DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 5, 2025 has been entered.
Claim 1 has been amended.
Claims 2 and 7-8 have been cancelled.
Claims 1 and 3-6 are pending.
The effective filing date of the claimed invention is April 11, 2023.
Response to Amendment
Amendments to Claim 1 is acknowledged. Amendments to Claim 1 are sufficient to overcome the 35 USC 103 rejection of Claims 1 and 3-6.
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 and 3-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 – Statutory Categories
As indicated in the preamble of the claim, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter.( Claims 1 and 3-6 are machines). Accordingly, step 1 is satisfied.
Step 2A – Prong 1: was there a Judicial Exception Recited
Claim 1 recites the following abstract concepts that are found to include “abstract idea.” Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
An autonomous AI system 101 for reducing food wastage, comprising;
an edge device comprising a processor;
an array of external input peripherals comprising at least one camera device connected to the edge device, the edge device and the array of input peripherals configured for gathering real time data of inventory, including images of the inventory, and processing the data of the inventory by the processor;
a cloud server connected to the edge device for receiving the processed data of the inventory from the edge device, wherein the cloud server using computer vision model detects the inventory from the processed inventory image data, classifies the inventory detected, calculates inventory count based on the classification, and displays inventory count on a dashboard, along with forecasting inventory demand for a future period of time based on historical sales data of inventory (See MPEP 2106.04(a)(2)(III) mental processes, See July 2024 PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “detecting” encompasses mental observations or evaluations that are practically performed in the human mind.”” As discussed in Step 2A, Prong Two above, the recitations of “(a) receiving continuous training data” and “(g) outputting the anomaly data from the trained ANN” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.”);
wherein said system further comprises:
a proprietary AI model that autonomously detects, classifies based on at least one of shape and color of inventory detected in images of inventory, and computes the inventory count of various products on a shelf of an establishment in which the array of external input peripherals are installed, and, further trains and corrects the inventory count in real time (See MPEP 2106.04(a)(2)(III) mental processes, See July 2024 PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “detecting” encompasses mental observations or evaluations that are practically performed in the human mind.”” As discussed in Step 2A, Prong Two above, the recitations of “(a) receiving continuous training data” and “(g) outputting the anomaly data from the trained ANN” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.”);
a co-pilot module that assists in identifying and fixing errors in AI model predictions and further reflecting the corrections in the system in real time (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and PEG Example 47, Claim 2: performing evaluation, judgment, and opinion to make a determination about detected anomalies. Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III); and
a Forecasting AI module, wherein the Forecasting AI module predicts the exact amount of inventory to be sold within a prescribed time frame based on several parameters, including at least one of historical data, spoilage data, and weather data (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and PEG Example 47, Claim 2: performing evaluation, judgment, and opinion to make a determination about detected anomalies. Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III); and
wherein the Forecasting AI module displays the forecasted data on the dashboard, advising a user on the quantity of inventory to be prepared and stocked (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and PEG Example 47, Claim 2: performing evaluation, judgment, and opinion to make a determination about detected anomalies. Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III),
wherein the establishment is at least one of a restaurant and food outlet, and wherein the cloud server monitors classified inventory, advises the at least one of the restaurant and food outlets staff on an amount of food products to be prepared for the prescribed time based on forecasting by the Forecasting AI module (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and PEG Example 47, Claim 2: performing evaluation, judgment, and opinion to make a determination about detected anomalies. Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III);
wherein the proprietary AI model is trained to detect, classify, and track inventory using images of incoming unprepared food products in their packaging to the establishment and images of outgoing food products (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and PEG Example 47, Claim 2: performing evaluation, judgment, and opinion to make a determination about detected anomalies. Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III);
wherein the images of outgoing food products include images of plated food prepared using a portion of the incoming unprepared food products, and wherein the forecasting AI module forecasts quantity of inventory of unprepared food products to be stocked based on tracking of images captured in real time of prepared and plated food products via the at least one camera (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and PEG Example 47, Claim 2: performing evaluation, judgment, and opinion to make a determination about detected anomalies. Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III).
Claim 1 is directed to a series of steps for forecasting an amount of inventory to be sold in a timeframe, which is are mental processes. The mere nominal recitation of an edge device, processor, an array of external input peripherals comprising at least one camera device, a cloud server, and a dashboard, does not take the claim out of the mental processes. Thus, Claim 1 recites an abstract idea.
Step 2A – Prong 2: Can the Judicial Exception Recited be integrated into a practical application
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The identified abstract idea of exemplary Claim 1 is not integrated into a practical application. The additional elements are: an edge device, processor, an array of external input peripherals comprising at least one camera device, a cloud server, and a dashboard. These additional elements are broadly recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Claim 1 is directed to an abstract idea.
Step 2B – Significantly More Analysis
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, steps a) gather real time data of inventory and processing the data of the inventory, b) receiving the processed data to detect the inventory, classify the inventory, calculate inventory, and display inventory on a dashboard, c) detect, classify, and compute the inventory count of various products, d) identifying and fixing errors in predictions, e) predicting the exact amount of inventory to be sold within a time frame, and f) displaying forecasted data and advising a user on a quantity of inventory to be prepared and stocked, do not add significantly more to the exception because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Claim 1 is ineligible.
