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 .
This action is made final.
Claims 1-7, 9, 11-13, 15-18 and 20-24 are pending. Claims 8, 10, 14 and 19 are cancelled without prejudice. Claims 1, 11 and 16 are independent claims.
Response to Arguments
Examiner agrees with the applicant’s submission that the “Mohanty” reference is not available as prior art.
Applicant’s arguments, dated 11/26/2025, regarding the 35 U.S.C. 101 rejections of the previous office action have been fully considered, but were not persuasive. Due to the claim amendments, the scope of the claims has changed and new grounds of rejection (35 U.S.C. 103 rejections instead) are applied – see the updated rejections below.
Applicant argues that the amended independent claims now recite generating and transmitting a control signal based on the predicted device recycling opportunity. However, as described in the previous office action, examiner argues that outputting a signal based on the prediction of the recycling opportunity is extra-solution activity of routine data outputting. Aside from the data outputting steps and implementation with generic artificial intelligence techniques (interpreted as additional elements that do not provide an inventive concept – see the 101 rejection below), the prediction step is a process capable of being performed in the human mind. The examiner argues that no additional elements are present in the independent claims that can integrate the mental prediction process into a practical application.
Applicant’s arguments, dated 11/26/2025, regarding the 35 U.S.C. 102 rejections of the previous office action have been fully considered. Due to the claim amendments, the scope of the claims has changed and new grounds of rejection (35 U.S.C. 103 rejections instead) are applied – see the updated rejections below.
Applicant’s arguments, dated 11/26/2025, regarding the 35 U.S.C. 103 rejections of the previous office action have been fully considered. Due to the claim amendments, the scope of the claims has changed and new grounds of rejection are applied – see the updated rejections below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. Claim 1 recites: A computer-implemented method… Claim 1 is directed to a method (Step 1: YES).
Step 2A prong 1: Does the claim recite a judicial exception? Claim 1 recites: comprising… predicting at least one device recycling opportunity for at least one of the one or more devices by processing at least a portion of the determined end of life-related information… Making a prediction about a device recycling opportunity by analyzing data related to the device end of life is a mental process (making a judgement or observation). These steps can be performed mentally or are mathematical calculations (Step 2A prong 1: YES).
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, considered individually and in combination, integrate the judicial exception into a practical application? Claim 1 recites: obtaining data associated with one or more devices; determining end of life-related information for the one or more devices by processing at least a portion of the obtained data… using one or more artificial intelligence techniques trained using historical device information, device- related support information, and recycling-related information;… and performing one or more automated actions based at least in part on the at least one predicted device recycling opportunity, wherein performing one or more automated actions comprises generating, and transmitting to one or more recycling operations systems, at least one control signal for controlling at least portions of at least one asset recovery and recycling application within the one or more recycling operations systems, the at least one control signal being based at least in part on the at least one predicted device recycling opportunity; wherein the method is performed by at least one processing device comprising a processor coupled to a memory. Obtaining and processing data related to a device recycling opportunity is extra-solution activity of data gathering that does not add a meaningful limitation to the recycling opportunity prediction. Similarly, performing an automated action in response to the prediction comprising generation and transmission of a control signal based on the prediction is an extra-solution activity of data outputting that does not add a meaningful limitation to the recycling opportunity prediction. Stating that the prediction is done using artificial intelligence techniques and that the method is performed by a processing device are mere instructions to implement the abstract idea on a generic computer, which is equivalent to adding the words “apply it” to the recited judicial exception. Training the artificial intelligence techniques with “historical device information, device- related support information, and recycling-related information” is an attempt to limit the field of use of the prediction model, or insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated (see MPEP 2106.05(g)) (Step 2A prong 2: NO).
Step 2B: These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since they only amount to data gathering, data outputting, or selecting without significantly more (MPEP 2106.05(g)) (also see, e.g., CyberSource v. Retail Decisions and Electric Power Group, LLC v. Alstom S.A., both of which were found to merely perform data gathering or selecting a particular data source or type of data to be manipulated), provide nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)), or limit the field of use without significantly more (MPEP 2106.05(h)). These limitations, taken either alone or in combination, fail to provide an inventive concept (Step 2B: NO). Thus, the claim is not patent eligible.
