DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/327163. This communication is the first action on the merits. Claims 1-20 are currently pending and have been rejected as follows.
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 .
Information Disclosure Statement
No Information Disclosure Statement (IDS) has been submitted on behalf of this case. Accordingly, the examiner has not considered an IDS.
Drawings
The drawings filed on 10/18/2025 are acceptable as filed.
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, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for near-optimal scheduling considering processing-time variations and real-time data. Examiner formulates an abstract idea analysis, following the framework described in "The 2019 Revised Patent Subject Matter Eligibility Guidance", as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 12-20) and "system" (claims 1-11).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
…parse a plurality of sets of historical work-in-process (WIP) data generated by a manufacturing execution system (MES) in past multiple production periods according to a statistical method, thereby obtaining a plurality of historical features of operation processing time in the multiple production periods; a publish subscribe mechanism module signally connected to the data parser module and
… receiving the historical features of operation processing time, wherein the publish subscribe mechanism module transmits the historical features of operation processing time via a publish subscribe mechanism;
… receiving the historical features of operation processing time, wherein the processing time prediction module analyzes the historical features of operation processing time according to an orthogonal greedy algorithm (OGA) … thereby obtaining a predicted operation processing time in a next production period,
… receiving the predicted operation processing time, wherein the scheduling optimization module executes an ordinal-optimization algorithm on the predicted operation processing time to generate an optimized schedule report so that the optimized schedule report considers variation of the predicted operation processing time.
Independent claims 11 and 12 recite substantially similar claim language.
Dependent claims 2-10, and 13-20 recite the same or similar abstract idea(s) as independent claims 1, 11, and 12 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-11 and 18-26 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to near-optimal scheduling considering processing-time variations and real-time data and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
"Mental processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)" as the limitations identified above include mere data observations, evaluations, judgements, and/or opinions, e.g. including user observation and evaluation through near-optimal scheduling considering processing-time variations and real-time data, which is capable of being performed mentally and/or using pen and paper.
Step 2A - Prong 2: Claims 1-20 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" A near-optimal scheduling system for considering processing-time variations and real-time data streaming, comprising: a data parser module… a publish subscribe mechanism module signally connected to the data parser module… a processing time prediction module signally connected to the publish subscribe mechanism module … a recurrent neural network (RNN), … a scheduling optimization module signally connected to the publish subscribe mechanism module" (claims 1, 11, and 12) however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of receiving data from a generic "building system" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
"and transmits the predicted operation processing time to the publish subscribe mechanism module" (claims 1, 11, and 12); “wherein the data parser module is activated by a first trigger signal for every first time period, the processing time prediction module is activated by a second trigger signal for every second time period, and the scheduling optimization module is activated by a third trigger signal, a length of the first time period is different from a length of the second time period, and the first trigger signal, the second trigger signal, and the third trigger signal are different from each other,” (claims 2 and 13) however the receiving of data from a user input device is merely the use of a general purpose computer as a tool to apply the abstract idea (MPEP 2106.0S(f)) and/or is merely insignificant extra- solution activity including data gathering over a network (MPEP 2106.0S(g)) and thus fails to integrate the recited abstract idea into a practical application.
Step 2B: Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of near-optimal scheduling considering processing-time variations and real-time data as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to near-optimal scheduling considering processing-time variations and real-time data.
Claims 1-20 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis.
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
Additionally, claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed towards system or apparatus claims but they do not recite structural features but rather recite features such as “A near-optimal scheduling system for considering processing-time variations and real-time data streaming, comprising: a data parser module… a publish subscribe mechanism module signally connected to the data parser module… a processing time prediction module signally connected to the publish subscribe mechanism module … a scheduling optimization module signally connected to the publish subscribe mechanism module” etc. which are primarily software terms per se. Therefore, these claims appear to be directed towards software per se and software is not a statutory category of patentable subject matter. Appropriate correction and/or clarification is required. The Office recommends amending the claims so that more structural features are recited in the bodies of these claims.
