DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 16, 2026 has been entered. Claims 1, 7, 9, 15, 17, and 23 have been amended. Claims 2, 10, and 18 are cancelled. Claims 1, 3-9, 11-17, and 19-24 are presented for examination.
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 .
Response to Arguments
Applicant's arguments filed July 16, 2026 have been fully considered but they are not persuasive.
Preliminarily, it is noted that the previously-pending rejections of claims 2, 7, 10, 15, 18, and 23 under 35 U.S.C. § 112(a) have been obviated by Applicant’s claim amendments.
Regarding the rejection under 35 U.S.C. § 101, Applicant argues:
The amended claims do not merely recite the desired outcome of consistency; they recite a specific mechanism for achieving it. The scheduling available capacity is computed at the finest level from the aggregate shift duration, number of shifts, and aggregate downtime, "aggregated based on a shift calendar pattern of the resource scheduling data model," and the planning available capacity is then "generated in bucketized time units to synchronize the resource planning data model with the resource scheduling data model." This is a particular, ordered transformation that harmonizes the two data models - importing capacity at the finest level and aggregating it into the bucketized units used by the planning model - thereby eliminating the "noise," "delay," and "data mismatch" the specification attributes to maintaining the two models separately. (Page 11 of Applicant’s response)
It is noted that the general idea of the concept of a solution alone is not sufficient to present an improvement that would overcome the rejection. MPEP § 2106.05(a) states, “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.” Generation and synchronization of models speak to details of the abstract ideas, as identified in the rejection. Viewing the models as solutions, the claims still merely presents the concept of a solution alone. The solution is never actually executed in the real world to effect a technical solution in data structures, for example. Additionally, further narrowing details of the abstract ideas does not make the claims any less abstract. Applicant’s amendments have simply narrowed the scope of the identified abstract ideas.
Regarding the Enfish decision (as argued by Applicant on pages 11-12 of the response), MPEP § 2106.05(d)(I) states, “The eligible claims in Enfish recited a self-referential database having two key features: all entity types can be stored in a single table; and the table rows can contain information defining the table columns. Enfish, 822 F.3d at 1332, 118 USPQ2d at 1687. Although these features were taught by a single prior art reference (thus anticipating the claims), Microsoft Corp., 662 Fed. App'x at 986, the features were not conventional and thus were considered to reflect an improvement to existing technology. In particular, they enabled the claimed table to achieve benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish, 822 F.3d at 1337, 118 USPQ2d at 1690.” Contrary to the eligible claims in Enfish, Applicant’s claims do not present an unconventional use of the additional elements. Again, Applicant’s amendments have simply narrowed the scope of the identified abstract ideas.
Also, unlike the claims in the McRO decision, Applicant’s claims present an algorithm that (aside from the general use of processing elements and machine learning at a high level) a human user can perform.
On page 13 of the response, Applicant states, “That is a data-model-architecture improvement of the kind credited in Enfish, not a mere field-of-use application of an existing tool. Applicant respectfully submits that the eligibility of the amended claims does not depend on any improvement to machine learning, and that the recited data-model synchronization is a technical improvement independent of the model used to compute the underlying metrics.” Again, generation and synchronization of models speak to details of the abstract ideas, as identified in the rejection. Viewing the models as solutions, the claims still merely present the concept of a solution alone (which is insufficient to present a technical improvement). The solution is never actually executed in the real world to effect a technical solution in data structures, for example. Additionally, further narrowing details of the abstract ideas does not make the claims any less abstract. Applicant’s amendments have simply narrowed the scope of the identified abstract ideas.
Applicant argues that “[s]ynchronizing a bucketized planning data model with a finer-grained scheduling data model is not an ‘observation, evaluation, judgment, [or] opinion’ of the kind the mental-process grouping encompasses. To the extent the rejection relies on the mental-process grouping, it is respectfully traversed.” (Pages 13-14 of the response) The Examiner respectfully disagrees. A human user can analyze and process information related to a bucketized planning data model with a finer-grained scheduling data model. As to the use of machine learning, the claims present generic machine learning processing operations. The machine learning is generally applied to perform the abstract ideas and used as a mere tool to facilitate operations that could otherwise be performed by a human user.
On page 14 of the response, Applicant submits the following:
Even if the amended claims were considered to recite an abstract idea, the ordered combination amounts to significantly more than any exception. Under Step 2B, the analysis asks whether the additional elements, considered as an ordered combination, provide an inventive concept. BASCOM Global Internet Services V. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016), holds that "an inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces." Id. at 1350. The amended claims recite a particular arrangement: deriving scheduling available capacity at the finest level from shift and downtime data aggregated on the scheduling model's shift calendar pattern; transforming that capacity, via an efficiency factor comprising aggregate changeover and aggregate maintenance time, into a planning available capacity generated in bucketized time units that synchronizes the planning data model with the scheduling data model; deriving a throughput-based planning available capacity from a measured inventory of units produced and a time availability; and maintaining an iterative closed-loop update. As in BASCOM, this specific, ordered arrangement is the inventive concept.
