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 .
Notice to Applicant
This communication is in response to the amendment filed 05/06/2026. Claim 1 has been amended. Claim 15 has been canceled. Claims 1-11, 14, 16-17 are presented for examination.
Subject Matter Free of Prior Art
Claim(s) 1-11, 14, 16-17 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “based on such processing, retrieving a mapping information for the one or more medical procedures, relational information, seasonality of medical requirements data, a stock database indicative of inventory and including a threshold data and an indicator, and a historical information for the one or more medical procedures from a memory device, wherein the historical information includes a consumption deviation data related to deviation in consumption of inventory items in the past and a requirement variation data related to deviation between the inventory forecast and the order placed in the past,” “processing the mapping information and the historical information by the processing unit, and automatically generating at least one or more combinations of an inventory forecast related to the inventory of items required by the one or more medical facilities, or a procedure forecast related to a number of medical procedures to be taking place in the one or more medical facilities, a specific medical facility forecast, a disease outbreak forecast, a safety inventory forecast, a cumulative inventory forecast related to the inventory of items required by the one or more medical facilities and a cumulative procedure forecast related to the number of procedures to be taking place in the one or more medical facilities.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claim 1, claim 1 is hereby deemed to be allowable over prior art. Originally numbered dependent claims 2-11, 14, 16-17 incorporate the allowable features of originally numbered independent claim 1 through dependency.
However, the claims are still rejected under 101.
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 1-11, 14, 16-17 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to a method which is within the four statutory categories (i.e., method).
Independent claim 1 recites…receiving a user input related to one or more medical procedures…, wherein the user input comprises identification information related to one or more medical facilities for which a forecast is to be generated; receiving and processing the user input…, and based on such processing, retrieving a mapping information for the one or more medical procedures, relational information, seasonality of medical requirements data, a stock database indicative of inventory and including a threshold data and an indicator, and a historical information for the one or more medical procedures…, wherein the historical information includes a consumption deviation data related to deviation in consumption of inventory items in the past and a requirement variation data related to deviation between the inventory forecast and the order placed in the past, and wherein the threshold data relates to one or more thresholds of quantity of an item required to be kept in the inventory, and the indicator being a level indicator giving visual information to the user regarding stock available for a particular item in the inventory; processing the mapping information and the historical information…, and automatically generating at least one or more combinations of an inventory forecast related to the inventory of items required by the one or more medical facilities, or a procedure forecast related to a number of medical procedures to be taking place in the one or more medical facilities, a specific medical facility forecast, a disease outbreak forecast, a safety inventory forecast, a cumulative inventory forecast related to the inventory of items required by the one or more medical facilities and a cumulative procedure forecast related to the number of procedures to be taking place in the one or more medical facilities…, wherein the one or more combinations of the forecasts are generated for multiple time intervals, wherein the mapping information relates to mapping between a medical procedure of the one or more medical procedures and the inventory of items required to carry out the one or more medical procedures, and wherein the mapping information is dynamically updated based on an editing input received…, the historical information is related to consumption of items in past for the one or more medical procedures... said historical information being of medical facilities for a dynamically configurable geographical area having a radius based on which the forecasts are fine-tuned for medical facilities that form part of said configurable geographical area; retrieving…a current stock information… based on processing the mapping information, wherein the current stock information is related to quantities of each inventory item in the inventory; processing the current stock information along with the at least one of: the inventory forecast, or the procedure forecast, or a combination thereof…and generating a list of items required to be ordered, wherein the list of items are generated…based on the historical information, the current stock information, the relational information, the seasonality of medical requirements data, the inventory, the threshold data, the indicator, and the mapping information, wherein the stock [information] is defined by quantities of each item present in the inventory; and processing…the threshold data and the current stock information, and generating one or more reminders for making an order for the list of items on predefined intervals.
