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 .
Status of Claims
This action is in reply to the present action filed on 07/09/2026.
Claims 1, 11-12, 14, and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/09/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 6-9 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 6 recites “the soon to expire analysis model comprises a machine learning model and one or more heuristic models”. This limitation is already introduced in independent Claim 1, upon which Claim 6 relies, and therefore claim 6 fails to further limit the subject matter. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. Claims 7-9 are further rejected as being dependent on a rejected claim. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claims 1, 14, and 20 are within one of the four statutory categories. Claims 1, 14, and 20 are directed to a method, a system (i.e. machine) and a non-transitory computer-readable medium (i.e. machine), respectively. Dependent Claims 2-13 are directed to a method and Claims 15-19 are directed to a system, and therefore the dependent claims are also within the statutory categories.
Step 2A Analysis – Prong One:
The substantially similar independent claims, taking Claim 1 as exemplary, recite the following:
A method comprising: under control of one or more processors, receiving training data comprising historical data related to an item;
training a soon to expire analysis model using the training data to generate a trained soon to expire analysis model, the trained soon to expire analysis model configured to receive item data associated with the item and generate a soon to expire prediction for one or more items associated with the item data,
wherein the soon to expire analysis model comprises a machine learning model and one or more heuristic models each of the one or more heuristic models being associated with a different requirement for a quantity of training data;
receiving inventory information for the item;
processing at least a portion of the inventory information with the trained soon to expire analysis model to determine the item stocked at one or more locations as being likely to remain unused past a target date;
and providing an output, to an inventory controller, to perform an action to prevent the item from remaining unused past the target date.
The series of steps as shown in underline above, given the broadest reasonable interpretation, recite the abstract idea of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions- in this case, receiving training data, training an analysis model, the analysis model including a heuristic model, receiving inventory information for the item, processing the inventory information using an analysis model, and providing an output to perform an action to prevent the item from remining unused past the target date) e.g., see MPEP 2106.04(a)(2). Any limitations not identified as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2-5, 7-13, and 15-19 recite other limitations directed toward the abstract idea. For example, Claim 2 recites the action to prevent the item from remaining unused past the expiration date comprises moving a portion of the item from the one or more locations to a use location, Claim 3 recites determining the item stocked at the locations as being likely to remain unused past the expiration date comprises comparing a categorical value to a threshold, Claim 4 recites the categorical value comprises one or more of a unit cost, a usage velocity, and a moving speed of the item, Claim 5 recites the historical data comprises inventory changes from a plurality of locations, Claim 7 recites training the model comprises determining whether historical data satisfies a first threshold and executing the training, Claim 8 recites determining whether the historical data satisfies the first threshold and if it fails, determining if the data satisfies a second threshold, Claim 9 recites heuristic models comprise defining an association between an item unit value and an item usage rate, an item value and the item usage rate, or an item unit value and a distance to earliest expiration date, Claims 10 and 19 recite training comprises any of a supervised training, an unsupervised training, a reinforced training, a dynamic training, or a hybrid training, Claim 11 recites generating a first and second analysis model, measuring resource utilization, and selecting one of the models based on the resource utilization, Claim 12 recites the target data is an expiration date for the item, a predetermined amount of time from a current date, or a scheduled inventory update date, Claim 13 recites the first instance of the item is available at a first location, and the action to prevent the item from remaining unused past the target date comprises upon receiving a request to dispense the item, dispensing the item from the first location, Claim 15 recites moving a portion of the item to a use location, Claim 16 recites determining the item stocks as being likely to remain unused past the expiration and what the categorical value comprises, Claim 17 recites heuristic models comprise defining an association between an item unit value and an item usage rate, item value and the item usage rate, or item unit value and distance to earliest expiration date, Claim 18 recites determining if the historical data satisfies a first and a second threshold. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g., see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-13 and 15-19 not addressed above are deemed additional elements to the abstract idea and will be further addressed below. Hence dependent Claims 2-5, 7-13, and 15-19 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 14, and 20.
