Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. This Final Office action is in reply to the Applicant amendment filed on 15 May 2026.
2. Claims 1, 13 have been amended. New Claims 14-20 have been added.
3. Claims 1-20 are currently pending and have been examined.
Response to Amendment
In the previous office action, Claims 1-13 were rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (abstract idea). Applicants have not amended now Claims 1-20 to provide statutory support and the rejection is maintained.
In the previous office action, Claims 1-13 needed clarification under 35 U.S.C. §112(f) Claim Interpretation for “means-plus-function”. Applicants have amended and further clarified the claims to resolve the ambiguity. Claims 1-13 were rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which Applicant regards as the invention. Applicants have amended Claims 1-13 and the rejection is withdrawn.
Response to Arguments
Applicant’s arguments filed 15 May 2026 have been fully considered but they are not persuasive. In the remarks regarding the 35 USC § 101 rejection for now Claims 1-20, Applicant argues that: (1) the claims are not directed to an abstract idea, and even if they were, they would amount to significantly more than the abstract idea. Examiner respectfully disagrees. Still commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, and the Examiner details the maintained rejection under 35 U.S.C. 101 in the below rejection with further explanation. Applicant argues that as amended, Applicant basically states: “…traverses this rejection…[directed to non-statutory subject matter]; various minor statement that the claims “…provide concrete improvement to how computers perform optimization analysis”; “The claims as amended recite significantly more than any alleged abstract idea, as the specific technical limitations are not generic computer limitations but rather technical improvements to optimization analysis.” (see Remarks/Arguments pages 10-12). However the Examiner respectfully disagrees. The claims still recite Mathematical concepts – [mathematical relationships, mathematical formulas or equations, mathematical calculations]. The steps recite mathematical operations, data manipulation, constraints combination, numerical bound calculations (relaxation problems of integer linear programming), and comparative determinations to terminate a computational search. Because the claim as a whole is recited at a high level of generality focused on solving an optimization model via mathematical programming techniques rather than a specific technological improvement to the operation of a machine or a transformative industrial process by reciting a judicial exception. As currently recited the claims merely recite user input data into a generic computer (processor and a memory) utilizing general and well-known mathematical concepts and calculations with no additional specific elements defining as to specifically how the broadly recited “generating; calculating; searching determining” steps are accomplished other than, as broadly interpreted, data is input into a pre-programmed software program and broadly recited data results are output. See MPEP § 2106.04(a) III C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. In summary as indicated below with further clarification through Steps 1-2B, the recitation of a computer (a processor and a memory) to perform the claim limitations amount to no more than mere instruction to apply the exception using generic computer components. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. For at least these reasons, the rejection is maintained.
Applicant submits that: (2) Palanisamy et al. (Palanisamy) (US 2021/0232985) does not teach or suggest in amended and broadly recited Claim 1: “terminates(ing) the search when the executable solution under one condition exceeds the upper bound under another condition; calculating upper bounds under search initial conditions that are derived from a user-specified focus state” [see Remarks pages 12-13]. With regard to argument (2), the Examiner respectfully disagrees. First these specific limitations are not specifically recited as such in the claims. These recitations are parts and pieces allegations that are not part of the entire claim’s scope. As amended, the Examiner has below provided additional clarification and additional citations from Palanisamy. Additionally, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections. For at least these reasons, the rejection is maintained. It is noted that any citations to specific, pages, columns, paragraphs, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. The Examiner has a duty and responsibility to the public and to Applicant to interpret the claims as broadly as reasonably possible during prosecution. In re Prater, 415 F.2d 1 393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 1-20 are each focused to a statutory category of invention, namely “method; system;” set.
Step 2A: Prong One: Claims 1-20 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention as directed to an abstract idea without significantly more. For representative independent Claim 1, the claim recites steps for:
“generating a plurality of optimization patterns by combining the constraints to be analyzed;
calculating, for each of the optimization patterns, an upper bound by solving a relaxation problem of an integer linear programming optimization problem based on the evaluation indices when a condition corresponding to a user-specified focus state in the optimal plan is satisfied and the upper bound when the condition is not satisfied, wherein the upper bound is calculated under each search initial condition derived from the focus state;
searching for an executable solution for the optimization under each of the conditions; and
determining, based on the upper bound and the executable solution, whether an optimal solution for the optimization is present under the conditions, wherein the determining terminates the search when the executable solution under one condition exceeds the upper bound under another condition”
As detailed in the MPEP 2106 and commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, the 2019 PEG explains that the abstract idea exception includes the following groupings of subject matter. The 35 U.S.C. 101 Step 2A, Prong One analysis focuses on whether a claim recites a judicial exception by evaluating if it falls into one of three specific groupings: mathematical concepts, mental processes, or certain methods of organizing human activity. Based on the provided steps above in bolded, the claims are based on an abstract idea of generating optimization patterns, calculating bounds via integer linear programming relaxations, searching for solutions, and terminating searches. The analysis for Step 2A Prong One is as follows:
Mathematical concepts – [mathematical relationships, mathematical formulas or equations, mathematical calculations]. The steps recite mathematical operations, data manipulation, constraints combination, numerical bound calculations (relaxation problems of integer linear programming), and comparative determinations to terminate a computational search. Because the claim as a whole is recited at a high level of generality focused on solving an optimization model via mathematical programming techniques rather than a specific technological improvement to the operation of a machine or a transformative industrial process by reciting a judicial exception.