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. That is, other than reciting a “input unit,” “processing unit,” the claim encompasses rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Claim 1 recites additional elements (i.e., input unit; processing unit; memory device storing a historical database; a decision-tree-based ensemble Machine Learning mechanism… wherein the decision-tree-based ensemble Machine Learning mechanism is selected from any or a combination of XG Boost (eXtreme Gradient Boosting) mechanism, Random Forest, SVM (Support Vector Machine), LSTM (Long Short-Term Memory), SARIMA (Seasonal Autoregressive Integrated Moving Average) Gated Recurrent Units, and RNN (Recurrent Neural Network) mechanism; a stock database stored in the memory device). Looking to the specifications, a computing device having an input unit, processing unit, memory device storing databases is described at a high level of generality (¶ 0051-0053; ¶ 0066), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Furthermore, a “decision-tree-based ensemble Machine Learning mechanism…selected from any or a combination of XG Boost (eXtreme Gradient Boosting) mechanism, Random Forest, SVM (Support Vector Machine), LSTM (Long Short-Term Memory), SARIMA (Seasonal Autoregressive Integrated Moving Average) Gated Recurrent Units, and RNN (Recurrent Neural Network) mechanism” is described at a high level of generality (¶ 0063), such that it is only used to generally apply the abstract idea without placing any limits on how the machine learning mechanism functions and only recite the outcome of the abstract idea and does not include details about how “forecast future stock requirements” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. As previously analyzed, the use of a general purpose computer or computers (i.e., a computing device having an input unit, processing unit, memory device storing databases) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Furthermore, a “decision-tree-based ensemble Machine Learning mechanism…selected from any or a combination of XG Boost (eXtreme Gradient Boosting) mechanism, Random Forest, SVM (Support Vector Machine), LSTM (Long Short-Term Memory), SARIMA (Seasonal Autoregressive Integrated Moving Average) Gated Recurrent Units, and RNN (Recurrent Neural Network) mechanism” is only used to generally apply the abstract idea without placing any limits on how the machine learning mechanism functions and only recite the outcome of the abstract idea and does not include details about how “forecast future stock requirements” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2-11, 14, 16-17 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-11, 14, 16-17 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Response to Arguments
Applicant's arguments filed 05/06/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 05/06/2026.
In the remarks, Applicant argues in substance that:
Regarding the 112(b) rejections, the amendments overcome the rejections.
Regarding the 101 rejections,
“Amended independent claim 1 is directed to an improvement in the field of hospital inventory management and forecasting technology… The features as recited in claim 1 do not regulate human conduct, relationships, or interactions between people, but instead define machine-level processing operations performed by computing components. The recited operations define technical operating conditions for automated machine execution and are not rules governing human behavior or social interactions. Any user interaction is merely incidental to the claimed technological process and does not render the claims directed to organizing human activity… The technical solution as provided by independent claim 1 addresses these deficiencies by utilizing a decision-tree-based ensemble Machine Learning mechanism to analyze data including mapping information, relational information, seasonality of medical requirements data, historical consumption deviation data, requirement variation data, and dynamically configurable geographical area parameters to generate accurate forecasts. This approach enables accurate forecasting of the stock requirement, thereby helping hospitals maintain optimal inventory levels and avoid cancellation of medical procedures due to missing supplies. Independent claim 1 is directed to improving the operation of computerized inventory management systems by providing accurate, consumption-based forecasting with dynamic configurability, rather than organizing human conduct…This level indicator provides real-time visual feedback to users regarding inventory status, representing a tangible output that goes beyond mere data analysis or organizing human conduct…This dynamic updating capability ensures that the system adapts to changing medical protocols and procedures, representing a technical improvement in inventory management systems…This reminder generation feature triggers real-world activity - automated reminders that prompt actual ordering of inventory items at predefined intervals - rather than merely analyzing or displaying data…The technical solution disclosed in amended independent claim 1 addresses the limitations of conventional systems by integrating advanced ML- based forecasting with dynamic configurability and automated reminder generation. This integration involves several specific technical steps, including: - Multi-Parameter Forecasting… Dynamic Geographical Configuration… Dynamic Mapping Updates… Visual Level Indicator… Automated Reminder Generation… The inclusion of the automated reminder generation step ensures that the claim is not merely an abstract idea but represents a concrete technological solution that improves hospital inventory management outcomes by ensuring timely ordering of supplies”; and