Applicant references details that are directed to the abstract ideas. Even reading in performance of a closed-loop update for machine learning purposes, using feedback to train a machine learning model is a generic machine learning operation. This is how machine learning works. Applicant has not specifically pointed to particular additional element combinations that Applicant asserts to not be well-understood, routine, and conventional.
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, 3-9, 11-17, and 19-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1, 3-9, 11-17, and 19-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “unified resource capacity data management” as well as managing and scheduling available capacity without significantly more (abstract).
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 17, 19-24), Apparatus (claims 1, 3-8), Article of Manufacture (claims 9, 11-16)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite the following operations:
[Claims 1, 9, 17] receive a set of training data from the data store, the set of training data including data for a plurality of resources and a customer demand;
determine an aggregate shift duration, a number of shifts, and an aggregate downtime duration for each resource of the plurality of resources within a specific calendar period based on the set of training data, the plurality of resources including at least one critical resource and at least one non-critical resource;
generate a scheduling available capacity during the specific calendar period for the plurality of resources using the aggregate shift duration, the number of shifts, and the aggregate downtime duration;
receive an efficiency factor for the unified resource capacity data management from the data store, the efficiency factor comprising an aggregate changeover time and an aggregate maintenance time during the specific calendar period;
generate a planning available capacity using the scheduling available capacity and the efficiency factor, the planning available capacity being generated in bucketized time units to synchronize the resource planning data model with the resource scheduling data model, wherein the resource scheduling data model is defined at a finer level of resource and routing detail than the resource planning data model, and wherein the scheduling available capacity is aggregated based on a shift calendar pattern of the resource scheduling data model;
calculate an aggregate scheduling available capacity for the plurality of resources and generate a second planning available capacity using the aggregate scheduling available capacity and the efficiency factor;
retrieve an inventory of units produced for the specific calendar period;
calculate a throughput rate based on the inventory of units produced and a time availability, the time availability based on the aggregate shift duration, the number of shifts, and the specific calendar period; and
generate a third planning available capacity using the throughput rate and the time availability for the plurality of resources;
calculate an optimal resource allocation based on the planning available capacity and the customer demand;
display, on a display, the optimal resource allocation;
automatically identify a change in the set of training data for a resource of the plurality of resources;
receive an indication that the set of training data was updated based on the optimal resource allocation; and
update the planning available capacity for each resource of the plurality of resources based on the updated set of training data to maintain an iterative closed-loop capacity planning process for the specific calendar period.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human can receive data, process the information to train a machine learning model, perform the various determinations and calculations recited throughout the claims, present information on a display, generate capacity information, update planning availability capacity, etc. A human user can analyze and process information related to a bucketized planning data model with a finer-grained scheduling data model. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “unified resource capacity data management” as well as managing and scheduling available capacity without significantly more (abstract), which (under its broadest reasonable interpretation) is an example of business relations (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 includes a system comprising: a storage device, comprising a data store to host data provided from a unified resource capacity data management; at least one processor; and memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform the recited operations at a high level of generality. Claim 1 further recites displaying information on a computing device and receiving an indication via the computing device.
Claim 9 includes at least one non-transitory machine-readable medium comprising instructions for a unified resource capacity data management, which when executed by processing circuitry, cause the processing circuitry to perform the recited operations at a high level of generality. Claim 9 further recites displaying information on a computing device and receiving an indication via the computing device.
Claim 17 recites displaying information on a computing device and receiving an indication via the computing device.
Claims 1, 9, and 17 train a machine learning model using the received set of training data.
Claims 1, 9, and 17 recite that an inventory of units is retrieved from a data store.
Claims 1, 9, and 17 perform determining, various calculations, and the updating using the machine learning model.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 55-61).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶¶ 49-54; Known machine learning models are used as seen in ¶ 54: “The machine learning algorithm 610 may be selected from among many different potential supervised or unsupervised machine learning algorithms. Examples of supervised learning algorithms include artificial neural networks, Bayesian networks, instance-based learning, support vector machines, decision trees (e.g., Iterative Dichotomiser 3, C4.5, Classification and Regression Tree (CART), Chi-squared Automatic Interaction Detector (CHAID), and the like),random forests, linear classifiers, quadratic classifiers, k-nearest neighbor, linear regression, and hidden Markov models. Examples of unsupervised learning algorithms include expectation-maximization algorithms, vector quantization, and information bottleneck method. In an example embodiment, a multi-class logistical regression model is used.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
It is noted that the general idea of the concept of a solution alone is not sufficient to present an improvement that would overcome the rejection. MPEP § 2106.05(a) states, “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.” Generation and synchronization of models speak to details of the abstract ideas. Viewing the models as solutions, the claims still merely present the concept of a solution alone. The solution is never actually executed in the real world to effect a technical solution.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
As explained above, there is nothing in the claims as a whole that adds significantly more to the abstract idea(s). Evidence regarding operations of the additional elements that are well-understood, routine, and conventional is provided below.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite the following operations:
[Claims 3, 11, 19] wherein the planning available capacity is generated only for the at least one critical resource of the plurality of resources.