See MPEP § 2106.04(a) III C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception.
Prong Two: Claims 1-20: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Claims 1-13 recite additional elements directed to “a processor and a memory; processor; (various “unit(s); processing device 1002 is a general-purpose computer including a processor such as a CPU and a memory” (e.g., see Applicants’ published Specification ¶’s 53-60, 113-119). Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. See MPEP § 2106.05(f) (h).
Step 2B: As explained in MPEP § 2106.05, Claims 1-13 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of “a processor and a memory; processor; [various] unit(s); processing device 1002 is a general-purpose computer including a processor such as a CPU and a memory”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
The Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ published Specification ¶’s 53-60, 113-119) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “plan analysis system 1” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Based on all these, Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Accordingly, Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) without significantly more.
Claim Rejections - 35 USC § 102
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Palanisamy et al. (Palanisamy) (US 2021/0232985).
With regard to Claims 1, 13, Palanisamy teaches a plan analysis method/system/ to be executed by a plan analysis system that analyzes (Planner insight analytics identifies orders and sizes customers can execute to achieve business and operational efficiency) an optimal plan in which an optimization target is optimized (Generating optimal trimming patterns for trimming raw rolls and/or sheets of a flat sheet stock/customer orders) based on a plurality of constraints and evaluation indices (generating solutions for (i) order quantity fulfillment, (ii) a primary cutting pattern for the primary machine, (iii) a secondary cutting pattern for the secondary machine, and (iv) inventory details, wherein the solutions are generated with consideration of the initial trade constraints; (c) executing a batch and generating suggestions; (d) generating modified solutions for the parameters in step (b) using revised trade constraints derived from the suggestions generated that override the initial trade constraints in (c); and (e) operating a cutting apparatus), the plan analysis system including a processor and a memory (QCS 4 includes any hardware, software, firmware, or combination thereof for controlling the operation of the sheetmaking machine 2 or other machine. QCS 4 can, for example, include a processor and memory storing instructions and data used, generated, and collected by the processor), the plan analysis method/system comprising the processor executing (see at least paragraphs 36, 39; Abstract):
generating a plurality of optimization patterns by combining the constraints ((a) receiving customer order specifications; (b) receiving primary machine specifications; (c) receiving secondary machine specifications; (d) receiving warehouse inventory specifications; (e) receiving trade constraints; (f) generating solutions for the following parameters (i) order quantity fulfillment, (ii) a primary cutting pattern for the primary machine, (iii) a secondary cutting pattern for the secondary machine, and (iv) inventory details, wherein the solutions are generated with consideration of initially received trade constraints; (g) executing a batch and generating suggestions; [0015] (h) generating modified solutions for the parameters in step (f) using revised trade constraints derived from the suggestions generated in (g), which override the initial trade and inventory constraints) to be analyzed (the invention is directed to a trimming system which includes a computer device that automatically calculates the above-described optimal trimming patterns and a cutting apparatus for cutting a reel or sheet of material into a plurality of smaller reels or sheets of material, wherein the cutting apparatus is configured to receive the modified solutions generated by the computer device; Planner insight analytics) (see at least paragraphs 8-15, 20, 51-62; Abstract);
calculating, for each of the optimization patterns, an upper bound by solving a relaxation problem (Most trim algorithms focus on minimization of the number master rolls used to fulfill customer demand with relaxation on the upper bound of the demand constraints instead of minimization of the trim loss. Consequently, suboptimal solutions are derived for the original problem; includes the dualities of both upper and lower bound demand constraints) of an integer linear programming optimization problem (FIG. 6 describes the execution of Trim Planner Algorithm 100 (FIG. 5) in further detail. Order details 130 are fed from an order manager system and the orders are grouped in step 132 based on their dimensions, grade or product type, and preferences. The grouping order is input for formulation of Mixed Integer Linear Programming (MILP) 134 with trim loss as the objective. Machine details 138 and customer specific preferences 136 are inputs for the MILP. A MILP trim problem is formulated with intern call column generation with trim loss objective with demand constraints. After execution the solution is generated in step 140. In the next steps, the solution is checked to determine whether it is optimal or suboptimal) based on the evaluation indices when a condition (complete enumerated pattern generation approach or a column generation; customer demands or orders; policies, parameters and constraints; regarding the order quantity and sizes they can make to achieve higher efficiencies) corresponding to a user-specified focus state (a user selects specific machine parameters