“The features of amended independent claim 1 integrate the ML-based forecasting with the practical process of effecting inventory management. Specifically: (1) providing a level indicator giving visual information to the user regarding stock available for a particular item in the inventory; (2) dynamically updating the mapping information based on an editing input received by the input unit to adapt to changing medical protocols; (3) fine-tuning forecasts based on a dynamically configurable geographical area having a radius; and (4) generating one or more reminders for making an order for the list of items on predefined intervals. This chain from data retrieval to ML-based forecasting to visual feedback to automated reminder generation demonstrates a clear practical application of ML-based analysis in hospital inventory management. This integration results in a functional improvement in inventory management systems by providing hospitals with accurate forecasts and automated reminders to prevent cancellation of medical procedures due to missing supplies… The practical application of the claimed features significantly enhances the inventory management capabilities of hospitals, providing timely and accurate information that enables hospitals to maintain optimal inventory levels. Furthermore, the claims recite specific technical elements that go beyond generic computer implementation: (a) using a decision-tree-based ensemble Machine Learning mechanism selected from XG Boost, Random Forest, SVM, LSTM, SARIMA, Gated Recurrent Units, and RNN to forecast future stock requirements; (b) dynamically configuring a geographical area having a radius based on which forecasts are fine-tuned for medical facilities within that area; (c) providing a level indicator giving visual information to the user regarding stock available for a particular item in the inventory; and (d) generating one or more reminders for making an order for the list of items on predefined intervals. The practical benefits of the claimed solution are evident in its ability to accurately forecast inventory requirements, provide visual feedback on stock levels, adapt to changing medical protocols through dynamic mapping updates, and generate automated reminders for ordering supplies. The integration of ML- based multi-parameter forecasting with dynamic geographical configuration, visual level indicators, and automated reminder generation represents a significant technological advancement over conventional inventory management systems, further demonstrating the practical application of the claimed features”;
“the specific combination of elements recited in amended independent claim 1 - including ML-based forecasting using decision-tree-based ensemble mechanisms, dynamic geographical configuration with radius-based fine-tuning, consumption deviation and requirement variation data analysis, visual level indicators for stock availability, dynamic mapping updates based on editing input, and automated reminder generation for ordering on predefined intervals - is not routine or conventional in the field of hospital inventory management.”
It is respectfully submitted that Examiner has considered Applicant’s arguments and does not find them persuasive. Examiner has attempted to address all of the arguments presented by Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
In response to Applicant’s argument that (a) regarding the 112(b) rejections, the amendments overcome the rejections:
It is respectfully submitted that Examiner withdraws the aforementioned 112(b) rejections of Office Action dated 11/06/2025 because the amendments have rendered the rejections moot.
In response to Applicant’s argument that (b) regarding the 101 rejections,
“Amended independent claim 1 is directed to an improvement in the field of hospital inventory management and forecasting technology… The features as recited in claim 1 do not regulate human conduct, relationships, or interactions between people, but instead define machine-level processing operations performed by computing components. The recited operations define technical operating conditions for automated machine execution and are not rules governing human behavior or social interactions. Any user interaction is merely incidental to the claimed technological process and does not render the claims directed to organizing human activity… The technical solution as provided by independent claim 1 addresses these deficiencies by utilizing a decision-tree-based ensemble Machine Learning mechanism to analyze data including mapping information, relational information, seasonality of medical requirements data, historical consumption deviation data, requirement variation data, and dynamically configurable geographical area parameters to generate accurate forecasts. This approach enables accurate forecasting of the stock requirement, thereby helping hospitals maintain optimal inventory levels and avoid cancellation of medical procedures due to missing supplies. Independent claim 1 is directed to improving the operation of computerized inventory management systems by providing accurate, consumption-based forecasting with dynamic configurability, rather than organizing human conduct…This level indicator provides real-time visual feedback to users regarding inventory status, representing a tangible output that goes beyond mere data analysis or organizing human conduct…This dynamic updating capability ensures that the system adapts to changing medical protocols and procedures, representing a technical improvement in inventory management systems…This reminder generation feature triggers real-world activity - automated reminders that prompt actual ordering of inventory items at predefined intervals - rather than merely analyzing or displaying data…The technical solution disclosed in amended independent claim 1 addresses the limitations of conventional systems by integrating advanced ML- based forecasting with dynamic configurability and automated reminder generation. This integration involves several specific technical steps, including: - Multi-Parameter Forecasting… Dynamic Geographical Configuration… Dynamic Mapping Updates… Visual Level Indicator… Automated Reminder Generation… The inclusion of the automated reminder generation step ensures that the claim is not merely an abstract idea but represents a concrete technological solution that improves hospital inventory management outcomes by ensuring timely ordering of supplies”:
It is respectfully submitted that Applicant argues “an improvement in the field of hospital inventory management and forecasting technology.” However, as stated previously in Office Action dated 11/06/2025, “hospital inventory management and forecasting” addresses an administrative problem, and not a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution.