Claim 3 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 4 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 5 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 6 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Prior Art
The prior arts of record fail to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Exemplary claim 1 recites the following:
An autonomous artificial intelligence system for reducing food wastage, comprising:
an edge device comprising a processor;
an array of external input peripherals comprising at least one camera device connected to the edge device, the edge device and the array of input peripherals configured for gathering real time data of inventory, including images of the inventory, and processing the data of the inventory by the processor;
a cloud server connected to the edge device for receiving the processed data of the inventory from the edge device, wherein the cloud server using computer vision model detects the inventory from the processed inventory image data, classifies the inventory detected, calculates inventory count based on the classification, along with forecasting inventory demand for a future period of time based on historical sales data of inventory;
wherein said system further comprises:
a proprietary AI model that autonomously detects, classifies based on at least one of shape and color of inventory detected in images of inventory, and computes the inventory count of various products on a shelf of an establishment in which the array of external input peripherals are installed, and, further trains and corrects the inventory count in real time;
a co-pilot module that assists in identifying and fixing errors in AI model predictions and further reflecting the corrections in the system in real time; and
a Forecasting AI module, wherein the Forecasting AI module predicts the exact amount of inventory to be sold within a prescribed time frame based on several parameters, including at least one of historical data, spoilage data, and weather data, and wherein the Forecasting AI module displays the forecasted data on the dashboard, advising a user on the quantity of the inventory to be prepared and stocked ;
wherein the establishment is at least one of a restaurant and food outlet, and wherein the cloud server monitors classified inventory, advises the at least one of the restaurant and food outlets staff on an amount of food products to be prepared for the prescribed time based on forecasting by the Forecasting AI module;
wherein the proprietary AI model is trained to detect, classify, and track inventory using images of incoming unprepared food products in their packaging to the establishment and images of outgoing food products;
wherein the images of outgoing food products include images of plated food prepared using a portion of the incoming unprepared food products, and wherein the forecasting AI module forecasts quantity of inventory of unprepared food products to be stocked based on tracking of images captured in real time of prepared and plated food products via the at least one camera. (Emphasis added to highlight features that distinguish over the prior art).
As further explained below, the prior art of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the Applicant’s claimed invention.
US Pat Pub 2022/0270238 “McDonnell” measuring food, food consumption and waste with image recognition and sensor technology is presented to empower cafeterias, processors, quick service restaurants, and other institutions and locations to reduce food waste. Using kitchen and consumer data, the system allows food providers to characterize key food properties, optimize portion sizing, ingredient combinations, and meal preparation, to maximize profits and minimize excess. The current system, process and method optimize the food chain uses analytics and prediction insights. Gathering data through the use of a sensor package (typically including a camera system), and/or smartphone application, a digital platform is then used to give insights to institutions over time. This feedback includes food supplier changes, quality reports, preparation changes, portion sizing, ingredient usage, and customer feedback. McDonnell fails to disclose an AI model trained to detect, classify, and track inventory using images of incoming unprepared food products in their packaging to the establishment and images of outgoing food products, and wherein the forecasting AI module forecasts quantity of inventory of unprepared food products to be stocked based on tracking of images captured in real time of prepared and plated food products via at least one camera.
US Pat Pub 2021/0217159 “Balachandran” measuring waste material weight within two or more waste collection bins The bins can be measured using one or more waste measurement devices, and the measurements can be collected and transmitted to networked data storage. Waste measurement data can be collected over time from multiple bin locations, and the stored data can be analyzed and processed to generate reports and ratings of waste collection, disposal, and diversion trends over time. Balachandran fails to teach an AI model trained to detect, classify, and track inventory using images of incoming unprepared food products in their packaging to the establishment and images of outgoing food products, and wherein the forecasting AI module forecasts quantity of inventory of unprepared food products to be stocked based on tracking of images captured in real time of prepared and plated food products via at least one camera.
US Pat Pub 2025/0005512 “Ward” a property management system (PMS) may be configured to store a digital inventory of a hospitality property. The PMS, for example, may generate a historical future booking data (HFBD) of the hospitality property based on near future booking data and correlated attributes including season of a year, environmental attributes, and booking attributes. For example, the PMS may apply a machine learning model to generate a predicted inventory usage based on the HFBD. Ward fails to teach an AI model trained to detect, classify, and track inventory using images of incoming unprepared food products in their packaging to the establishment and images of outgoing food products, and wherein the forecasting AI module forecasts quantity of inventory of unprepared food products to be stocked based on tracking of images captured in real time of prepared and plated food products via at least one camera.
Response to Arguments
35 USC 101
Applicant's arguments filed December 5, 2025 have been fully considered but they are not persuasive.
Applicant argues that the claims are eligible under 35 USC 101 by the same rationale provided in ex parte Desjardins, and that as a whole, the claim is directed to a computing system that includes an AI model trained in a way that improves its real-time performance. However, this rationale is found to be different than the explanation provided in ex parte Desjardins. In ex parte Desjardins, it was found that “effectively learn new tasks in succession whilst protecting knowledge about previous tasks,” providing improvements to artificial intelligence (AI) systems to “us[e] less of their storage capacity’ and enables “reduced system complexity,” amounted to an improvement in training the machine learning model itself by “adjusting the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task.” This improvement to the machine learning has to do with improving the integrity of the machine learning model by protecting performance of the machine learning model on the first machine learning task. Thus the improvement isn’t to the identification of training data, training a machine learning model, nor the output of the machine learning model. The improvement is to the steps for protecting knowledge about previous tasks in a way that uses less storage capacity and enables reduced system complexity. The claims of this application are merely providing specific data for training and analysis using machine learning. This use of machine learning has been found to be a computerized implementation of the abstract idea of mental processes in PEG Example 47, Claim 2. Therefore, the 35 USC 101 rejection of Claims 1 and 3-6 is maintained.
.
35 USC 103
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed December 5, 2025, with respect to the 35 USC 103 rejection have been fully considered and are persuasive. The 35 USC 103 rejection of Claims 1 and 3-6 has been withdrawn.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/PETER LUDWIG/Primary Examiner, Art Unit 3627