from machine details 102. User also selects the orders details 104 from the order management system. The machine details, order details and inventory details 106, from available inventory or unassigned inventory, are provided to the Trim Planning Algorithm 100) in the optimal plan is satisfied and the upper bound when the condition is not satisfied (When a user solves the above problem, the user either uses a complete enumerated pattern generation approach or a column generation approach. The upper bound is not provided for in the demand in the column generation approach. The reason is that the column generation approach uses a Knapsack which uses the duality of above demand constraint in the pattern generation. The duality is used in both objectives. When a user wants to have both upper and lower bounds, the standard or conventional Knapsack approach does not work because it always tries to minimize the number of rolls used instead of the overall trim loss. Also, the current column generation approach uses a Knapsack based on lower bound on the demand. In actual practice, a user preferably wants to minimize the loss which is indirectly attained by the standard or conventional approach but it is not efficient because there is no upper limit of demand), wherein the upper bound is calculated under each search initial condition derived from the focus state (Most trim algorithms focus on minimization of the number master rolls used to fulfill customer demand with relaxation on the upper bound of the demand constraints instead of minimization of the trim loss. Consequently, suboptimal solutions are derived for the original problem; includes the dualities of both upper and lower bound demand constraints) (see at least paragraphs 5, 21, 42-48, 51-62);
searching for an executable solution (generates solutions) for the optimization under each of the conditions (complete enumerated pattern generation approach or a column generation; customer demands or orders; policies, parameters and constraints; regarding the order quantity and sizes they can make to achieve higher efficiencies) (see at least paragraphs 5, 21, 42-44, 51-62);
determining, based on the upper bound and the executable solution, whether an optimal solution for the optimization is present under the conditions (Traditional trim algorithms 72 focus on minimization of the number of master rolls used to fulfill customer demands or orders with relaxation on the upper bound of the demand constraints rather than minimization of trim loss. Applying established policies, parameters and constraints (P-P-C), the profit maximization algorithm 70 generates solutions to operate the trim applications 76 of primary and secondary equipment) (see at least paragraphs 5, 21, 42-44, 51-62),
wherein the determining terminates the search when the executable solution under one condition exceeds (identifying roll size with additional quantity beyond tolerance, (ii) identifying roll size with minimal reduction of quantity beyond tolerance, (iii) identifying roll size which can be removed from the current batch to improve the overall efficiency (iv) identifying different inventory sizes and roll quantities) the upper bound under another condition (a user can minimize the loss with upper and lower bound limits on the demand. This is achieved by modifying the problem with respect to both the Knapsack objectives and approach. In particular, the proposed unique objective functions of the trim optimization algorithm contain a term related to loss which is eventually minimized. The objective of the Knapsack in the column generation approach contains the sum of the duality of upper and lower demand constraints subtracted by loss. By incorporating two additional terms for the dualities relating to the upper bound and loss, the term would eventually move to the solution to minimization of overall loss) (see at least paragraphs 5, 21, 42-44, 50-62).
With regard to Claim 2, Palanisamy teaches: determining, based on the focus state, an initial condition for calculating the upper bound and searching for the executable solution (see at least paragraphs 5, 21, 42-44, 50-62).
With regard to Claim 3, Palanisamy teaches: holding the upper bound and the executable solution in each of the conditions, and outputting information indicating that a certain condition among the conditions is satisfied in the optimal solution when the executable solution in the certain condition exceeds the upper bound in another condition (see at least paragraphs 5, 21, 42-44, 50-62).
With regard to Claim 4, Palanisamy teaches: searching for the executable solution under the condition to which a new condition (new insights) is added in a search process of the executable solution (see at least paragraphs 5, 21, 42-44, 50-62);
updating the upper bound based on the condition to which the new condition is added (see at least paragraphs 5, 21, 42-44, 50-62).
With regard to Claim 5, Palanisamy teaches: when a completion condition of the search for the executable solution is satisfied, completing the search for the executable solution, executing predetermined exception processing, and outputting an execution result of the exception processing instead of outputting the information (see at least paragraphs 42-44, 67).
With regard to Claim 6, Palanisamy teaches: generating the optimization patterns in an order including more constraints among the plurality of constraints (see at least paragraphs 5, 21, 42-44, 50-62).
With regard to Claim 7, Palanisamy teaches: determining whether the executable solution already acquired in another optimization pattern satisfies the condition in the target optimization pattern, and when the condition is satisfied, holding the executable solution as a provisional solution in the target optimization pattern (see at least paragraphs 68-71).
