CTFR 18/702,380 CTFR 94175 DETAILED ACTION Acknowledgement This final office action is in response to the amendment filed on 02/24/2026. 12-151 AIA 26-51 12-51 Status of Claims Claims 1, 9, and 11 have been amended. Claims 1-12 are now pending. Response to Arguments Applicant's arguments filed on 02/24/2026 regarding the 35 U.S.C. 101, 102, and 103 rejections of claims 1-12 have been fully considered. The Applicant argues the following: (1) As per the 101 rejection, the Applicant argues, in summary, that (i) the claims do not recite any mathematical concept or mental process; (ii) the independent claims integrate any allegedly abstract idea into a practical application. The features of "displaying user selectable graphical elements..., receiving a feature quantity selection instruction..., automatically selecting a predetermined number of feasible solutions..., and outputting a graphical representation..." reflect technical improvements in the field of image and data processing system. This allows the system and method to reduce unnecessary display processing operations; and (iii) the claimed invention provides a non-conventional and inventive combination of known elements of the image and data processing system, which constitutes "inventive concept". The Examiner respectfully disagrees with all arguments. As per argument (i), the Examiner submits that the claims as amended are directed to the abstract groupings of Mental Processes, Mathematical Concepts, and Certain Methods of Organizing Human Activity because the claims describe a process of facilitating a user request for providing graphical solutions to an optimization problem via use of an objective function and feature quantities. Generating solutions to an optimization problem using an objective function and feature quantities can be performed in the human mind with pen and paper and involve iterative mathematical calculations. Facilitating a user request and providing solutions to problems reflect certain methods of organizing human activities as the solutions provide guidance and instructions for the user to follow. Per MPEP 2106.04(a), a claim recites a judicial exception when the judicial exception is “set forth” or “described” in the claim. As per arguments (ii) and (iii), the Examiner submits that the additional elements recited in the claims and listed in Steps 2A(2) and 2B do not integrate the abstract idea into a practical application nor provide an inventive concept because the additional elements do not improve the functioning of a computer or improve another technology. The additional elements are viewed as mere instructions to apply or implement the abstract idea on a computer. The features argued by the Applicant reflect the use of computer technology to analyze and display data to a user, which is considered abstract. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Sorting through possible solutions and displaying only a predetermined amount versus all solutions is a reduction in the data itself and does not improve or change the computer processing functionality or capabilities. Therefore, there is no direct improvement to a technology as a result of implementing the steps of amended claims 1, 9, and 11. Thus, the 35 U.S.C. 101 rejection is maintained. (2) As per the 102 rejection, the Applicant argues that Yoshimizu fails to disclose each and every elements of amended claims 1, 9, and 11. The Examiner finds the Applicant’s arguments persuasive. Therefore, the 102 rejection has been withdrawn. However, upon further search and consideration, a new ground of 103 rejection for claims 1, 9, and 11 is made. See details below. Notice of Pre-AIA or 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. 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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention, “Visualization Method, Visualization Device, and Recording Medium”, is directed to an abstract idea, specifically Mental Processes, Mathematical Concepts, and Certain Methods of Organizing Human Activity without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer. Step 1 : Claims 1-12 are directed to a statutory category, namely a process (claims 1-8), a machine (claims 9-10), and a manufacture (claims 11-12). Step 2A (1) : Independent claims 1, 9, and 11 are directed to an abstract idea of Mental Processes and Mathematical Concepts, based on the following claim limitations: “acquiring/acquire, for each of a plurality of solutions different from one another of an optimization problem obtained based on an objective function, values of a plurality of feature quantities of the objective function in each of the plurality of solutions; displaying user selectable graphical elements representing the plurality of feature quantities…; receiving a feature quantity selection instruction to select at least one of the plurality of feature quantities among the plurality of feature quantities…; automatically selecting a predetermined number of feasible solutions from the plurality of solutions based on an order of values of the at least one of the plurality of feature quantities of the predetermined number of feasible solutions; and outputting a graphical representation that includes a value of the at least one of the plurality