DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Summary
This Final Office Action in response to the communication received on May 26, 2026.
Claim 1 has been amended.
Claims 14-20 have been cancelled.
Claims 1-13 and 21 are pending.
Effective filing date is June 2, 2021.
Response to Amendment
Amendments to Claim 1 are acknowledged.
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 1-13 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 – Statutory Categories
As indicated in the preamble of the claim, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter. (Claims 1-13 and 21 are processes.). Accordingly, step 1 is satisfied.
Step 2A – Prong 1: was there a Judicial Exception Recited
Claim 1 recites the following abstract concepts that are found to include “abstract idea.” Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
receiving an input at a platform from a notification service configured to transmit data over a data network using an application programming interface (“API”) in data communication with one or more CNC-enabled machines (See MPEP 2106.04(a)(2)(III) mental processes, Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1345, 113 USPQ2d 1354, 1356 (Fed. Cir. 2014) (system and method claims of inputting information from a hard copy document into a computer program).), the input being transmitted from the one or more CNC-enabled machines in one or more data communication formats and being translated into another data communication format being a common format contemporaneously used by the platform (See MPEP 2106.04(a)(2)(III) mental processes, Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of “translating a functional description of a logic circuit into a hardware component description of the logic circuit” are directed to an abstract idea, because the claims “read on an individual performing the claimed steps mentally or with pencil and paper”)), the input also including at least a computer-originated requirement associated with an item and the computer-originated requirement associated with the item being identified by one of the one or more CNC-enabled machines, and the input further including an attribute associated with the item, the attribute being identified and determined by at least one of the one or more CNC-enabled machines, the attribute being used to manufacture the item using at least the one of the one or more CNC-enabled machines (See MPEP 2106.04(a)(2)(III) mental processes, a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011));
querying one or more databases in response to the input to request other data including encoded part machining requirement data identifying machining capabilities associated with the one or more CNC-enabled machines configured to manufacture at least a portion of the item, at least one of the one or more databases being in data communication with a platform (See MPEP 2106.04(a)(2)(III) mental processes, a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011));
implementing an algorithmic module configured to match the item of the computer-originated requirement for manufacturing the item to the one or more CNC-enabled machines, the algorithmic module including a machine learning algorithm to perform one or more of matching and ranking data operations, the algorithmic module including training data that is adjusted automatically in association with the machine learning algorithm to improve accuracy of subsequent data operations (See MPEP 2106.04(a)(2)(I) mathematical concepts, a mathematical relationship between enhanced directional radio activity and antenna conductor arrangement (i.e., the length of the conductors with respect to the operating wave length and the angle between the conductors), Mackay Radio & Tel. Co. v. Radio Corp. of America, 306 U.S. 86, 91, 40 USPQ 199, 201 (1939) (while the litigated claims 15 and 16 of U.S. Patent No. 1,974,387 expressed this mathematical relationship using a formula that described the angle between the conductors, other claims in the patent (e.g., claim 1) expressed the mathematical relationship in words) See MPEP 2106.04(a)(2)(III) mental processes, a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011));
generating match data by evaluating data representing characteristics of the item, the other data, and the requirement to identify one or more suppliers using the algorithmic module of the platform, each of the one or more suppliers being identified using a file including further data associated with an attribute value of the each of the one or more suppliers (See MPEP 2106.04(a)(2)(I) mathematical concepts, a mathematical relationship between enhanced directional radio activity and antenna conductor arrangement (i.e., the length of the conductors with respect to the operating wave length and the angle between the conductors), Mackay Radio & Tel. Co. v. Radio Corp. of America, 306 U.S. 86, 91, 40 USPQ 199, 201 (1939) (while the litigated claims 15 and 16 of U.S. Patent No. 1,974,387 expressed this mathematical relationship using a formula that described the angle between the conductors, other claims in the patent (e.g., claim 1) expressed the mathematical relationship in words) See MPEP 2106.04(a)(2)(III) mental processes, a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011));
ranking the match data using the machine learning module, wherein machine learning module adjusts automatically the ranking of match data based on the training data (See MPEP 2106.04(a)(2)(I) mathematical concepts, using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979));
selecting automatically a CNC-enabled machine as a selected CNC-enabled machine based on at least one the attribute value of each of the one or more suppliers as derived from the machine learning module (See MPEP 2106.04(a)(2)(II) methods of organizing human activity, filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis));
presenting the match data after the ranking, the match data being sent by the platform over the application programming interface (“API”) as a network interface to a remote service including the notification service and a display (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
causing manufacture of the item based on the selected CNC-enabled machine as a function of the attribute data derived from the machine learning module (See MPEP 2106.04(a)(2)(II)(C) managing personal behavior or relationships or interactions between people, and thus methods of organizing human activity, a series of instructions of how to hedge risk, Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1004 (2010)); and
causing transportation via a specific type of transportation for the item based on data associated with the attribute and the automatically selected CNC-enabled machine (See MPEP 2106.04(a)(2)(II)(C) managing personal behavior or relationships or interactions between people, and thus methods of organizing human activity, a series of instructions of how to hedge risk, Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1004 (2010)).