Regarding claims 2-7 and 9, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occurs (Claim 2, processing data using a gradient boosting classifier model comprising multiple decision tree based models is mere instructions to implement the abstract idea on a generic computer; Claim 3, processing data using a gradient boosting classifier model including an extreme gradient boosting classifier model is mere instructions to implement the abstract idea on a generic computer; Claim 4, identifying devices that have exceeded a predetermined end of life status is a mental process; Claim 5, identifying devices that are within a threshold value of a predetermined end of life status is a mental process; Claim 6, obtaining the types of data listed is still an additional element of data gathering without significantly more; Claim 7, the automated action being outputting a notification related to the predicted device recycling opportunity to a user is still an additional element of data outputting without significantly more; Claim 9, the automated action being training the AI techniques with feedback related to the prediction is mere instructions to implement the abstract idea on a generic computer and does not add a meaningful limitation to the prediction method).
Regarding claim 11, it is an apparatus that implements the method of claim 1 and is rejected on the same grounds – see above.
Regarding claims 12, 13 and 15, they recite similar limitations to claims 2, 7 and 9 respectively and are rejected on the same grounds – see above.
Regarding claim 16, it is another apparatus that implements the method of claim 1 and is rejected on the same grounds – see above.
Regarding claims 17, 18 and 20, they recite similar limitations to claims 2, 7 and 9 respectively and are rejected on the same grounds – see above.
Regarding claims 21 and 22, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occurs (Claim 21, identifying if a device has exceeded a predetermined end of life status is a mental process, like evaluating a parameter, or insignificant extra-solution activity of data gathering; Claim 22, identifying if a device is within a threshold value of a predetermined end of life status is similarly a mental process, i.e., evaluating a parameter, or perhaps insignificant extra-solution activity of data gathering).
Regarding claims 23 and 24, they recite limitations similar to claims 21 and 22 respectively and are rejected on the same grounds – see above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 7, 9, 11-13, 15-18, and 20 is/are rejected under 35 U.S.C. 103 as being anticipated by Kummari et al. (US 20190152011 A1), herein Kummari, in view of Borlick et al. (US 20200004435 A1), herein Borlick and Forutanpour et al. (US 20210192484 A1), herein Forutanpour.
Regarding claim 1, Kummari teaches: A computer-implemented method comprising: obtaining data associated with one or more devices; determining end of life-related information for the one or more devices by processing at least a portion of the obtained data (¶31, According to various example embodiments, the system 200 includes a sensor monitoring system (i.e., sensors 150) to capture data from the milling machine 100 in real-time); predicting at least one device recycling opportunity for at least one of the one or more devices by processing at least a portion of the determined end of life-related information using one or more artificial intelligence techniques (¶31, The system 200 also includes a machine Learning model (i.e., software executing on host server 210) which predicts tool failure in advance)… and performing one or more automated actions based at least in part on the at least one predicted device recycling opportunity (¶31, alerting the user when a tool is about to fail)… wherein the method is performed by at least one processing device comprising a processor coupled to a memory (¶51, the computing system 800 includes a network interface 810, a processor 820, an output 830, and a storage device 840 such as a memory).
Kummari fails to teach: trained using historical device information, device- related support information, and recycling-related information.
However, in the same field of endeavor, Borlick teaches: trained using historical device information, device- related support information, and recycling-related information (¶13, the attributes used as the input to the machine learning module include a plurality of: a response time to respond to read and write requests to the storage device; a response time to respond to read and write requests to a storage array including the storage device; for each of at least one error type, a number of errors of the error type in a specified time interval; a type of the storage device; a manufacturer of the storage device; a storage capacity of the storage device; a time of first use of the storage device; a firmware level of the storage device; a read operations per second at the storage device; an expected remaining lifespan of the storage device; and write operations per second at the storage device – a response time, i.e., historical device information; a device type, i.e., device-related support information; a time of first use, i.e., recycling related information).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use historical device information, support information and recycling information as disclosed by Borlick in the method disclosed by Kummari to provide accurate estimates about when a device will fail, improving the user experience (¶5, provides accurate expected remaining life information the system administrator may use to determine when to replace a deployed storage device to maximize the operational life of a storage device and replace before failure).