of feature quantities of an optimal solution and values of the at least one of the plurality of feature quantities of the predetermined number of feasible solutions in a comparable manner” . These claims describe a process of facilitating a user request for providing graphical solutions to an optimization problem via use of an objective function and feature quantities (i.e. mathematical concept). Generating solutions to an optimization problem using an objective function and feature quantities can be performed in the human mind with pen and paper and involve mathematical calculations. Facilitating a user request and providing solutions to problems reflect certain methods of organizing human activities as the solutions provide guidance and instructions for the user to follow. Dependent claims 2-7, 10, and 12 further describe the outputs and solutions of the objective function with the limitations of “ outputting, among the plurality of solutions, values of the feature quantities for a predetermined number of the feasible solutions selected based on a predetermined order of the values of the feature quantities and values of the feature quantities for the optimal solution are output in a comparable manner (claim 2); wherein, in the outputting, the values of the feature quantities are output in a predetermined order (claim 3); wherein, in the outputting, the value of the feature quantity acquired for the optimal solution and the value of the feature quantity acquired for the feasible solution are graphically output (claim 4); wherein the feasible solution is a designated feasible solution among the plurality of solutions (claim 5); wherein, in the outputting, a difference between the value of the feature quantity for the optimal solution and the value of the feature quantity for the feasible solution is output (claim 6); and wherein, in the acquiring, for each of the plurality of solutions, a value of a feature quantity for each of a plurality of feature quantities in the objective function is acquired, and in the outputting, the value of the feature quantity acquired for each of the plurality of solutions is output in such a way that the value of the feature quantity for a predetermined feature quantity among the plurality of feature quantities is in a predetermined order (claim 7); wherein the objective function is an objective function generated…(claims 8, 10, and 12) . Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions, Mathematical Concepts which encompasses mathematical relationships, mathematical formulas or equations, and mathematical calculations, and Certain Methods of Organizing Human Activity which encompasses managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping) (MPEP 2106.04(a)(2)). Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. Therefore, claims 1-12 are directed to an abstract idea and are not patent eligible. Step 2A (2) : The claims as a whole do not integrate this abstract idea into a practical application. In particular, claims 1 and 8-12 recite additional elements of “ implemented by a computer (claim 1); displaying user selectable graphical elements….on a display screen of a terminal device of a user, receiving…from the terminal device (claims 1, 9, and 11); inverse reinforcement learning (claims 8, 10, and 12); a visualization device comprising: a memory storing instructions; and at least one processor configured to execute the instructions (claim 9); and a non-transitory recording medium that is readable by a computer and records a program causing the computer to execute processing (claim 11) ”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing devices that are used to perform the abstract process identified in Step 2A(1). Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Therefore, claims 1-12 as a whole do not include individual or a combination of additional elements that integrate the abstract idea into a practical application and thus are not patent eligible. Step 2B : The claims as a whole do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1 and 8-12 recite additional elements of “ implemented by a computer (claim 1); displaying user selectable graphical elements….on a display screen of a terminal device of a user, receiving…from the terminal device (claims 1, 9, and 11); inverse reinforcement learning (claims 8, 10, and 12); a visualization device comprising: a memory storing instructions; and at least one processor configured to execute the instructions (claim 9); and a non-transitory recording medium that is readable by a computer and records a program causing the computer to execute processing (claim 11) ”. These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1-12 as a whole do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the abstract idea and thus are not patent eligible. 