Claim 1 is directed to a series of steps for performing actions to match and rank data of machines for manufacturing an item, which is an evaluation of data using mathematical calculations to manage commercial interactions such as manufacturing and thus grouped as mathematical concepts, mental processes, and certain method of organizing human activity. The mere nominal recitation of data network, application programming interface, platform, CNC-enabled machines, algorithmic module, display, and transportation does not take the claim out of the method of organizing human interactions. Thus, claim 1 recites an abstract idea.
Step 2A – Prong 2: Can the Judicial Exception Recited be integrated into a practical application
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The identified abstract idea of exemplary Claim 1 is not integrated into a practical application. The additional elements are: data network, application programming interface, platform, algorithmic module, display, and transportation are using the computer components as a tool to perform the abstract idea - see MPEP 2106.05(f). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Claim 1 is directed to an abstract idea.
Step 2B – Significantly More Analysis
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, receiving an input including an item requirement that was transmitted using an application programming interface; querying databases in data communication with a platform to request additional data in data communication with one or more CNC-enabled machines, the input being transmitted from the one or more CNC-enabled machines in one or more data communication formats and being translated into another data communication format used by the platform, identifying an item and an attribute of an item by one or more CNC-enabled machines; generating match data by evaluating data using an algorithmic module of the platform; ranking the match data; selecting a CNC-enabled machine as a selected CNC -enabled machine based on the attribute value of each of the suppliers; presenting the match data by sending it by the platform over the application programming interface and a display; and causing transportation for the item based on data associated with the attribute and the automatically selected CNC-enabled machine, do not add significantly more to the exception because it amounts to using the computer components as a tool to perform the abstract idea - see MPEP 2106.05(f). Claim 1 is ineligible.
Claim 2 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III). For the additional limitation of a demand signal, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 3 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III). For the additional limitation of a demand signal, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 4 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 5 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 6 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(I). For the additional limitation of a search module, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 7 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(I). For the additional limitation of an encoded part data repository, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 8 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(I). For the additional limitation of an encoded part data repository, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 9 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(I). For the additional limitation of a logic module, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 10 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(I). For the additional limitation of networked clients, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 11 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(I). For the additional limitation of networked supplier machines, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 12 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 13 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 21 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Prior Art
Claims 1-13 and 21 in the instant application is allowable over the prior art because the prior arts of record fail to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Exemplary claim 1 recites the following:
A method, comprising:
receiving an input at a platform from a notification service configured to transmit data over a data network using an application programming interface (“API”) in data communication with one or more CNC-enabled machines, the input being transmitted from the one or more CNC-enabled machines in one or more data communication formats and being translated into another data communication format being a common format contemporaneously used by the platform, the input also including at least a computer-originated requirement associated with an item and the computer-originated requirement associated with the item being identified by one of the one or more CNC-enabled machines, and the input further including an attribute associated with the item, the attribute being identified and determined by at least one of the one or more CNC-enabled machines, the attribute being used to manufacture the item using at least the one of the one or more CNC-enabled machines;
querying one or more databases in response to the input to request other data including encoded part machining requirement data identifying machining capabilities associated with the one or more CNC-enabled machines configured to manufacture at least a portion of the item, at least one of the one or more databases being in data communication with a platform;
implementing an algorithmic module configured to match the item of the computer-originated requirement for manufacturing the item to the one or more CNC-enabled machines, the algorithmic module including a machine learning algorithm to perform one or more of matching and ranking data operations, the algorithmic module including training data that is adjusted automatically in association with the machine learning algorithm to improve accuracy of subsequent data operations;
generating match data by evaluating data representing characteristics of the item, the other data, and the requirement to identify one or more suppliers using the algorithmic module of the platform, each of the one or more suppliers being identified using a file including further data associated with an attribute value of the each of the one or more suppliers;
ranking the match data using the machine learning module, wherein machine learning module adjusts automatically the ranking of match data based on the training data;
selecting automatically a CNC-enabled machine as a selected CNC-enabled machine based on at least one the attribute value of each of the one or more suppliers as derived from the machine learning module;
presenting the match data after the ranking, the match data being sent by the platform over the application programming interface (“API”) as a network interface to a remote service including the notification service and a display;
causing manufacture of the item based on the selected CNC-enabled machine as a function of the attribute data derived from the machine learning module; and
causing transportation via a specific type of transportation for the item based on data associated with the attribute and the automatically selected CNC-enabled machine.