Subject Matter Overcoming Prior Art
Claims 1-20 are found to be provisionally allowable. The claims would be found to be allowable if they overcame the 35 USC 101 rejection.
Reasons for Overcoming the Prior Art
It appears that the instant invention is beyond the skill of one of ordinary skill in the art. Accordingly the invention would NOT have been obvious because one of ordinary skill could not have been expected to achieve it, NOR would they have been able to predict the results, and as such, they would have had no capability of expecting success.
The following is an examiner's statement of features not found in the prior art of record:
Claims 1-20 overcome the prior art of record and are found to be provisionally allowable. The following limitations of claim 1,
…
a data parser module configured to parse a plurality of sets of historical work-in-process (WIP) data generated by a manufacturing execution system (MES) in past multiple production periods according to a statistical method, thereby obtaining a plurality of historical features of operation processing time in the multiple production periods;
a publish subscribe mechanism module signally connected to the data parser module and receiving the historical features of operation processing time, wherein the publish subscribe mechanism module transmits the historical features of operation processing time via a publish subscribe mechanism;
a processing time prediction module signally connected to the publish subscribe mechanism module and receiving the historical features of operation processing time, wherein the processing time prediction module analyzes the historical features of operation processing time according to an orthogonal greedy algorithm (OGA) and a recurrent neural network (RNN), thereby obtaining a predicted operation processing time in a next production period, and transmits the predicted operation processing time to the publish subscribe mechanism module; and
a scheduling optimization module signally connected to the publish subscribe mechanism module and receiving the predicted operation processing time, wherein the scheduling optimization module executes an ordinal-optimization algorithm on the predicted operation processing time to generate an optimized schedule report so that the optimized schedule report considers variation of the predicted operation processing time.
in combination with the remainder of the claim limitations are neither taught nor suggested, singularly or in combination, by the prior art of record. Furthermore, neither the prior art, the nature of the problem, nor knowledge of a person having ordinary skill in the art provides for any predictable or reasonable rationale to combine prior art teachings. Independent claims 11 and 12, and dependent claims 2-10 and 13-20 are likewise provisionally allowable.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
The closest prior art of record is described as follows:
Zinner et al. (U.S. Patent Application Publication Number 2017/0032016) - The abstract provides for the following: The present invention relates to the field of information system technology. More particularly, the present invention relates to methods and systems for Real-Time information processing, including Real-Time Data Warehousing, using Real-Time in-formation aggregation (including calculation of the performance indicators and the like) based on continuous homomorphic processing, thus preserving the linearity of the underlying structures. The present invention further relates to a computer program product adapted to perform the method of the invention, to a computer-readable storage medium comprising said computer program product and a data processing system, which enables Real-Time information processing according to the methods of the invention.
Gil et al. (U.S. Patent Application Publication Number 2016/0028605) - The abstract provides for the following: Certain systems and methods herein are directed to features of accessing and/or improving building system efficiency and supporting linear asset networks, including aspects involving IoT (the Internet of things). For example, some embodiments may include ways to measure occupant comfort, ways to conserve energy in heating and cooling linear asset networks, measure the efficiency of linear assets for energy and water delivery and consumption, improve machine efficiency by increasing maintenance effectiveness and many others. The safe fusion of sensor data from human devices, machines, linear assets and space provides a new correlated collection of data for analysis and optimization of building control systems. Innovations herein may pertain, inter alia, to water, gases, liquids, and buildings including commercial, homes, industrial and transportation-oriented spaces such as ships, trains, airplanes, mobile homes.
Xenos et al. (U.S. Patent Application Publication Number 2025/0085698) - The abstract provides for the following: The present invention relates to the generation of scheduling data for machinery. More particularly, the present invention relates to modelling machinery capabilities and states, planned inputs, outputs and constraints in order to generate priorities for a schedule for manufacturing machinery. Aspects and/or embodiments seek to provide a method and system of generating scheduling data that can be used in complex manufacturing settings such as semiconductor wafer fabrication plants.