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 1-5, 7, 9, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshizumi (US 2021/0109990 A1) in view of Cacioppi et al. (US 2014/0089234 A1) . As per claims 1, 9, and 11 (Currently Amended), Yoshizumi teaches a visualization method, implemented by a computer, comprising; a visualization device comprising: a memory storing instructions; and at least one processor configured to execute the instructions to; and a non-transitory recording medium that is readable by a computer and records a program causing the computer to execute processing for (Yoshizumi e.g. Provided is an apparatus comprising a processor and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to. Also provided as the first aspect are a method and non-transitory computer readable storage medium (Abstract). FIG. 1 shows an exemplary optimization problem to be solved by an apparatus according to an embodiment of the present invention [0011]. FIG. 3 shows an exemplary operational flow of the apparatus according to the present embodiment [0013]. FIG. 10 shows an exemplary hardware configuration of a computer according to the embodiment of the invention [0020]. FIG. 2 shows an exemplary configuration of the apparatus 100 according to the present embodiment [0033].) Yoshizumi teaches acquiring/acquire, for each of a plurality of solutions different from one another of an optimization problem obtained based on an objective function, values of a plurality of feature quantities of the objective function in each of the plurality of solutions ; (Yoshizumi e.g. FIG. 1 shows an exemplary optimization problem to be solved by an apparatus according to an embodiment of the present invention [0011]. The example shown in FIG. 1 may be a problem of packing medical devices used for surgery. Each medical device has a different size and shape [0022]. Such a packaging method can be treated as an optimization problem of determining where to position partitions that indicate divisions for inserting the medical devices into the containers, for an arrangement of medical devices in a predetermined order [0024]. FIG. 2 shows an exemplary configuration of the apparatus 100 according to the present embodiment [0033]. The apparatus 100 includes an acquiring section 110, a storage section 120, a candidate calculating section 130, and a selecting section 150 (Fig. 2 and [0034]). The acquiring section 110 may be operable to acquire candidates for the solution of the optimization problem of optimizing the third objective function based on the first objective function and the second objective function. For example, the acquiring section 110 acquires the solution candidates by reading information stored in the storage section 120. The acquiring section 110 may acquire constraint conditions of the optimization problem. The acquiring section 110 may supply the candidate calculating section 130 with the acquired information [0035]. FIG. 3 shows an exemplary operational flow of the apparatus 100 according to the present embodiment [0043]. The apparatus 100 may be operable to calculate a solution of a minimization problem for the third objective function h*(i, j) defined as the sum of the first objective function g*(i, j) and the second objective function f*(i, j) by performing the processing from S110 to S160 [0043]. First, at S110, the acquiring section 110 may acquire a solution candidate UB from the storage section 120. If this is the starting stage of the operation, the acquiring section 110 may acquire the initial value of the solution candidate UB stored in the storage section 120. The initial value of the solution candidate UB may be a number greater than the number K of medical devices [0044]. Next, at S120, the candidate calculating section 130 may calculate the solution of the optimization problem P'(i, j, UB_g) using dynamic programming [0046]. In this case, the candidate calculating section 130 may use the predetermined constants C1 and C2 as weighting coefficients or conversion coefficients of the values of the first objective function and second objective function [0047]. The problem being solved by the apparatus 100 is not limited to such an optimization problem, and the apparatus 100 may be used to calculate the solution of a third objective function based on other first objective functions and second objective functions [0084]. For example, the apparatus 100 may solve a shortest route problem in a car navigation system. The apparatus 100 can solve an optimization problem of "arriving at a destination in a shorter time or across a shorter distance while avoiding heavy traffic in the route" by setting the traffic density in a route as the first objective function value and the travel time or travel distance as the second objective function [0085].) Yoshizumi teaches outputting/output a graphical representation that includes a value of the at least one of the plurality of feature quantities of an optimal solution and values of the at least one of the plurality of feature quantities of the predetermined number of feasible solutions in a comparable manner . (Yoshizumi e.g. At S140, the selecting section 150 may receive the calculation result of the candidate calculating section 130, and select a solution from the solution candidates of the optimization problem. The selecting section 150 may select the solution candidate having a smaller value from among the plurality of solution candidates of the optimization problem (Fig. 3 and [0048]). At S160, the selecting section 150 may output the most recently selected solution candidate UB as the solution h of the optimization problem of the third objective function h*(i, j). The selecting section 150 may also output