Step 2A Analysis – Prong Two:
Claims 1, 14, and 20 are not integrated into a practical application because the additional elements (i.e., the non-underlined limitations above – in this case, the processor, machine learning model, and inventory controller of Claim 1, the processor, memory, and machine learning model of Claim 14, and the non-transitory computer-readable medium, processor, and machine learning model of Claim 20) are recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply an exception using generic computer parts. For example, Applicant’s specification explains that the medical inventory management system 100 may include an inventory controller 110, a client device 120, and one or more medical locations 130. As shown in FIG. 1, the inventory controller 110, the client device 120, and the one or more medical locations 130 may be communicatively coupled via a network 140 (see Applicant’s specification, ¶ 0023). Similarly, computer systems are also described that may include one or more processors and one or more memories coupled to the one or more processors. A memory, which can include a non-transitory computer-readable or machine-readable storage medium, may include, encode, store, or the like one or more programs that cause one or more processors to perform one or more of the operations described herein [0005]. As noted, the soon-to-expire (STE) analysis model 115 may be implemented as one or more heuristic models and/or machine learning models. Thus, it should be appreciated that one or more aspects of the artificial intelligence described may be implemented in whole or in part by a model, including a machine learning model [0049]. Accordingly, these additional elements, whether considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore, independent Claims 1, 14, and 20 are directed to an abstract idea without practical application.
Dependent Claims 2, 6-7, 11, 13, 15, and 17 also recite additional elements. Claim 2 recites the previously recited inventory controller and specifies the inventory controller moves a portion of the item from the location to a use location, Claim 6 recites a new additional element of a machine learning model and specifies the soon to expire analysis model comprises a machine learning model, Claim 7 recites the previously recited machine learning model and specifies executing the training using the machine learning model, Claim 11 recites the previously recited machine learning model and specifies the machine learning model receives the training item data and provides output to the heuristic model, and the machine learning model receives a second output from the heuristic model, Claim 13 recites the previously recited inventory controller and specifies the first instance of the item is available at a first location managed by the inventory controller and a second instance of the item is available a second location managed by the inventory controller, and the inventory controller dispenses the item from the first location, Claim 15 recites the inventory controller and specifies the inventory controller moves a portion of the item from one location to a use location, Claim 17 recites a new element of a machine learning model and specifies the analysis model comprises a machine learning model. However, these additional elements are described only at a high level of generality and are being used in their expected fashion, so these additional elements do not integrate the abstract idea into a practice application because they do not impose any meaningful limits on the abstract idea. These limitations amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, whether considered individually or as an ordered combination, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to step 2A prong two, the additional elements of the processor, machine learning model, and inventory controller of Claim 1, the processor, memory, and machine learning model of Claim 14, and the non-transitory computer-readable medium, processor, and machine learning model of Claim 20 amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”) in step 2B. MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more"). For these reasons, independent Claims 1, 14, and 20 are not patent eligible.
Dependent Claims 3-5, 8-10, 12, 16, and 19 do not recite any additional elements and further narrow the abstract idea. Claim 3 recites determining the item stocked at the locations as being likely to remain unused past the expiration date comprises comparing a categorical value to a threshold. Claim 4 recites the categorical value comprises one or more of a unit cost, a usage velocity, and a moving speed of the item. Claim 5 recites the historical data comprises inventory changes from a plurality of locations. Claim 8 recites determining whether the historical data satisfies the first threshold and if it fails, determining if the data satisfies a second threshold, Claim 9 recites heuristic models comprise defining an association between an item unit value and an item usage rate, an item value and the item usage rate, or an item unit value and a distance to earliest expiration date. Claims 10 and 19 recite training comprises any of a supervised training, an unsupervised training, a reinforced training, a dynamic training, or a hybrid training. Claim 12 recites the target data is an expiration date for the item, a predetermined amount of time from a current date, or a scheduled inventory update date. Claim 16 recites determining the item stocks as being likely to remain unused past the expiration and what the categorical value comprises.
Dependent Claims 6, 15, and 17 recite new additional elements. Claim 6 recites a new additional element of a machine learning model and specifies the soon to expire analysis model comprises a machine learning model. Claim 15 recites a new element of an inventory controller and specifies the inventory controller moves a portion of the item from one location to a use location. Claim 17 recites a new element of a machine learning model and specifies the analysis model comprises a machine learning model.
Dependent Claims 2, 7, 11, and 13 recite previously recited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claim 2 recites the previously recited inventory controller and specifies the inventory controller moves a portion of the item from the location to a use location, Claim 7 recites the previously recited machine learning model and specifies executing the training using the machine learning model, Claim 11 recites the previously recited machine learning model and specifies the machine learning model receives the training item data and provides output to the heuristic model, and the machine learning model receives a second output from the heuristic model, Claim 13 recites the previously recited inventory controller and specifies the first instance of the item is available at a first location managed by the inventory controller and a second instance of the item is available a second location managed by the inventory controller, and the inventory controller dispenses the item from the first location.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination does not add anything that is already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to a non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-6, 10, 12-16, and 19-20 are rejected under 35 USC § 103 as being unpatentable over Tiwari et al. (US 20210192436 A1) in view of Breese et al. (US 20210365876 A1) and Wook et al. (KR 20210023641 A).