(Emphasis added to highlight features that distinguish over the prior art).
As further explained below, the prior art of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the Applicant’s claimed invention.
U.S. Pat Pub 2022/0107984 “Reed” discloses an electronic procurement system (“system”) implements features such as real-time adaptive extraction of online data. The system is configured to manage a plurality of page types for webpages and for each page type a plurality of field types for fields in webpages. The system is configured to further generate a computer application that can be integrated into a web browser. For each page type, the computer application is programmed to initially generate a signature for each field type based on minimal user interaction with webpages of the page type. The system is configured to also generate an agent using the signature. The agent is programmed to automatically extract data corresponding to the field types from additional webpages of the page type using the signatures. Reed fails to disclose selecting automatically a CNC-enabled machine as a selected CNC-enabled machine based on at least one attribute value of each of the one or more suppliers, and causing transportation via a specific type of transportation for the item based on data associated with the attribute and the automatically selected CNC-enabled machine.
U.S. Pat Pub 2017/0235294 ”Shapiro” teaches an execution plan segment of an execution plan can be received at a control unit of a computer numerically controlled machine from a general purpose computer. The execution plan segment can define operations for causing movement of a movable head of the computer numerically controlled machine to deliver electromagnetic energy to effect a change in a material within an interior space of the computer numerically controlled machine. Shapiro fails to teach selecting automatically a CNC-enabled machine as a selected CNC-enabled machine based on at least one attribute value of each of the one or more suppliers, and causing transportation via a specific type of transportation for the item based on data associated with the attribute and the automatically selected CNC-enabled machine.
US Pat Pub 2019/0018391 “Rogers” teaches a method relating to machining parts include a CNC system, and a computer including a processor and a computer-readable medium, wherein the computer-readable medium encodes instructions including receiving, at the computer program, output data from a CNC machine that receives instructions of a Numerical Control (NC) program at a computer of the CNC machine, the instructions causing the CNC machine to i) manufacture a part, and ii) output the output data, parsing, by the computer program, the output data before completion of the manufacturing of the part by the CNC machine in accordance with the instructions of the NC program, selecting, by the computer program and based on one or more predetermined parameters, a set of data from the parsed output data; and providing, by the computer program to a remote system, the set of data for processing to facilitate machining using the CNC machine. Rogers fails to teach selecting automatically a CNC-enabled machine as a selected CNC-enabled machine based on at least one attribute value of each of the one or more suppliers, and causing transportation via a specific type of transportation for the item based on data associated with the attribute and the automatically selected CNC-enabled machine.
Response to Arguments
35 USC 101
Applicant's arguments filed May 26, 2026 have been fully considered but they are not persuasive.
Applicant argues that the Precedential ruling by the Patent Trial and Appeal Board (“PTAB”) dated 11/4/2025 (Ex parte Guillaume Desjardins, et al. regarding application 16/319040) is of similar or parallel descriptive nature. Amended Claim 1 of the present application recites an “algorithmic module including a machine learning algorithm to perform one or more of matching and ranking data operation, the algorithmic module including ‘training data’ that is adjusted automatically in association with the machine learning algorithm ‘to improve accuracy of subsequent data operations’…selecting automatically a CNC-enabled machine as a selected CNC-enabled machine based on at least on the attribute value of each of the one or more suppliers as derived from the machine learning module…causing ‘manufacture of the item based on the selected CNC-enabled machine’ as a function of the ‘attribute data derived from the machine learning module’…”, and that the amended claims are of similar or parallel descriptive nature as those ruled to be patent eligible in Ex parte Desjardins.
The decision in Ex parte Desjardins states that they were “persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.” It is the Examiner’s understanding of the decision in Ex parte Desjardins that claims that are directed to an improvement to the machine learning model itself are eligible, but claims that are directed to a non-technical improvement, such as the general output of the machine learning, are ineligible. Adjusting the algorithmic module based on received training data to improve accuracy of subsequent data operations is considered a normal operation of a generic machine learning algorithm/module. The subsequent portions of the claim fail to provide additional detail or improvement to the machine learning such as "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." As recited in Ex parte Desjardins.
The claimed limitation of “causing manufacture of the item based on the selected CNC-enabled machine as a function of the attribute data derived from the machine learning module,” fails to include technical details about how the CNC enabled machines interacted and what technical features are used for the “causing manufacture of the item”. As currently claimed the “causing” could merely be providing instructions to an operator directing them to operate a specific CNC enabled machine for manufacture of the item, and is insufficient to be considered a practical application of the abstract idea.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
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