Xenos et al. (U.S. Patent Application Publication Number 2025/0054078) - The abstract provides for the following: A method and system for generating a schedule of tasks for a group of machines to perform in a factory. The method and system are particularly applicable in semiconductor manufacturing in manufacturing semiconductor wafers. A first method is used to generate a schedule for the group of machines comprising allocating one task or a time-ordered list of several tasks to each machine in a group of machines. The schedule is then optimized using a second method.
Norman (U.S. Patent Application Publication Number 2018/0299872) - The abstract provides for the following: Embodiments presented herein provide techniques for generating and optimizing a plan in a manufacturing environment. The techniques begins by receiving a plurality of demands for a plan, wherein each demand of the plurality of demands has parameters specifying a set of operations, a due date, user specified business logic and priority. The demands are ranked based on the parameters and the user specified business logic. The plurality of demands is broken into sets of demands based on a predefined number and the demand rank. The demands in a first set of demands are optimized to generate a strategy for fulfilling the demands in the first set of demands. One or more constraints are applied to the first set of demands to ensure the first set of demands is fulfilled in preference to the remaining sets of demands.
Yedidsion et al. (U.S. Patent Application Publication Number 2023/0315953) - The abstract provides for the following: A method for training an agent for a substrate manufacturing system is provided. The method includes initializing an agent of a predictive subsystem of a substrate manufacturing system to select an action to perform in a simulation environment associated with the substrate manufacturing system and initiating a simulation of the selected action in the simulation environment. In response to pausing the simulation, the method further includes obtaining, based on an environment state associated with the simulation, output data and updating the agent, based on the output data, to be configured to generate one or more dispatching decisions indicative of a time to initiate processing of one or more substates in the substrate manufacturing system.
Qi Tang et al. “A Model Predictive Control for Lot Sizing and Scheduling Optimization in the Process Industry under Bidirectional Uncertainty of Production Ability and Market Demand.” The abstract provides for the following: In the face of bidirectional uncertainty of market demand and production ability, this paper establishes a multi-objective mathematical model for lot sizing and scheduling integrated optimization of the process industry considering both material network and production manufacturing and finds the optimal decision of the model through model predictive control to minimize total completion time and total production cost. While realizing the model predictive control proposed in this paper, the Elman neural network predicts the relevant parameters required by learning historical orders for the uncertain market demand and equipment production ability. ,en, the calculation formulas of product supply and demand matching and equipment production ability are formed and introduced into the next stage of the model as a constraint condition. In addition to the above constraints for constructing lot sizing and scheduling integrated models in the process industry, this paper also considers both the material network and production manufacturing and uses the IMOPSO algorithm to solve the problem iteratively. So far, a complete model predictive control can be generated. ,rough the model predictive control, the production system can respond in advance, make appropriate changes to offset the foreseeable interference, and obtain the lot sizing and scheduling scheme considering bidirectional uncertainty, thereby improving the system’s overall robustness. Finally, this paper realizes the model's predictive control process through example simulation and analyzes the operation results combined with the scheduling Gantt chart to verify the applicability and effectiveness of the model.
Lin Guoyi et al. (CN Patent Application Publication Number CN 112862325 A) - The abstract provides for the following: The invention provides a scheduling system of a complex manufacturing system based on data in a federal learning mechanism, which comprises a DSACMS scheduling system, wherein the DSACMS scheduling system comprises a data layer, a model layer, a data processing and analyzing module and a scheduling method module which are in interactive connection with each other; the data layer comprises a data interface for calling a data acquisition source and a database connected with the data interface, and the database is also in interactive connection with the model layer, the data processing and analyzing module and the scheduling method module; the model layer comprises one or more of an object-oriented simulation model, a parameter prediction model, a performance index prediction model and an adaptive scheduling model; the scheduling method module comprises a production planning module and a real-time dispatching module, and is used for assisting the generation of the off-line simulation data. The invention discloses a scheduling system of a data complex manufacturing system, which can comprehensively support the scheduling problem of the complex manufacturing system by using data related to scheduling in the manufacturing system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787.
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/M. H. D./
Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624