Applicant argues “The features as recited in claim 1 do not regulate human conduct, relationships, or interactions between people, but instead define machine-level processing operations performed by computing components. The recited operations define technical operating conditions for automated machine execution and are not rules governing human behavior or social interactions. Any user interaction is merely incidental to the claimed technological process and does not render the claims directed to organizing human activity.” However, Applicant fails to specify how “The features as recited in claim 1 do not regulate human conduct, relationships, or interactions between people” and “are not rules governing human behavior or social interactions.” Furthermore, the “operations” to which Applicant seem to refer are interpreted as rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility, which is the abstract idea of managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components. Furthermore, per MPEP § 2106.04(a)(2)(II), “It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within [the "certain methods of organizing human activity"] grouping.
Applicant argues “The technical solution as provided by independent claim 1 addresses these deficiencies by utilizing a decision-tree-based ensemble Machine Learning mechanism to analyze data including mapping information, relational information, seasonality of medical requirements data, historical consumption deviation data, requirement variation data, and dynamically configurable geographical area parameters to generate accurate forecasts. This approach enables accurate forecasting of the stock requirement, thereby helping hospitals maintain optimal inventory levels and avoid cancellation of medical procedures due to missing supplies. Independent claim 1 is directed to improving the operation of computerized inventory management systems by providing accurate, consumption-based forecasting with dynamic configurability, rather than organizing human conduct.” However, as stated previously in Office Action dated 11/06/2025 and above, “hospital inventory management” and “forecasting” addresses an administrative problem, and not a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. Even if the claims provide the aforementioned alleged improvements, these alleged benefits are at best, an improvement to the abstract idea of rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility. However, an improved abstract idea is still an abstract idea.
Applicant argues “This level indicator provides real-time visual feedback to users regarding inventory status, representing a tangible output that goes beyond mere data analysis or organizing human conduct.” However, Applicant fails to specify how “This level indicator…goes beyond mere data analysis or organizing human conduct.” Regardless, the claim limitations to which Applicant refer are interpreted as rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility, which is the abstract idea.
Applicant argues “This dynamic updating capability ensures that the system adapts to changing medical protocols and procedures, representing a technical improvement in inventory management systems.” However, as stated previously in Office Action dated 11/06/2025 and above, “inventory management” addresses an administrative problem, and not a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. Even if the claims provide the aforementioned alleged improvements (i.e., “adapts to changing medical protocols and procedures”), these alleged benefits are at best, an improvement to the abstract idea of rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility. However, an improved abstract idea is still an abstract idea.
Applicant argues “This reminder generation feature triggers real-world activity - automated reminders that prompt actual ordering of inventory items at predefined intervals - rather than merely analyzing or displaying data.” However, Applicant fails to specify how “triggers real-world activity” is relevant. Regardless, the claim limitations to which Applicant refer are interpreted as rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility, which is the abstract idea.
Applicant argues “The technical solution disclosed in amended independent claim 1 addresses the limitations of conventional systems by integrating advanced ML- based forecasting with dynamic configurability and automated reminder generation. This integration involves several specific technical steps, including: - Multi-Parameter Forecasting… Dynamic Geographical Configuration… Dynamic Mapping Updates… Visual Level Indicator… Automated Reminder Generation… The inclusion of the automated reminder generation step ensures that the claim is not merely an abstract idea but represents a concrete technological solution that improves hospital inventory management outcomes by ensuring timely ordering of supplies.” However, as stated previously in Office Action dated 11/06/2025 and above, “hospital inventory management outcomes” addresses an administrative problem, and not a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. Even if the claims provide the aforementioned alleged improvements (i.e., “ensuring timely ordering of supplies”), these alleged benefits are at best, an improvement to the abstract idea of rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility. However, an improved abstract idea is still an abstract idea.