[Claims 4, 12, 20] wherein the specific calendar period includes at least one of a day, a week, a month, or a year.
[Claims 5, 13, 21] wherein the aggregate shift duration and aggregate downtime duration are calculated in hours.
[Claims 6, 14, 22] wherein the efficiency factor further comprises an aggregate setup-unloading time and unplanned downtime derived from maintenance logs.
[Claims 7, 15, 23] wherein the aggregate scheduling available capacity is computed as a weighted average of throughput of a virtual resource that comprises two or more heterogenous resources.
[Claims 8, 16, 24] wherein the throughput rate is computed as a minimum throughput among individual resources for a routing path to ensure bottleneck-constrained capacity.
The dependent claims further recite details of the abstract ideas identified in regard to the independent claims above.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human can receive data, process the information to train a machine learning model, perform the various determinations and calculations recited throughout the claims, present information on a display, generate capacity information, update planning availability capacity, etc. A human user can analyze and process information related to a bucketized planning data model with a finer-grained scheduling data model. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “unified resource capacity data management” as well as managing and scheduling available capacity without significantly more (abstract), which (under its broadest reasonable interpretation) is an example of business relations (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
The calculation of a weighted average in claims 7, 15, and 23 is an example of a mathematical concept.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of the independent claim from which each depends.
Claims 1 and 3-8 include a system comprising: a storage device, comprising a data store to host data provided from a unified resource capacity data management; at least one processor; and memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform the recited operations at a high level of generality. Claim 1 further recites displaying information on a computing device and receiving an indication via the computing device.
Claims 9 and 11-16 include at least one non-transitory machine-readable medium comprising instructions for a unified resource capacity data management, which when executed by processing circuitry, cause the processing circuitry to perform the recited operations at a high level of generality. Claim 9 further recites displaying information on a computing device and receiving an indication via the computing device.
Claim 17 recites displaying information on a computing device and receiving an indication via the computing device.
Claims 1, 9, and 17 train a machine learning model using the received set of training data.
Claims 1, 9, and 17 recite that an inventory of units is retrieved from a data store.
Claims 1, 9, and 17 perform determining, various calculations, and the updating using the machine learning model.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 55-61).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶¶ 49-54; Known machine learning models are used as seen in ¶ 54: “The machine learning algorithm 610 may be selected from among many different potential supervised or unsupervised machine learning algorithms. Examples of supervised learning algorithms include artificial neural networks, Bayesian networks, instance-based learning, support vector machines, decision trees (e.g., Iterative Dichotomiser 3, C4.5, Classification and Regression Tree (CART), Chi-squared Automatic Interaction Detector (CHAID), and the like),random forests, linear classifiers, quadratic classifiers, k-nearest neighbor, linear regression, and hidden Markov models. Examples of unsupervised learning algorithms include expectation-maximization algorithms, vector quantization, and information bottleneck method. In an example embodiment, a multi-class logistical regression model is used.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
It is noted that the general idea of the concept of a solution alone is not sufficient to present an improvement that would overcome the rejection. MPEP § 2106.05(a) states, “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.” Generation and synchronization of models speak to details of the abstract ideas. Viewing the models as solutions, the claims still merely present the concept of a solution alone. The solution is never actually executed in the real world to effect a technical solution.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
As explained above, there is nothing in the claims as a whole that adds significantly more to the abstract idea(s). Evidence regarding operations of the additional elements that are well-understood, routine, and conventional is provided below.
Allowable Subject Matter
Claims 1, 3-9, 11-17, and 19-24 are allowed over the prior art of record. The claims remain rejected under 35 U.S.C. § 101.
The following is a statement of reasons for the indication of allowable subject matter:
Venkataraman et al. (US 2023/0061899) in view of Lin et al. (WO 2023/129164 A1) most closely address the various concepts recited in each of the independent claims, as seen in the last art rejection of claims 1-2, 6-8, 9-10, 14-16, 17-18, and 22-24 in the Office action dated August 8, 2025. However, the Examiner finds that one of ordinary skill in the art prior to Applicant’s invention would not have, in light of the teachings of the aforementioned references, found it obvious to create the claimed invention with the level of detail and specific manner of integration of operations as they are presented in each of the independent claims. Therefore, claims 1, 3-9, 11-17, and 19-24 are deemed to be allowable over the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Samouilidou, M.E., et al. "A Multi-Bucket Time Representation Framework for Optimal Scheduling in Beverage Production Facilities." Computers and Chemical Engineering 183 (2024) 108611. Available online 26 January 2024. – Optimizes scheduling in beverage production facilities, incorporating shift, capacity, changeovers, demand, etc. into account.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights