations.” This teaches that the SNP in trees (multiple tree) are randomized with number of SNPs keep unchanged. Thus, you have a number of trees with SNP positions and amount random. It then teaches doing this 100s of times, thus each tree and SNP feature position (feature amount) in each tree, thus satisfying the two rules of shuffling each tree in the forest and feature amount in the nodes of the tree. For these reasons the examiner maintains the rejection under 35 USC 102 in view of Yoshida.
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.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127