values of f_cur, g_cur, and/or the partition position k corresponding to the most recently selected solution candidate UB [0052]. FIG. 7 shows exemplary results obtained by simulating the operation of the apparatus 100 according to the present embodiment. FIG. 7 shows results obtained by actually calculating the solution of the third objective function, based on artificial data. In FIG. 7, the horizontal axis indicates the value of the constraint UB and the vertical axis indicates the value of the objective function. The value g indicates the solution of the first objective function, the value f indicates the solution of the second objective function, and the value h (= f+g) indicates the solution of the third objective function [0077].) Yoshizumi does not explicitly teach, however, Cacioppi teaches the following : Cacioppi teaches displaying/display user selectable graphical elements representing the plurality of feature quantities on a display screen of a terminal device of a user ; (Cacioppi e.g. A method for interactive visualization of multi-objective optimization is described. The method includes displaying a visualization of an approximation to a Pareto frontier for a multi-objective problem in a user interface (Abstract and [0003]). Some of the described embodiments present a system and method for interactive visualization of multi-objective optimization. The system is also configured to receive user selections to interact with or modify various aspects of the visualization [0019]. FIG. 1 depicts a schematic diagram of one embodiment of a multi-objective optimization system 100. The optimization system 100 may include other components, such as input/output devices 106, a disk storage drive 108, a visualization engine 110, an optimization engine 112, and a display device 114 [0021]. A user interface 116 is displayed on the display device 114. The display device 114 may be any display device 114 for a computing device. The user interface 116 may be part of an operating system for the computing device. The user interface 116 may allow the user to interact with the operating system and applications within the operating system. In one embodiment, the visualization engine 110 and the optimization engine 112 are associated with one or more applications [0022]. The visualization engine 110 displays a visualization 122 of the approximation 120. The visualization 122 is displayed on the user interface 116 to allow the user to view and interact with the visualization 122 in real-time as the optimization engine 112 is finding solutions for the multi-objective problem 118 [0024]. The visualization 122 may also include additional visual elements that the user may interact with. For example, the visualization 122 may include selectable options proximate in a side panel 210 the approximation graph 124. The visualization 122 may also display detailed information in the side panel 210, such as a legend describing the visual elements of the visualization 122. In the embodiment of FIG. 2, the approximation graph 124 displays two solutions 212 that have been found for the multi-objective problem 118 [0029].) Cacioppi teaches receiving/receive a feature quantity selection instruction to select at least one of the plurality of feature quantities among the plurality of feature quantities from the terminal device ; (Cacioppi e.g. The user may interact with the visualization 122 via the user interface 116. In one embodiment, the visualization engine 110 is configured to modify an element of the visualization 122 in response to receiving a user selection. The user selection may include selections to perform various operations that modify the look of the visualization 122 or that modify how the optimization engine 112 optimizes the multi-objective problem 118. The user selection may modify the visualization 122 or optimization of the multi-objective problem 118 in real-time while the multi-objective problem 118 is being optimized [0025].) Cacioppi teaches automatically selecting/select a predetermined number of feasible solutions from the plurality of solutions based on an order of values of the at least one of the plurality of feature quantities of the predetermined number of feasible solutions ; (Cacioppi e.g. The visualization engine 110 displays a visualization 122 of the approximation 120. The visualization 122 is displayed on the user interface 116 to allow the user to view and interact with the visualization 122 in real-time as the optimization engine 112 is finding solutions for the multi-objective problem 118 [0024]. The visualization 122 may update after a predetermined number of new solutions 212 have been found. The visualization 122 may update after a predetermined amount of time has passed since the last update. The visualization 122 may update in response to a user selection to update the visualization 122 [0032]. In one embodiment, such as depicted in FIG. 5, the user may select targets 500 in the visualization 122 [0036]. In some embodiments, the optimization system 100 may find more than one solution 212 for a single target 500. The optimization system 100 may be configured to eliminate any solutions 212 that are less than optimal within the target range [0037].) The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Yoshizumi’s