Thus, the claims recite an abstract idea.
“The features of amended independent claim 1 integrate the ML-based forecasting with the practical process of effecting inventory management. Specifically: (1) providing a level indicator giving visual information to the user regarding stock available for a particular item in the inventory; (2) dynamically updating the mapping information based on an editing input received by the input unit to adapt to changing medical protocols; (3) fine-tuning forecasts based on a dynamically configurable geographical area having a radius; and (4) generating one or more reminders for making an order for the list of items on predefined intervals. This chain from data retrieval to ML-based forecasting to visual feedback to automated reminder generation demonstrates a clear practical application of ML-based analysis in hospital inventory management. This integration results in a functional improvement in inventory management systems by providing hospitals with accurate forecasts and automated reminders to prevent cancellation of medical procedures due to missing supplies… The practical application of the claimed features significantly enhances the inventory management capabilities of hospitals, providing timely and accurate information that enables hospitals to maintain optimal inventory levels. Furthermore, the claims recite specific technical elements that go beyond generic computer implementation: (a) using a decision-tree-based ensemble Machine Learning mechanism selected from XG Boost, Random Forest, SVM, LSTM, SARIMA, Gated Recurrent Units, and RNN to forecast future stock requirements; (b) dynamically configuring a geographical area having a radius based on which forecasts are fine-tuned for medical facilities within that area; (c) providing a level indicator giving visual information to the user regarding stock available for a particular item in the inventory; and (d) generating one or more reminders for making an order for the list of items on predefined intervals. The practical benefits of the claimed solution are evident in its ability to accurately forecast inventory requirements, provide visual feedback on stock levels, adapt to changing medical protocols through dynamic mapping updates, and generate automated reminders for ordering supplies. The integration of ML- based multi-parameter forecasting with dynamic geographical configuration, visual level indicators, and automated reminder generation represents a significant technological advancement over conventional inventory management systems, further demonstrating the practical application of the claimed features”:
Applicant argues “The features of amended independent claim 1 integrate the ML-based forecasting with the practical process of effecting inventory management. Specifically: (1) providing a level indicator giving visual information to the user regarding stock available for a particular item in the inventory; (2) dynamically updating the mapping information based on an editing input received by the input unit to adapt to changing medical protocols; (3) fine-tuning forecasts based on a dynamically configurable geographical area having a radius; and (4) generating one or more reminders for making an order for the list of items on predefined intervals. This chain from data retrieval to ML-based forecasting to visual feedback to automated reminder generation demonstrates a clear practical application of ML-based analysis in hospital inventory management. This integration results in a functional improvement in inventory management systems by providing hospitals with accurate forecasts and automated reminders to prevent cancellation of medical procedures due to missing supplies… The practical application of the claimed features significantly enhances the inventory management capabilities of hospitals, providing timely and accurate information that enables hospitals to maintain optimal inventory levels. Furthermore, the claims recite specific technical elements that go beyond generic computer implementation: (a) using a decision-tree-based ensemble Machine Learning mechanism selected from XG Boost, Random Forest, SVM, LSTM, SARIMA, Gated Recurrent Units, and RNN to forecast future stock requirements; (b) dynamically configuring a geographical area having a radius based on which forecasts are fine-tuned for medical facilities within that area; (c) providing a level indicator giving visual information to the user regarding stock available for a particular item in the inventory; and (d) generating one or more reminders for making an order for the list of items on predefined intervals. The practical benefits of the claimed solution are evident in its ability to accurately forecast inventory requirements, provide visual feedback on stock levels, adapt to changing medical protocols through dynamic mapping updates, and generate automated reminders for ordering supplies. The integration of ML- based multi-parameter forecasting with dynamic geographical configuration, visual level indicators, and automated reminder generation represents a significant technological advancement over conventional inventory management systems, further demonstrating the practical application of the claimed features.” However, as stated previously in Office Action dated 11/06/2025 and above, “inventory management” addresses an administrative problem, and not a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. Even if the claims provide the aforementioned alleged