Regarding Claim 1, Tiwari discloses the following:
A method comprising: under control of one or more processors, (Tiwari discloses methods may include one or more devices, such as one or more computers or other terminal devices and/or computer systems for managing inventory through the supply chain, and/or managing the expiration dates of the inventory…[0007]. Computer system 400 includes one or more processors, such as processor 404 [0048].)
receiving…data comprising historical data related to an item; (Tiwari discloses the method may include reviewing and analyzing current and historical data patterns and/or trends of the lifecycle of products affixed with RFID tags [0036].)
the…soon to expire analysis model configured to receive item data associated with the item and generate soon to expire prediction for one or more items associated with the item data; (Tiwari discloses the product expiration management system 140 may continuously review the data provided to the system for storing, along with data previously stored in the system, …to determine if a product is likely to expire prior to being used/consumed, using the machine learning algorithms…Using previously stored data and machine learning algorithms, the product expiration management system 140 may also take into account the typical usage rate of product X, …Based on this analysis, the hospital may have 150 units likely to expire without being used/consumed, and thus the system may generate an alert, as described in more detail below, indicating that an action should be taken regarding the 150 units of product X likely to expire without being timely used [0039].)
receiving inventory information for the item; (Tiwari discloses referring to method 300 of FIG. 3, at block 310 the system receives data regarding a product affixed with a RFID tag. For example, as discussed above in relation to FIGS. 1 and 2, a product may be registered in the product expiration management system 140 (FIG. 1), along with corresponding data regarding the product, for example, the location of the consumer where the product was received, the specific location of the product within the location of the consumer, the time the product was received, the type of product, and the product expiration, among other data [0037]. The Examiner interprets the location, type and product expiration as inventory information for the item.)
processing at least a portion of the inventory information with the…soon to expire analysis model to determine the item stocked at one or more locations as being likely to remain unused past a target date; (Tiwari discloses the product expiration management system 140 may continuously review the data provided to the system for storing, along with data previously stored in the system, for example, to determine if a product is likely to expire prior to being used/consumed, using the machine learning algorithms… the machine learning algorithms may be continuously updated based on data provided on a lifecycle of a product and/or location, and/or consumer. In another aspect of the disclosure, the machine learning algorithms may be implemented to forecast if and when products may expire prior to being used/consumed [0039].)
and providing an output, to an inventory controller, to perform an action to prevent the item from remaining unused past the target date. (Tiwari discloses through the use of machine learning algorithms with access to data from a network of consumer locations, the system may make a determination to relocate the product to another location within the same consumer location or to another consumer location. For example, the product expiration management system 140 of FIG. 1 may determine that hospital A has quantities of product X stored in operating room 1 that may be expiring in a month. The alert (block 340 of FIG. 3) may indicate that operating room 2 uses large quantities of product X and that a user should relocate quantities of the product from operating room 1 to operating room 2 [0046].)
Tiwari does not disclose using training data to train the model which is met by Breese:
training a soon to expire analysis model using the training data to generate a trained soon to expire analysis model, (Breese teaches machine learning/prediction module 110 can obtain data from training data set(s) such as those stored in predictive inventory purchasing database 112. Machine learning/prediction module 110 can create a predictive inventory purchasing model from training data from predictive inventory purchasing database 112 [0027]. [W]herein the training data comprises a set of historical purchasing and sales transactions (Claim 4).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate training the machine learning model using training data as taught by Breese. This modification would make inventory management more efficient by predicting inventory needs (see Breese, ¶ 0004-5).
Tiwari and Breese do not teach the use of a machine learning model and heuristic models which is met by Wook:
wherein the soon to expire analysis model comprises a machine learning model and one or more heuristic models each of the one or more heuristic models being associated with a different requirement for a quantity of training data; (Wook teaches a rule-based model based on at least one predefined rule and a machine learning model based on massive sample data may each have unique advantages and disadvantages due to different characteristics. For example, rule-based models can be easily understood by humans and require relatively little data (p. 2, ¶ 0005). Referring to FIG. 14B, the hybrid model 140b may include a first rule-based model 142b, a first machine learning model 144b, and a second machine learning model 146b. Accordingly, in the hybrid models 140a and 140b, the first rule-based models 142a and 142b may process an input in parallel with the machine learning models 144a and 144b. In some embodiments, the hybrid model generates a first output OUT1 and a second output OUT2 generated by the first rule-based models 142a and 142b and the first machine learning models 144a and 144b. It may include both the second rule-based model and the second machine learning-based model to be received (p. 11, ¶ 0001).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate the model comprising both a machine learning model and a heuristic model as taught by Wook. This modification would create a model which minimizes the disadvantages and maximizes the advantages for each model type (see Wook, p. 2, ¶ 0002).
Regarding Claim 2, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 1 above. Tiwari further discloses:
the action to prevent the item from remaining unused past the expiration date comprises moving, by the inventory controller, at least a portion of the item from the one or more locations to a use location. (Tiwari discloses the system may make a determination to relocate the product to another location within the same consumer location or to another consumer location. For example, the product expiration management system 140 of FIG. 1 may determine that hospital A has quantities of product X stored in operating room 1 that may be expiring in a month. The alert (block 340 of FIG. 3) may indicate that operating room 2 uses large quantities of product X and that a user should relocate quantities of the product from operating room 1 to operating room 2 [0046].)
Regarding Claim 3, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 1 above. Tiwari further discloses:
determining the item stocked at the one or more locations as being likely to remain unused past the expiration date comprises comparing a categorical value to a threshold. (Tiwari discloses using previously stored data… the product expiration management system 140 may also take into account the typical usage rate of product X at hospital A, which may indicate that only 50 units of product X are typically used/consumed per month. Based on this analysis, the hospital may have 150 units likely to expire without being used/consumed, and thus the system may generate an alert, as described in more detail below, indicating that an action should be taken regarding the 150 units of product X likely to expire without being timely used [0039] [0039]. The Examiner interprets the usage rate of the product as the categorical value and the historical rate as the threshold.)
Regarding Claim 4, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 3 above. Tiwari further discloses:
the categorical value comprises one or more of a unit cost, a usage velocity, and a moving speed of the item. (Tiwari discloses using previously stored data…system 140 may also take into account the typical usage rate of product X at hospital A, which may indicate that only 50 units of product X are typically used/consumed per month. Based on this analysis, the hospital may have 150 units likely to expire without being used/consumed, and thus the system may generate an alert, as described in more detail below, indicating that an action should be taken regarding the 150 units of product X…[0039]. The Examiner interprets the usage rate of the product as the categorical value of a usage velocity.)
Regarding Claim 5, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 1 above. Tiwari further discloses:
the historical data comprises inventory changes from a plurality of locations. (Tiwari discloses radio frequency identification (RFID) tags are frequently used to identify and track medical products. For example, RFID tags may be attached to some medical products for purposes of tracking [0005]. The product expiration management system 140 may track one or more products tagged with an RFID tag through the supply chain, for example, from manufacture to expiration of the product… [0024].)
Regarding Claim 6, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 1 above. Tiwari and Breese do not teach the following limitations met by Wook:
the soon to expire analysis model comprises a machine learning model and one or more heuristic models. (Wook teaches the hybrid model 10 according to an exemplary embodiment of the present disclosure combines the rule-based model 12 and the machine learning model 14 to provide the rule-based model 12 (p. 3, ¶ 0001). The Examiner interprets the rule-based model as the heuristic model.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate the model comprising both a machine learning model and a heuristic model as taught by Wook. This modification would create a model which minimizes the disadvantages and maximizes the advantages for each model type (see Wook, p. 2, ¶ 0002).
Regarding Claim 10, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 1 above. Tiwari does not disclose the following limitations met by Breese:
training comprises any of a supervised training, an unsupervised training, a reinforced training, a dynamic training, or a hybrid training. (Breese teaches deep learning is a family of machine learning methods based on learning data representations. Learning can be supervised, semi-supervised or unsupervised. The model (e.g. a neural net or a naive Bayes classifier) is trained on the training dataset using a supervised learning method (e.g. gradient descent or stochastic gradient descent) [0029-30].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate training the machine learning model using supervised or unsupervised training as taught by Breese. This modification would make inventory management more efficient by predicting inventory needs (see Breese, ¶ 0004-5).