apparatus and method for calculating a solution of an optimization problem with Cacioppi’s method and system for interactive visualization of multi-objective optimization in order to enable a user to interact with a visualization to view the results of the optimization in real-time while the optimization of the multi-objective problem is still running to reduce the time needed to present worthwhile solutions to the user. (Cacioppi e.g. [0020]). As per claim 2 (Original), Yoshizumi in view of Cacioppi teach the visualization method according to claim 1, Yoshizumi teaches wherein, in the outputting, among the plurality of solutions, values of the feature quantities for a predetermined number of the feasible solutions selected based on a predetermined order of the values of the feature quantities and values of the feature quantities for the optimal solution are output in a comparable manner . (Yoshizumi e.g. FIG. 1 shows an exemplary optimization problem to be solved by an apparatus 100 according to an embodiment of the present invention [0022]. In the example described here, the medical devices are used in order according to the progression of the surgery, and are packed in a plurality of containers [0022]. FIG. 1 shows an example in which medical devices with various volumes arranged in a predetermined order are packed into containers having various volumes [0022]. Such a packaging method can be treated as an optimization problem of determining where to position partitions that indicate divisions for inserting the medical devices into the containers, for an arrangement of medical devices in a predetermined order [0024]. The apparatus 100 according to the present embodiment described above is an example in which the apparatus 100 solves, for an arrangement of medical devices in a predetermined order, an optimization problem of "performing packing while minimizing the number of containers used and minimizing the empty space g in the containers." [0084]. FIG. 4 shows an exemplary calculation of a solution using dynamic programming made by the optimal solution calculating section 140 according to the present embodiment. FIG. 4 shows the sequence of K medical devices using bar graphs 1 to K [0054]. The optimal solution calculating section 140 may store the calculated solutions as shown in the "DP table" of FIG. 4 [0056].) As per claim 3 (Previously Presented), Yoshizumi in view of Cacioppi teach the visualization method according to claim 1, Yoshizumi teaches wherein, in the outputting, the values of the feature quantities are output in a predetermined order . (Yoshizumi e.g. FIG. 4 shows an exemplary calculation of a solution using dynamic programming made by the optimal solution calculating section 140 according to the present embodiment. FIG. 4 shows the sequence of K medical devices using bar graphs 1 to K [0054]. The optimal solution calculating section 140 may store the calculated solutions as shown in the "DP table" of FIG. 4 [0056].) As per claim 4 (Previously Presented), Yoshizumi in view of Cacioppi teach the visualization method according to claim 1, Yoshizumi teaches wherein, in the outputting, the value of the feature quantity acquired for the optimal solution and the value of the feature quantity acquired for the feasible solution are graphically output . (Yoshizumi e.g. The candidate calculating section 130 includes an optimal solution calculating section 140. The candidate calculating section 130 may be operable to calculate, as other solution candidates for the optimization problem, solutions that optimize the second objective function under constraints corresponding to the first objective function values for the acquired solution candidates [0037]. The candidate calculating section 130 may calculate the empty space for each package, which is an optimization result, and set the largest empty space as the optimal solution g_cur of the first objective function g*(i, j) [0047]. FIG. 4 shows an exemplary calculation of a solution using dynamic programming made by the optimal solution calculating section 140 according to the present embodiment. FIG. 4 shows the sequence of K medical devices using bar graphs 1 to K [0054]. The optimal solution calculating section 140 may store the calculated solutions as shown in the "DP table" of FIG. 4 [0056]. FIG. 7 shows exemplary results obtained by simulating the operation of the apparatus 100 according to the present embodiment. FIG. 7 shows results obtained by actually calculating the solution of the third objective function, based on artificial data. In FIG. 7, the horizontal axis indicates the value of the constraint UB and the vertical axis indicates the value of the objective function. The value g indicates the solution of the first objective function, the value f indicates the solution of the second objective function, and the value h (= f+g) indicates the solution of the third objective function [0077].) As per claim 5 (Previously Presented), Yoshizumi in view of Cacioppi teach the visualization method according to claim 1, Yoshizumi teaches wherein the feasible solution is a designated feasible solution among the plurality of solutions (Yoshizumi e.g. The candidate calculating section 130 may be operable to