With regard to Claim 8, Palanisamy teaches: executing a breadth-first search when searching for the executable solution (see at least paragraphs 5, 21, 42-44, 50-62, 70).
With regard to Claim 9, Palanisamy teaches: calculating an inverse constraint for an excluded constraint that is the constraint excluded in the optimization pattern; searching for the executable solution for the optimization pattern by using the inverse constraint (see at least paragraphs 5, 21, 42-44, 50-62);
comparing the upper bound or the executable solution acquired during the search for the executable solution with the upper bound or the executable solution under the same condition among the upper bound or the executable solution obtained for the optimization pattern including the excluded constraint, and determining the upper bound or the executable solution with a larger value as the upper bound or the executable solution under the condition (see at least paragraphs 5, 21, 42-44, 50-62, 70).
With regard to Claim 10, Palanisamy teaches: converting the optimization pattern and a determination result as to whether an optimal solution for the optimization is present under the condition for the optimization pattern into a feature and storing the feature in contribution degree calculation data (see at least paragraphs 50-52);
calculating, based on the feature, a contribution degree of each of the constraints for the optimal plan (see at least paragraphs 50-52).
With regard to Claim 11, Palanisamy teaches: generating, by the processor, a description for the focus state based on the contribution degree and outputting a result through an output device (see at least paragraphs 50-52).
With regard to Claim 12, Palanisamy teaches: receiving a user input of information related to generation of the description via an input device (see at least paragraphs 48-52).
With regard to Claim 14, Palanisamy teaches:
storing, in the memory, the upper bound and the executable solution in each of the conditions (see at least paragraphs 5, 21, 36, 42-44, 50-62, 70);
executing a breadth-first search when searching for the executable solution, wherein the breadth-first search is terminated when the executable solution in a certain condition exceeds the upper bound in another condition (see at least paragraphs 5, 21, 42-44, 50-62, 70).
With regard to Claim 15, Palanisamy teaches:
determining whether the executable solution already acquired in another optimization pattern satisfies the condition in a target optimization pattern (see at least paragraphs 5, 21, 42-44, 50-62, 70);
when the condition is satisfied, storing the executable solution in the memory as a provisional solution in the target optimization pattern (see at least paragraphs 5, 21, 42-44, 50-62, 70).
With regard to Claim 16, Palanisamy teaches: calculating an inverse constraint for an excluded constraint that is the constraint excluded in the optimization pattern (see at least paragraphs 5, 21, 42-44, 50-62, 70);
searching for the executable solution for the optimization pattern by using the inverse constraint (see at least paragraphs 5, 21, 42-44, 50-62, 70);
comparing the upper bound or the executable solution acquired during the search with the upper bound or the executable solution under the same condition obtained for the optimization pattern including the excluded constraint, and storing in the memory the upper bound or the executable solution with a larger value as the upper bound or the executable solution under the condition (see at least paragraphs 5, 21, 42-44, 50-62, 70).
With regard to Claim 17, Palanisamy teaches:
converting the optimization pattern and a determination result as to whether an optimal solution for the optimization is present under the condition for the optimization pattern into a feature and storing the feature in contribution degree calculation data in the memory (see at least paragraphs 5, 21, 42-44, 50-62, 70);
calculating, based on the feature, a contribution degree of each of the constraints for the optimal plan (see at least paragraphs 5, 21, 42-44, 50-62, 70);
generating a description for a focus state based on the contribution degree and outputting the description through an output device (see at least paragraphs 5, 21, 42-44, 50-62, 70).
With regard to Claim 18, Palanisamy teaches:
generating the optimization patterns in an order including more constraints among the plurality of constraints, wherein each optimization pattern represents a different combination of the constraints to be analyzed (see at least paragraphs 5, 21, 42-44, 50-62, 70).
With regard to Claim 19, Palanisamy teaches:
holding the upper bound and the executable solution in each of the conditions; and outputting information indicating that a certain condition among the conditions is satisfied in the optimal solution when the executable solution in the certain condition exceeds the upper bound in another condition (see at least paragraphs 5, 21, 42-44, 50-62, 70).
With regard to Claim 20, Palanisamy teaches:
determining, based on the user-specified focus state, an initial condition for calculating the upper bound and searching for the executable solution, wherein the initial condition defines a starting point for the search for the executable solution under each of the conditions (see at least paragraphs 5, 21, 42-44, 50-62, 70).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Quirynen et al. (US 2022/0137961)
Iyer et al. (US 2015/0039664)
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS L MANSFIELD whose telephone number is (571)270-1904. The examiner can normally be reached M-Thurs, alt. Fri. (9-6).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624
Prosecution Insights