Regarding Claim 12, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 1 above. Tiwari further discloses:
the target date is one of: an expiration date for the item, a predetermined amount of time from a current date, or a scheduled inventory update date. (Tiwari discloses method 300, as described below, may be used to determine why the product was not used/consumed prior to the expiration date, how to use/consume the product prior to the expiration date, and how to correct the problem of ensuring that a similar product in the future is not left to expire.)
Regarding Claim 13, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 1 above. Tiwari further discloses:
a first instance of the item is available at a first location…and a second instance of the item is available a second location (Tiwari discloses the product expiration management system 140 of FIG. 1 may determine that hospital A has quantities of product X stored in operating room 1 that may be expiring in a month. The alert (block 340 of FIG. 3) may indicate that operating room 2 uses large quantities of product X [0046].)
and wherein the action to prevent the first instance of the item from remaining unused past the target date comprises… (Tiwari discloses the product expiration management system 140 of FIG. 1 may determine that hospital A has quantities of product X stored in operating room 1 that may be expiring in a month. The alert (block 340 of FIG. 3) may indicate that operating room 2 uses large quantities of product X and that a user should relocate quantities of the product from operating room 1 to operating room 2 [0046].)
Tiwari does not disclose dispensing the item which is met by Breese:
…managed by the inventory controller,… (Breese teaches a robotic medication dispensing system that dispenses the medication [0040]. The Examiner interprets the medication dispensing system as the inventory controller.)
…the action …configuring the inventory controller to, upon receiving a request to dispense the item, dispense the item from the first location. (Breese teaches machine learning/prediction module 110 can enable the various modules of automated medication dispensing and delivery system server(s) 106 to perform specific tasks without using explicit instructions [0027]. Process 600 can obtain several voice commands from a user using a virtual personal assistant…In step 606, process 600 can manage a robotic medication dispensing system that dispenses the medication [0040].)
Regarding Claim 14, Tiwari discloses the following:
A system, comprising: at least one data processor; and at least one memory storing instructions which, when executed by the at least one data processor, result in operations comprising: (Tiwari discloses the consumer system 130, and the product expiration management system 140 may also include a memory that stores instructions for executing processes for managing inventory through the supply chain and lifecycle of a product, and a processor configured to execute the instructions [0023].)
receiving training data comprising historical data related to an item; (Tiwari discloses referring to method 300 of FIG. 3, at block 310 the system receives data regarding a product affixed with a RFID tag…as discussed above in relation to FIGS. 1 and 2, a product may be registered in the product expiration management system 140 (FIG. 1), along with corresponding data regarding the product, for example…the specific location of the product within the location of the consumer, the time the product was received, the type of product, and the product expiration, among other data [0037]. The Examiner interprets the location, type and product expiration as inventory information for the item.)
applying the…soon to expire analysis model to determine the item stocked at one or more locations as being likely to remain unused past an expiration date; (Tiwari discloses the product expiration management system 140 may continuously review the data provided to the system for storing, along with data previously stored in the system, for example, to determine if a product is likely to expire prior to being used/consumed, using the machine learning algorithms… the machine learning algorithms may be continuously updated based on data provided on a lifecycle of a product and/or location, and/or consumer. In another aspect of the disclosure, the machine learning algorithms may be implemented to forecast if and when products may expire prior to being used/consumed [0039].)
and providing an output to perform an action to prevent the item from remaining unused past the expiration date. (Tiwari discloses through the use of machine learning algorithms with access to data from a network of consumer locations, the system may make a determination to relocate the product to another location within the same consumer location or to another consumer location. For example, the product expiration management system 140 of FIG. 1 may determine that hospital A has quantities of product X stored in operating room 1 that may be expiring in a month. The alert (block 340 of FIG. 3) may indicate that operating room 2 uses large quantities of product X and that a user should relocate quantities of the product from operating room 1 to operating room 2 [0046].)
Tiwari does not disclose using training data to train the learning model which is met by Breese:
…training a soon to expire analysis model using the training data to generate a trained soon to expire analysis model,… (Breese teaches machine learning/prediction module 110 can obtain data from training data set(s) such as those stored in predictive inventory purchasing database 112. Machine learning/prediction module 110 can create a predictive inventory purchasing model from training data from predictive inventory purchasing database 112 [0027]. [W]herein the training data comprises a set of historical purchasing and sales transactions (Claim 4).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate training the machine learning model using training data as taught by Breese. This modification would make inventory management more efficient by predicting inventory needs (see Breese, ¶ 0004-5).
