CTNF 18/289,137 CTNF 97153 DETAILED ACTION Notice of AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority Regarding PCT Application No. PCT/JP2023/024893 (filed July 5, 2023), Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Regarding Japanese Patent App. No. JP2022-124955 (filed Aug. 4, 2022), receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statements submitted on 11/1/2023, 3/8/2024, and 10/7/2025 have been considered. Preliminary Amendments The Preliminary Amendments dated 11/1/2023 and 11/30/2023 have been considered. Claims 1-10 are pending. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding Step 1 of the Alice/Mayo framework, Claims 1-5 and 8-10 are directed to an apparatus (a machine), Claim 6 is directed to a method (a process), and Claim 7 is directed to a non-transitory computer-readable storage medium (an article of manufacture), which each fall within one of the four statutory categories of inventions. Regarding Claim 1 Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea). Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “processor”). a prediction process for, for a training example included in a training example set, calculating a prediction result on the basis of predicted values of k (k is a natural number of not less than 2) top-ranked decision rules which are among decision rules included in a decision list and whose conditions are satisfied by the training example; and (under the broadest reasonable interpretation, a human can evaluate a single training example in a training set (for example, (x=5, y=10), and using the top-k rules in a decision list (such as a first rule that y = 2x, and a second rule that y=x+5), predict that the example will have a predicted output of 10, which can be confirmed by the ground truth data) a list determining process for by repeatedly carrying out, until a predetermined condition is satisfied by a value of an objective function including an error term indicative of an error of the prediction result, a process for updating a variable indicative of the decision list, determining the decision list to be output, the variable including a variable indicative of a decision rule which is among the decision rules whose conditions are satisfied and which is given kth priority to be used for prediction. (under the broadest reasonable interpretation, a human can iteratively cycle through training examples and decision rule lists, until a margin of error converges to within a predetermined threshold, where an index keeps track of the number of decision rules actually tested for each list) Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?). The judicial exception is not integrated into a practical application. Regarding the “ information processing apparatus comprising at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. Regarding the “ information processing apparatus comprising at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Accordingly, at Step 2B after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Regarding Claim 2 Step 2A, Prong 1 wherein the variable includes a variable indicating whether each of decision rules whose conditions are satisfied by the training example is used for prediction about the training example by the prediction process. (under the broadest reasonable interpretation, a human can mentally define and keep track of a “variable indicating whether each of decision rules whose conditions are satisfied by the training example is used for prediction about the training example by the prediction process”, such as by adding a column to the example in Fig. 4 to check off if the prediction should be used or not) Regarding Step 2A, Prong 2 , the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception. Regarding Claim 3 Step 2A, Prong 1 wherein the variable includes a variable indicating whether the decision list includes decision rules that are included in a decision rule set, which is a set of decision rules. (under the broadest reasonable interpretation, a human can mentally define and keep track of a “variable indicating whether the decision list includes decision rules that are included in a decision rule set”, such as by adding a column to the example in Fig. 4 with a cross-reference to a different rule set that might have the same rule) Regarding Step 2A, Prong 2 , the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception. Regarding Claim 4 Step 2A, Prong 1 further carries out an acceptance process for accepting setting of a value of the k, and in the prediction process, ...calculates the prediction result with use of the value of the k, the setting of which value has been accepted in the acceptance process. (under the broadest reasonable interpretation, a human can carry out a acceptance process for setting a value of k, such as by using a mental checklist to confirm that the value of k is legitimate, and then calculate the prediction result using the accepted value of k) Step 2A, Prong 2 Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Step 2B Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Regarding Claim 5 Step 2A, Prong 1 a prediction process for calculating a prediction result with use of predicted values of k top-ranked decision rules which are among the decision rules included in the decision list and whose conditions are satisfied by the input data. (under the broadest reasonable interpretation, a human can use a rules list like the one in Fig. 4 to calculate a predicted result using the k-top ranked decision rules) Step 2A, Prong 2 Regarding the “ an input data acquiring process for acquiring input data to be subjected to prediction ” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)). Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Step 2B Regarding the “ an input data acquiring process for acquiring input data to be subjected to prediction ” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Regarding Claim 6 Step 2A, Prong 1 Claim 6 recites a method that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 6. While claim 6 recites additional generic computing components (“processor”) such additional generic computing components do not change the analysis under Step 2A, Prong 1. Step 2A, Prong 2 Claim 6 recites a method that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 6. While claim 6 recites additional generic computing components (“processor”) such additional generic computing components do not change the analysis under Step 2A, Prong 2. Step 2B Claim 6 recites a method that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 6. While claim 6 recites additional generic computing components (“processor”) such additional generic computing components do not change the analysis under Step 2B. Regarding Claim 7 Step 2A, Prong 1 Claim 7 recites a non-transitory computer-readable storage medium that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 7. While claim 7 recites additional generic computing components (“non-transitory computer-readable storage medium” and “computer”) such additional generic computing components do not change the analysis under Step 2A, Prong 1. Step 2A, Prong 2 Claim 7 recites a non-transitory computer-readable storage medium that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 7. While claim 7 recites additional generic computing components (“non-transitory computer-readable storage medium” and “computer”) such additional generic computing components do not change the analysis under Step 2A, Prong 2. Step 2B Claim 7 recites a non-transitory computer-readable storage medium that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 7. While claim 7 recites additional generic computing components (“non-transitory computer-readable storage medium” and “computer”) such additional generic computing components do not change the analysis under Step 2B. Regarding Claim 8 Step 2A, Prong 1 further carries out a ground presenting process for presenting, as ground for the prediction result, some or all of the k top-ranked decision rules used to calculate the prediction result. (under the broadest reasonable interpretation, a human can present (either orally or in writing) a ground for the prediction result, including the actual of the top-k rules used to predict the result) Step 2A, Prong 2 Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Step 2B Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Regarding Claim 9 Step 2A, Prong 1 further carries out a measure presenting process for, for some or all of the k top-ranked decision rules used to calculate the prediction result, presenting, as support information for supporting decision making by a user, a measure for improving the prediction result. (under the broadest reasonable interpretation, a human can present (either orally or in writing) a measure for improving the prediction result, such as a recommendation that a different loss metric would improve the result) Step 2A, Prong 2 Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Step 2B Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Regarding Claim 10 Step 2A, Prong 1 calculates, with use of the input data in which an effect of the measure is reflected, a prediction result obtained by carrying out the measure, and in the measure presenting process, ... presents not only the measure but also the prediction result obtained by carrying out the measure. (under the broadest reasonable interpretation, a human can present (either orally or in writing) a measure for an improved result, and then actually use such measure to present the improved result) Step 2A, Prong 2 Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Step 2B Regarding “ the at least one processor ” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-3 and 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Hara, Satoshi, et al. "Approximate and exact enumeration of rule models." Proceedings of the AAAI conference on artificial intelligence . Vol. 32. No. 1. 2018, pp. 3157-64, hereinafter referenced as HARA, in view of Fournier-Viger, Philippe, et al. "Mining top-k association rules." Canadian Conference on Artificial Intelligence . Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, hereinafter referenced as FOURNIER , and further in view of US 20190236460 A1, hereinafter referenced as JAGOTA . Regarding Claim 1 HARA teaches: An information processing apparatus comprising at least one processor, the at least one processor carrying out: (HARA, p. 3162, section 6: “Algorithm 1 was implemented in Python 3.5, while Algorithm 2 was implemented in C”; Examiner’s Note: One of ordinary skill would understand that implementing algorithms in the Python and C programming languages necessarily requires a processor to execute the programmed instructions) a prediction process for, for a training example included in a training example set, (HARA, p. 3158, section 1: PNG media_image1.png 232 388 media_image1.png Greyscale HARA, p. 3159, section 2: PNG media_image2.png 96 384 media_image2.png Greyscale HARA, p. 3162, section 6: “We used COMPAS dataset distributed at the github repository (Larus-Stone 2017). It comprises 19 categorical attributes of individual people, relating their criminal history, with a total of 6,489 training samples and 721 test samples. The task is binary classification , where the positive category y = 1 indicates that the individual recidivate within two years.” Examiner’s Note: HARA discloses a training set of 6,489 training samples, where each example is in an (x,y) format, and a prediction model is used to predict the binary classification of whether an individual recidivates within 2 years) calculating a prediction result on the basis of predicted values of k ... top-ranked decision rules which are among decision rules included in a decision list and whose conditions are satisfied by the training example; and (HARA, p. 3158, Fig. 1: PNG media_image3.png 144 784 media_image3.png Greyscale HARA, p. 3159, section 3: PNG media_image4.png 128 382 media_image4.png Greyscale HARA, p. 3160, section 4: “Output m as the k -th model if it has not already been output (lines 7–8)” HARA, p. 3160, Algorithm 1: PNG media_image5.png 392 386 media_image5.png Greyscale HARA, p. 3162, section 6: “We used COMPAS dataset distributed at the github repository (Larus-Stone 2017). It comprises 19 categorical attributes of individual people, relating their criminal history, with a total of 6,489 training samples and 721 test samples. The task is binary classification , where the positive category y = 1 indicates that the individual recidivate within two years.” Examiner’s Note: Algorithm 1, solves Problem 2, which searches for the k-th top rule model (corresponding to recited “k ... top-ranked decision rules which are among decision rules included in a decision list”), where the prediction is a binary classification about whether, based on the input parameters, that an individual will recidivate within 2 years) a list determining process for by repeatedly carrying out, until a predetermined condition is satisfied by a value of an objective function ..., a process for updating a variable indicative of the decision list, determining the decision list to be output (HARA, p. 3158, section 1: We then consider finding a model m ∈ F that maximizes the objective function f ( m ) , where F is the set of feasible models”; HARA, p. 3160, Algorithm 1: PNG media_image5.png 392 386 media_image5.png Greyscale HARA, p. 3160, section 4: PNG media_image6.png 142 386 media_image6.png Greyscale Examiner’s Note: As shown in Fig. 1, and explained in Section 4, Algorithm 1 determines a model (corresponding to recited “list” of rules) by iteratively for k-iterations (see line 4) (corresponding to recited “repeatedly carrying out”), which terminates upon objective function f(m) being below a threshold (corresponding to recited “until a predetermined condition is satisfied by a value of an objective function”), to determine if the model (m) is in the set of M (corresponding to recited “a process for updating a variable indicative of the decision list”, where (m) is the recited “variable” that corresponds to the k-th model list)) the variable including a variable indicative of a decision rule which is among the decision rules whose conditions are satisfied and which is given kth priority to be used for prediction ( HARA, p. 3158, section 2: PNG media_image7.png 342 394 media_image7.png Greyscale HARA, p. 3160, Algorithm 1: PNG media_image5.png 392 386 media_image5.png Greyscale HARA, p. 3160, section 4: PNG media_image6.png 142 386 media_image6.png Greyscale Examiner’s Note: As explained above, (m) is the recited “variable” that corresponds to the k-th model list), and as explained in section 2, the model (m) has i-number of rules (corresponding to recited “variable indicative of a decision rule which is among the decision rules whose conditions are satisfied”, and where “m” is given kth priority to be used for prediction because it’s the k-th model used for prediction) However, HARA fails to explicitly teach: (k is a natural number of not less than 2) including an error term indicative of an error of the prediction result However, in a related field of endeavor (mining top-k association rules, see p. 62, section 1), FOURNIER teaches and makes obvious: (k is a natural number of not less than 2) (FOURNIER, p. 68, section 4: “We first ran TopKRules with minconf = 0.8 on each dataset and varied the parameter k from 100 to 2000 to evaluate its influence on the execution time and the memory requirement of the algorithm . Results are shown in Table 2 for k =100, 1000 and 2000.”; Examiner’s Note: FOURNIER discloses that in a top-k rules mining, the value “k” can be set to an integer value above 1, such as 100; the HARA-FOURNIER combination now modifies Algorithm 1 of HARA so that in line 4, k starts at an integer number above 1 as taught by FOURNIER) Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of HARA with FOURNIER as explained above. As disclosed by FOURNIER, one of ordinary skill would have been motivated to do so in order to set thresholds for rule discovery, because “users have limited resources (time and storage space) for analyzing the results and thus are often only interested in discovering a certain amount of rules.” (p. 62, section 1). However, HARA and FOURNIER fail to explicitly teach: including an error term indicative of an error of the prediction result However, in a related field of endeavor (machine learning match rules, see para. 0001), JAGOTA teaches and makes obvious: a list determining process for by repeatedly carrying out, until a predetermined condition is satisfied by a value of an objective function including an error term indicative of an error of the prediction result (JAGOTA, para. 0065: “In some embodiments, a cost function (e.g., an objective function, a function measures errors for predictions, etc.) can be used, along with a distribution of training match scores as derived from the training instances of the training dataset (102 for a feature as described herein, to automatically determine a match score threshold for the feature.”; Examiner’s Note: the HARA-FOURNIER-JAGOTA combination now modifies the objective function f(m) of HARA to include an error function with respect to measuring prediction errors as in JAGOTA). Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of HARA with FOURNIER and JAGOTA as explained above. As disclosed by JAGOTA, one of ordinary skill would have been motivated to do so in order to minimize a error as part of the objective function during training. (para. 0123). Regarding Claim 2 HARA, FOURNIER, and JAGOTA teach the apparatus of claim 1 as explained above. HARA further teaches: wherein the variable includes a variable indicating whether each of decision rules whose conditions are satisfied by the training example is used for prediction about the training example by the prediction process. ( HARA, p. 3158, section 2: PNG media_image7.png 342 394 media_image7.png Greyscale HARA, p. 3160, section 4: PNG media_image6.png 142 386 media_image6.png Greyscale Examiner’s Note: As explained above, (m) is the recited “variable” that corresponds to the k-th model list), and as explained in section 2, the model (m) has i-number of rules (corresponding to recited “variable indicating whether each of decision rules whose conditions are satisfied by the training example is used for prediction about the training example by the prediction process” because i indicates that the first (i-1) inputs do not match, and only the ith input matches) Regarding Claim 3 HARA, FOURNIER, and JAGOTA teach the apparatus of claim 1 as explained above. HARA further teaches and makes obvious: wherein the variable includes a variable indicating whether the decision list includes decision rules that are included in a decision rule set, which is a set of decision rules. (HARA, p. 3158, Fig. 1: PNG media_image3.png 144 784 media_image3.png Greyscale HARA, p. 3159, section 3: PNG media_image8.png 446 404 media_image8.png Greyscale Examiner’s Note: As explained above, (m) is the recited “variable” that corresponds to the k-th model list), and as explained in an alternate embodiment in section 2 with respect to rule sets, the model (m) has i-number of rules in the rule set; as shown in Fig. 1, there are rules that are common to both the rule lists and rule sets (e.g., “priors > 3), and it would be obvious to compare the rules lists and sets for common rules) Regarding Claim 5 HARA, FOURNIER, and JAGOTA teach the apparatus of claim 1 as explained above. HARA further teaches: the at least one processor carrying out: an input data acquiring process for acquiring input data to be subjected to prediction; and (HARA, p. 3162, section 6: “We used COMPAS dataset distributed at the github repository (Larus-Stone 2017). It comprises 19 categorical attributes of individual people, relating their criminal history, with a total of 6,489 training samples and 721 test samples . The task is binary classification, where the positive category y = 1 indicates that the individual recidivate within two years.”; Examiner’s Note: the 721 test samples correspond to the recited “input data” that is acquired that is going to be predicted during the test phase) a prediction process for calculating a prediction result with use of predicted values of k top-ranked decision rules which are among the decision rules included in the decision list and whose conditions are satisfied by the input data. (HARA, p. 3158, section 1: PNG media_image1.png 232 388 media_image1.png Greyscale HARA, p. 3158, section 2: PNG media_image7.png 342 394 media_image7.png Greyscale HARA, p. 3160, Algorithm 1: PNG media_image5.png 392 386 media_image5.png Greyscale HARA, p. 3160, section 4: PNG media_image6.png 142 386 media_image6.png Greyscale Examiner’s Note: Algorithm 1 cycles through the models of rule lists and calculates prediction results for either based on true/false conditions (binary classification of recidivism)) Claim 6 recites a method that corresponds to the apparatus of claim 1 and is therefore rejected for the same reasons explained above with respect to claim 1. Regarding Claim 7 HARA teaches: A non-transitory computer-readable storage medium storing therein a learning program for causing a computer to carry out: (HARA, p. 3162, section 6: “Algorithm 1 was implemented in Python 3.5, while Algorithm 2 was implemented in C”; Examiner’s Note: One of ordinary skill would understand that implementing algorithms in the Python and C programming languages necessarily requires a storage medium to store the programming code that is executed by a computer) The remaining limitations in claim 7 correspond to the apparatus of claim 1, and therefore this claim 7 is rejected for the same reasons explained above with respect to claim 1 . 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over HARA , in view of FOURNIER and JAGOTA , and further in view of US 11080563 B2, hereinafter referenced as GURUPRASAD . Regarding Claim 4 HARA, FOURNIER, and JAGOTA teach the apparatus of claim 1 as explained above. However, HARA, FOURNIER, and JAGOTA fail to explicitly teach: wherein the at least one processor further carries out an acceptance process for accepting setting of a value of the k, and in the prediction process, the at least one processor calculates the prediction result with use of the value of the k, the setting of which value has been accepted in the acceptance process. However, in a related field of endeavor, (domain specific rules, see col. 4, lines 31-38), GURUPRASAD teaches and makes obvious: wherein the at least one processor further carries out an acceptance process for accepting setting of a value of the k, and in the prediction process, the at least one processor calculates the prediction result with use of the value of the k, the setting of which value has been accepted in the acceptance process. (GURUPRASAD, col. 9, lines 23-26: “The data adapter is configured to accept a set of predefined extraction criteria and a set of parameters as provided in the configuration file for acquiring the data.”; Examiner’s Note: GURUPRASAD teaches an acceptance procedure for accepting configuration parameters; the HARA-FOURNIER-JAGOTA-GURUPRASAD combination now builds acceptance criteria into the process for selecting a value of k in the top-k ranking as taught by FOURNIER) Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of HARA with FOURNIER, JAGOTA, and GURUPRASAD as explained above. One of ordinary skill would understand the benefit of double-checking and verifying parameters for change before implementation in order to present lost resources from running predictions according to incorrect model parameters . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over HARA in view of FOURNIER and JAGOTA and further in view of US 20200160998 A1, hereinafter referenced as WARD . Regarding Claim 8 HARA, FOURNIER, and JAGOTA teach the apparatus of claim 5 as explained above. However, HARA, FOURNIER, and JAGOTA fail to explicitly teach: wherein the at least one processor of the prediction apparatus further carries out a ground presenting process for presenting, as ground for the prediction result, some or all of the k top-ranked decision rules used to calculate the prediction result. However, in a related field of endeavor (machine learning predictions, see para. 0024), WARD teaches and makes obvious: wherein the at least one processor of the prediction apparatus further carries out a ground presenting process for presenting, as ground for the prediction result, some or all of the k top-ranked decision rules used to calculate the prediction result . (WARD, para. 0024: “The present techniques also possess the ability to automatically generate explanations/interpretations for predictions generated by nonlinear models used in the present techniques using feature importance algorithms (e.g., SHapley Additive exPlanation (SHAP) value analysis).”; Examiner’s Note: the HARA-FOURNIER-JAGOTA-WARD combination now modifies HARA so that the output predictions are accompanied by a SHAP value as disclosed by WARD that identifies the actual features, or rules, used for the prediction result) Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of HARA with FOURNIER, JAGOTA, and WARD as explained above. As disclosed by WARD, one of ordinary skill would have been motivated to do so because “SHAP offers a strong theoretical framework and produces more consistent results as compared to simpler explanation techniques.” (para. 0082) . 