improvements, these alleged benefits are at best, an improvement to the abstract idea of rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility. However, an improved abstract idea is still an abstract idea. Furthermore, the claims to which Applicant seem to refer as “(1) providing a level indicator giving visual information to the user regarding stock available for a particular item in the inventory; (2) dynamically updating the mapping information based on an editing input received by the input unit to adapt to changing medical protocols; (3) fine-tuning forecasts based on a dynamically configurable geographical area having a radius; and (4) generating one or more reminders for making an order for the list of items on predefined intervals” and “(b) dynamically configuring a geographical area having a radius based on which forecasts are fine-tuned for medical facilities within that area; (c) providing a level indicator giving visual information to the user regarding stock available for a particular item in the inventory; and (d) generating one or more reminders for making an order for the list of items on predefined intervals” are interpreted as rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility, which is the abstract idea. The “decision-tree-based ensemble Machine Learning mechanism…selected from any or a combination of XG Boost (eXtreme Gradient Boosting) mechanism, Random Forest, SVM (Support Vector Machine), LSTM (Long Short-Term Memory), SARIMA (Seasonal Autoregressive Integrated Moving Average) Gated Recurrent Units, and RNN (Recurrent Neural Network) mechanism” is described at a high level of generality (¶ 0063), such that it is only used to generally apply the abstract idea without placing any limits on how the machine learning mechanism functions and only recite the outcome of the abstract idea and does not include details about how “forecast future stock requirements” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
As stated previously in Office Action dated 11/06/2025, the computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined. Even a technical solution to a non-technical problem does not integrate the judicial exception into a practical application. Applicant’s claims do not recite the invention of improvements to computer functionality, technology, or any other technological field, but the use of generic computer components (i.e., a computing device having an input unit, processing unit, memory device storing databases) to forecast and manage resources (i.e., inventory) in a medical facility, which is an abstract idea, but for the recitation of generic computer components. Examiner cannot find and Appellant has not identified any problem caused by the technological environment to which the claims are confined (i.e., a well-known, general purpose computer). While the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement to computer technology, a physical improvement to the computer, or any other technical improvement. See MPEP § 2106.04(d)(1) and 2106.05(a).
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
“the specific combination of elements recited in amended independent claim 1 - including ML-based forecasting using decision-tree-based ensemble mechanisms, dynamic geographical configuration with radius-based fine-tuning, consumption deviation and requirement variation data analysis, visual level indicators for stock availability, dynamic mapping updates based on editing input, and automated reminder generation for ordering on predefined intervals - is not routine or conventional in the field of hospital inventory management”:
Applicant argues “the ordered combination would still amount to "significantly more," as the Office Action itself acknowledges that the prior art fails to teach or suggest the features of the evaluation process.” However, whether the elements define only well-understood, routine, conventional activity is not a standalone test for determining eligibility, but an exemplary consideration in a non-limiting list of considerations. Regardless, the claim limitations to which Applicant seem to refer are interpreted as rules or instructions followed to forecast and manage resources (i.e., inventory) in a medical facility. The “decision-tree-based ensemble Machine Learning mechanism…selected from any or a combination of XG Boost (eXtreme Gradient Boosting) mechanism, Random Forest, SVM (Support Vector Machine), LSTM (Long Short-Term Memory), SARIMA (Seasonal Autoregressive Integrated Moving Average) Gated Recurrent Units, and RNN (Recurrent Neural Network) mechanism” is described at a high level of generality (¶ 0063), such that it is only used to generally apply the abstract idea without placing any limits on how the machine learning mechanism functions and only recite the outcome of the abstract idea and does not include details about how “forecast future stock requirements” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claim as a whole does not amount to significantly more than the judicial exception.
Thus, Examiner maintains the 101 rejections of claims 1-11, 14, 16-17, which have been updated to address Applicant’s amendments and remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance in the above Office Action and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
Conclusion
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.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683
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