calculate, as other solution candidates for the optimization problem, solutions that optimize the second objective function under constraints corresponding to the first objective function values for the acquired solution candidates [0037]. The candidate calculating section 130 includes an optimal solution calculating section 140 [0037]. The candidate calculating section 130 may supply the selecting section 150 with the solution candidates [0038]. The apparatus 100 may treat a solution of the second objective function f*(i, j) that is less than UB_g as being feasible, and calculate this solution as a solution candidate [0042].) As per claim 7 (Previously Presented), Yoshizumi in view of Cacioppi teach the visualization method according to claim 1, Yoshizumi teaches wherein, in the acquiring, for each of the plurality of solutions, a value of a feature quantity for each of a plurality of feature quantities in the objective function is acquired , and (Yoshizumi e.g. The apparatus 100 includes an acquiring section 110, a storage section 120, a candidate calculating section 130, and a selecting section 150 [0034]. The acquiring section 110 may be operable to acquire candidates for the solution of the optimization problem of optimizing the third objective function based on the first objective function and the second objective function [0035]. For example, the acquiring section 110 acquires the solution candidates by reading information stored in the storage section 120. The acquiring section 110 may acquire constraint conditions of the optimization problem [0035]. The acquiring section 110 may supply the candidate calculating section 130 with the acquired information [0035]. FIG. 3 shows an exemplary operational flow of the apparatus 100 according to the present embodiment [0043]. First, at S110, the acquiring section 110 may acquire a solution candidate UB from the storage section 120. If this is the starting stage of the operation, the acquiring section 110 may acquire the initial value of the solution candidate UB stored in the storage section 120. The initial value of the solution candidate UB may be a number greater than the number K of medical devices [0044].) Yoshizumi teaches in the outputting, the value of the feature quantity acquired for each of the plurality of solutions is output in such a way that the value of the feature quantity for a predetermined feature quantity among the plurality of feature quantities is in a predetermined order . (Yoshizumi e.g. At S160, the selecting section 150 may output the most recently selected solution candidate UB as the solution h of the optimization problem of the third objective function h*(i, j).The selecting section 150 may also output values of f_cur, g_cur, and/or the partition position k corresponding to the most recently selected solution candidate UB (Fig. 3 and [0052]). The apparatus 100 may continue updating the value of UB_g and calculating the solution candidates, select a more preferable candidate, and output this candidate. Using this operation, the apparatus 100 can calculate an optimal solution of the third objective function based on the first objective function and the second objective function [0042]. FIG. 4 shows an exemplary calculation of a solution using dynamic programming made by the optimal solution calculating section 140 according to the present embodiment. FIG. 4 shows the sequence of K medical devices using bar graphs 1 to K [0054]. The optimal solution calculating section 140 may store the calculated solutions as shown in the "DP table" of FIG. 4 [0056]. The apparatus 100 according to the present embodiment described above is an example in which the apparatus 100 solves, for an arrangement of medical devices in a predetermined order, an optimization problem of "performing packing while minimizing the number of containers used and minimizing the empty space g in the containers." [0084].) . 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yoshizumi (US 2021/0109990 A1) in view of Cacioppi et al. (US 2014/0089234 A1) and in further view of Fox et al. (US 2016/0350072 A1) . As per claim 6 (Previously Presented), Yoshizumi in view of Cacioppi teach the visualization method according to claim 1, Yoshizumi nor Cacioppi explicitly teach, however, Fox teaches wherein, in the outputting, a difference between the value of the feature quantity for the optimal solution and the value of the feature quantity for the feasible solution is output (Fox e.g. The method includes solving the given combinatorial problem to obtain a solution to the given combinatorial problem; and evaluating the solution based on comparison of a value of the quality metric of the solution, and the value of the quality metric of the characteristic solution [0014]. The characteristic solution to the combinatorial problem is an optimal solution to the combinatorial problem, or a best solution to the combinatorial problem obtainable by a given combinatorial solver [0015]. FIG. 1 illustrates a system 100 for solving a given combinatorial problem, according to some example implementations of the present disclosure. As shown, for example, the system may include a function store 102, a calculator 104, a combinatorial solver 106 (e.g., heuristic solver, optimal solver) and an evaluator 108 coupled to one another [0040]. The calculator 104 may apply the function to obtain the value of the quality metric of the optimal solution or the best solution [0044]. The combinatorial solver 106 may be configured to solve the given combinatorial problem to obtain a solution to the given combinatorial problem [0046]. The evaluator 108 may be configured to evaluate the solution based on comparison of a value of the quality metric of the solution, and the value of the quality metric of the characteristic solution [0046]. The evaluator 108 may be configured to evaluate the solution obtained by the combinatorial solver 106 based on the aforementioned comparison in one or more of any of a number of different manners [0047]. FIG. 4 illustrates the system 100 similar to that provided in any of FIGS. 1-3 including an evaluator 408 similar to the evaluator 108 of FIG. 1 [0057]. In a more particular example, the evaluator 408 may be configured to calculate the difference between the quality metric value of a solution obtained by the combinatorial solver 106 and the quality metric value of the optimal solution, and then calculate "percentage error" as that difference divided by the quality metric value of the optimal solution. This calculation may represent a margin of added cost (as compared to the optimal solution) [0058]. The evaluator may then communicate the comparison to guide performance of a task using a solution obtained by the combinatorial solver or another combinatorial solver [0057].) The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Yoshizumi in view of Cacioppi’s apparatus and method for calculating a solution of an optimization problem with Fox’s method of evaluating the value of a solution to the value of an optimal solution of a combinational problem in order to make decisions such as to find a better or the optimal solution or to keep the obtained solution (Fox e.g. [0048]) . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Yoshizumi (US 2021/0109990 A1) in view of Cacioppi et al. (US 2014/0089234 A1) and in further view of Uchibe et al. (US 2017/0213151 A1) . As per claims 8 (Previously Presented), 10 (Original), and 12 (Original), Yoshizumi in view of Cacioppi teach the visualization method according to claim 1, the visualization device according to claim 9, and the recording medium according to claim 11, Yoshizumi nor Cacioppi explicitly teach, however, Uchibe teaches wherein the objective function is an objective function generated by inverse reinforcement learning . (Uchibe e.g. Uchibe teaches a method of inverse reinforcement learning for estimating cost and value functions of behavior s of a subject includes acquiring data representing changes in state variables that define the behaviors of the subject (Abstract and [0036]). The present invention provides inverse reinforcement learning that can infer the objective function from observed state transitions generated by demonstrators. FIG. 7 schematically shows a framework of the method according to an embodiment of the present invention [0218].) The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Yoshizumi in view of Cacioppi’s apparatus and method for calculating a solution of an optimization problem with Uchibe’s method of using inverse reinforcement learning that can estimate/infer an objective function from observed data in order to produce a model-free method/system of solving optimization problems (Uchibe e.g. [0214]). Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ayanna Minor whose telephone number is (571)272-3605. The examiner can normally be reached M-F 9am-5 pm. 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, Jerry O'Connor can be reached at 571-272-6787. 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. /A.M./Examiner, Art Unit 3624 /Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624 Application/Control Number: 18/702,380 Page 2 Art Unit: 3624 Application/Control Number: 18/702,380 Page 3 Art Unit: 3624 Application/Control Number: 18/702,380 Page 4 Art Unit: 3624 Application/Control Number: 18/702,380 Page 5 Art Unit: 3624 Application/Control Number: 18/702,380 Page 6 Art Unit: 3624 Application/Control Number: 18/702,380 Page 7 Art Unit: 3624 Application/Control Number: 18/702,380 Page 8 Art Unit: 3624 Application/Control Number: 18/702,380 Page 9 Art Unit: 3624 Application/Control Number: 18/702,380 Page 10 Art Unit: 3624 Application/Control Number: 18/702,380 Page 11 Art Unit: 3624 Application/Control Number: 18/702,380 Page 12 Art Unit: 3624 Application/Control Number: 18/702,380 Page 13 Art Unit: 3624 Application/Control Number: 18/702,380 Page 14 Art Unit: 3624 Application/Control Number: 18/702,380 Page 15 Art Unit: 3624 Application/Control Number: 18/702,380 Page 16 Art Unit: 3624 Application/Control Number: 18/702,380 Page 17 Art Unit: 3624 Application/Control Number: 18/702,380 Page 18 Art Unit: 3624 Application/Control Number: 18/702,380 Page 19 Art Unit: 3624 Application/Control Number: 18/702,380 Page 20 Art Unit: 3624 Application/Control Number: 18/702,380 Page 21 Art Unit: 3624 Application/Control Number: 18/702,380 Page 22 Art Unit: 3624 Application/Control Number: 18/702,380 Page 23 Art Unit: 3624
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