Kummari in view of Borlick fails to teach: wherein performing one or more automated actions comprises generating, and transmitting to one or more recycling operations systems, at least one control signal for controlling at least portions of at least one asset recovery and recycling application within the one or more recycling operations systems, the at least one control signal being based at least in part on the at least one predicted device recycling opportunity.
However, in the same field of endeavor, Forutanpour teaches: wherein performing one or more automated actions comprises generating, and transmitting to one or more recycling (¶27, The term “recycling” is used herein for ease of reference to generally refer to purchasing, reselling, exchanging, donating, etc. mobile phones and other electronic devices. For example, owners may elect to sell their used electronic devices at the kiosk 100, and the electronic devices can be recycled for resale, reconditioning, repair, recovery of salvageable components, environmentally conscious disposal, etc.) operations systems, at least one control signal for controlling at least portions of at least one asset recovery and recycling application within the one or more recycling operations systems, the at least one control signal being based at least in part on the at least one predicted device recycling opportunity (¶38, after the visual analysis is performed and the device 150 has been identified… display screen 104 can also provide an estimated price or an estimated range of prices that the kiosk portion 101 may offer the user for the mobile phone 150 based on the visual analysis and/or based on user input (e.g., input regarding the type, condition, etc. of the mobile phone 150) – the control signal in this case is the signal that the display screen receives to display the estimated, or predicted price of the potentially to-be-recycled device).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to generate and transmit a control signal to one or more recycling operation systems as disclosed by Forutanpour in the method disclosed by Kummari in view of Borlick to conveniently automate the device recycling process (¶24, phones that have been purchased by the kiosk do not have to be retrieved from the kiosk, manually prepared for resale by service personnel, and the restocked in a vending kiosk or offered for sale through other channels. Instead, embodiments of the kiosk systems described herein can purchase phones, automatically process them for resale, and then resell them without the phones ever having to be removed from the kiosk).
Regarding claim 2, Kummari further teaches: The computer-implemented method of claim 1, wherein predicting at least one device recycling opportunity comprises processing at least a portion of the determined end of life-related information using at least one gradient boosting classifier model comprising multiple decision tree-based models (¶38, An ensemble model combining several machine learning techniques such as… Random Forest).
Regarding claim 3, Kummari further teaches: The computer-implemented method of claim 2, wherein processing at least a portion of the determined end of life-related information using at least one gradient boosting classifier model comprises implementing at least one extreme gradient boosting algorithm as an extension to the at least one gradient boosting classifier model (¶39, failure prediction accuracies may be achieved by combining one or more of random forests, support vector machine, extreme gradient boosting).
Regarding claim 4, Kummari further teaches: The computer-implemented method of claim 1, wherein determining end of life- related information for the one or more devices comprises identifying at least one of the one or more devices that has exceeded a predetermined end of life status (¶43, The system can assign labels to all the tools (healthy, nearing end, end of life, etc.) – assigning an end of life label to a power tool is identifying that the device has exceeded a predetermined end of life status).
Regarding claim 5, Kummari further teaches: The computer-implemented method of claim 1, wherein determining end of life- related information for the one or more devices comprises identifying at least one of the one or more devices that is within a given threshold value of a predetermined end of life status (¶43, The system can assign labels to all the tools (healthy, nearing end, end of life, etc.)).
Regarding claim 7, Kummari further teaches: The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically generating and outputting at least one notification, in accordance with the at least one predicted device recycling opportunity, to at least one user associated with the at least one device (¶20, the system can output a notification to a plant operator with insight into the life and health of the cutting tool as well as notifications when it is or when it will be time to replace the cutting tool).