07-21-aia AIA Claim s 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over HARA in view of FOURNIER and JAGOTA and further in view of US 20160140984 A1, hereinafter referenced as CECCHI . Regarding Claim 9 HARA, FOURNIER, and JAGOTA teach the apparatus of claim 5 as explained above. However, HARA, FOURNIER, and JAGOTA fail to explicitly teach: wherein the at least one processor of the prediction apparatus further carries out a measure presenting process for, for some or all of the k top-ranked decision rules used to calculate the prediction result, presenting, as support information for supporting decision making by a user, a measure for improving the prediction result. However, in a related field of endeavor (machine learning tools, see para. 0064), CECCHI teaches and makes obvious: wherein the at least one processor of the prediction apparatus further carries out a measure presenting process for, for some or all of the k top-ranked decision rules used to calculate the prediction result, presenting, as support information for supporting decision making by a user, a measure for improving the prediction result. (CECCHI, para. 0071: “The behavior prediction module 410 may use the histories stored in the repository 418 to improve accuracy of the prediction and to recommend more useful measures .”; Examiner’s Note: the HARA-FOURNIER-JAGOTA-CECCHI combination now modifies HARA to also output recommended measures for improving the prediction accuracy) Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of HARA with FOURNIER, JAGOTA, and CECCHI as explained above. As disclosed by CECCHI, one of ordinary skill would have been motivated to do so in order to “improve accuracy of the prediction[s]”. (para. 0071). One of ordinary skill would further understand the benefit of providing recommendations for improved accuracy in settings such as a medical setting, to help a medical technician obtain better accuracy of results. Regarding Claim 10 HARA, FOURNIER, JAGOTA, and CECCHI teach the apparatus of claim 9 as explained above. However, HARA, FOURNIER, and JAGOTA fail to explicitly teach: wherein in the prediction process, the at least one processor of the prediction apparatus calculates, with use of the input data in which an effect of the measure is reflected, a prediction result obtained by carrying out the measure, and in the measure presenting process, the at least one processor of the prediction apparatus presents not only the measure but also the prediction result obtained by carrying out the measure. However, in a related field of endeavor (machine learning tools, see para. 0064), CECCHI teaches and makes obvious: wherein in the prediction process, the at least one processor of the prediction apparatus calculates, with use of the input data in which an effect of the measure is reflected, a prediction result obtained by carrying out the measure, and in the measure presenting process, the at least one processor of the prediction apparatus presents not only the measure but also the prediction result obtained by carrying out the measure. (CECCHI, para. 0071: “The behavior prediction module 410 may use the histories stored in the repository 418 to improve accuracy of the prediction and to recommend more useful measures .”; CECCHI, para. 0073: “The behavior prediction module 410 may use a display 460 to output an indication of the predicted behaviors and/or recommended measures.”; Examiner’s Note: the HARA-FOURNIER-JAGOTA-CECCHI combination now modifies HARA to also output recommended measures for improving the prediction accuracy, and then actually implements such measures to obtain the updated prediction accuracy from implementing said measures) Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of HARA with FOURNIER, JAGOTA, and CECCHI as explained above. As disclosed by CECCHI, one of ordinary skill would have been motivated to do so in order to “improve accuracy of the prediction[s]”. (para. 0071). One of ordinary skill would further understand the benefit of providing recommendations for improved accuracy in settings such as a medical setting, to help a medical technician obtain better accuracy of results . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Webb, Geoffrey I., et al. "K-optimal rule discovery." Data Mining and Knowledge Discovery 10.1 (2005): 39-79. Discloses a rule discovery system that allows a user to select the value for the number of k rules that optimize a number of constraints. (p. 40, section 1). Angelino, Elaine, et al. "Learning certifiably optimal rule lists for categorical data." Journal of Machine Learning Research 18.234 (2018): 1-78. Discloses the CORELS algorithm for determining the optimum solution for determining a rules list. (p. 2, section 1). Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm 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, Omar Fernandez Rivas can be reached at 571-272-2589. 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. /MICHAEL C. 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