Regarding claim 9, Kummari further teaches: The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training the one or more artificial intelligence techniques using feedback related to the at least one predicted device recycling opportunity (¶36, If the current signature deviates significantly from the benchmark curve, it is likely an indication that an anomaly is being detected and the cutting tool is nearing or has reached the end of its life. In a situation in which the model comes across a new pattern that was not seen in training data, Bayesian Change point detection technique may be used to learn the new pattern).
Regarding claims 11, 12, 13 and 15, they recite similar limitations to claims 1, 2, 7 and 9 respectively, and are rejected on the same grounds – see above.
Regarding claims 16, 17, 18 and 20, they also recite similar limitations to claims 1, 2, 7 and 9 respectively, and are rejected on the same grounds – see above.
Regarding claim 21, Kummari in view of Forutanpour fails to teach: The apparatus of claim 16, wherein determining end of life-related information for the one or more devices comprises identifying at least one of the one or more devices that has exceeded a predetermined end of life status.
However, in the same field of endeavor, Borlick teaches: wherein determining end of life-related information for the one or more devices comprises identifying at least one of the one or more devices that has exceeded a predetermined end of life status (¶9, the input is provided to the machine learning module in response to an event that tends to indicate an increased likelihood of disk failure, such as an error at the storage device or the measured age reaching a threshold level).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify that a device has exceeded a predetermined threshold level as disclosed by Borlick in the apparatus disclosed by Kummari in view of Forutanpour to improve model efficiency (¶9, Running the machine learning module in response to such events optimizes allocation of computational resources by invoking the machine learning module to determine an expected remaining life in response to events that may negatively impact the remaining life).
Regarding claim 22, Kummari in view of Forutanpour fails to teach: The apparatus of claim 16, wherein determining end of life-related information for the one or more devices comprises identifying at least one of the one or more devices that is within a given threshold value of a predetermined end of life status.
However, in the same field of endeavor, Borlick teaches: wherein determining end of life-related information for the one or more devices comprises identifying at least one of the one or more devices that is within a given threshold value of a predetermined end of life status (¶8, In a further embodiment, the event comprises detecting that a measured age of the storage device comprises a predetermined percentage of an expected lifetime of the storage device).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify that a device is within a predetermined threshold level as disclosed by Borlick in the apparatus disclosed by Kummari in view of Forutanpour to improve model efficiency (¶9, Running the machine learning module in response to such events optimizes allocation of computational resources by invoking the machine learning module to determine an expected remaining life in response to events that may negatively impact the remaining life).
Regarding claims 23 and 24, they recite similar limitations to claims 21 and 22 respectively and are rejected on the same grounds – see above.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kummari in view of Borlick and Forutanpour as applied to claim 1 above, and further in view of Venugopal et al. (US 20220036214 A1), herein Venugopal.
Regarding claim 6, Kummari further teaches: The computer-implemented method of claim 1, wherein obtaining data associated with one or more devices comprises obtaining one or more of… temporal-related data (¶28, The sensors may sense time-series data and transmit the data back to the host server 210).
Kummari in view of Borlick and Forutanpour fails to teach: telemetry data, configuration data, user-related data.
However, in the same field of endeavor, Venugopal teaches: telemetry data, configuration data, user-related data (¶28, The computing device 102 may, periodically or in response to determining that a particular set of events has occurred, send telemetry data 118 that includes the usage data 110, the logs 112, the configuration data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to obtain telemetry data, configuration data and usage data as disclosed by Venugopal in the method disclosed by Kummari in view of Borlick and Forutanpour to predict device status for a certain customer or component and maintain supply chain (¶32, when a particular customer is predicted to initiate a service request to address an issue associated with multiple computing devices of the particular customer, and the like – and – ¶20, provide demand forecasts for particular components that are predicted to fail, enabling the manufacture to request suppliers to schedule and initiate production of the particular components).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HARRISON CHAN YOUNG KIM whose telephone number is (571)272-0713. The examiner can normally be reached Monday - Friday 8:00 am - 4:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HARRISON C KIM/ Examiner, Art Unit 2145
/CESAR B PAULA/ Supervisory Patent Examiner, Art Unit 2145