Tiwari and Breese do not teach the use of a machine learning model and heuristic models which is met by Wook:
wherein the… model comprises a machine learning model and one or more heuristic models each of the one or more heuristic models being associated with a different requirement for a quantity of training data; (Wook teaches a rule-based model based on at least one predefined rule and a machine learning model based on massive sample data may each have unique advantages and disadvantages due to different characteristics. For example, rule-based models can be easily understood by humans and require relatively little data (p. 2, ¶ 0005). Referring to FIG. 14B, the hybrid model 140b may include a first rule-based model 142b, a first machine learning model 144b, and a second machine learning model 146b. Accordingly, in the hybrid models 140a and 140b, the first rule-based models 142a and 142b may process an input in parallel with the machine learning models 144a and 144b. In some embodiments, the hybrid model generates a first output OUT1 and a second output OUT2 generated by the first rule-based models 142a and 142b and the first machine learning models 144a and 144b. It may include both the second rule-based model and the second machine learning-based model to be received (p. 11, ¶ 0001).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate the model comprising both a machine learning model and a heuristic model as taught by Wook. This modification would create a model which minimizes the disadvantages and maximizes the advantages for each model type (see Wook, p. 2, ¶ 0002).
Regarding Claim 20, this claim recites limitations that are substantially similar to those recited in Claim 14 above; thus the same rejection applies. Tiwari further discloses:
A non-transitory computer-readable medium storing instructions,… (Tiwari discloses [a] non-transitory computer-readable recording medium having stored therein a program, which when executed by circuitry of a system, causes the system to:… (Claim 17).)
Regarding Claim 15, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 14 above. Tiwari further discloses:
the action to prevent the item from remaining unused past the expiration date comprises moving, by an inventory controller, at least a portion of the item from the one or more locations to a use location (Tiwari discloses the system may make a determination to relocate the product to another location within the same consumer location or to another consumer location. For example, the product expiration management system 140 of FIG. 1 may determine that hospital A has quantities of product X stored in operating room 1 that may be expiring in a month. The alert (block 340 of FIG. 3) may indicate that operating room 2 uses large quantities of product X and that a user should relocate quantities of the product from operating room 1 to operating room 2 [0046].)
and wherein the historical data comprises inventory changes from a plurality of locations. (Tiwari discloses radio frequency identification (RFID) tags are frequently used to identify and track medical products. For example, RFID tags may be attached to some medical products for purposes of tracking [0005]. The product expiration management system 140 may track one or more products tagged with an RFID tag through the supply chain, for example, from manufacture to expiration of the product… [0024].)
Regarding Claim 16, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 14 above. Tiwari further discloses:
determining the item stocked at the one or more locations as being likely to remain unused past the expiration date comprises comparing a categorical value to a threshold, (Tiwari discloses using previously stored data… the product expiration management system 140 may also take into account the typical usage rate of product X at hospital A, which may indicate that only 50 units of product X are typically used/consumed per month. Based on this analysis, the hospital may have 150 units likely to expire without being used/consumed, and thus the system may generate an alert…indicating that an action should be taken regarding the 150 units of product X likely to expire without being timely used [0039]. The Examiner interprets the usage rate of the product as the categorical value and the historical rate as the threshold.)
wherein the categorical value comprises one or more of: a unit cost, a usage velocity, and a moving speed of the item. (Tiwari discloses using previously stored data… system 140 may also take into account the typical usage rate of product X at hospital A, which may indicate that only 50 units of product X are typically used/consumed per month. Based on this analysis, the hospital may have 150 units likely to expire without being used/consumed, and thus the system may generate an alert, as described in more detail below, indicating that an action should be taken regarding the 150 units of product X…[0039]. The Examiner interprets the usage rate of the product as the categorical value of a usage velocity.
Regarding Claim 19, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 14 above. Tiwari does not disclose the following limitations met by Breese:
training comprises any of a supervised training, an unsupervised training, a reinforced training, a dynamic training, or a hybrid training. (Breese teaches deep learning is a family of machine learning methods based on learning data representations. Learning can be supervised, semi-supervised or unsupervised. The model (e.g. a neural net or a naive Bayes classifier) is trained on the training dataset using a supervised learning method (e.g. gradient descent or stochastic gradient descent) [0029-30].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate training the machine learning model using supervised or unsupervised training as taught by Breese. This modification would make inventory management more efficient by predicting inventory needs (see Breese, ¶ 0004-5).
Claims 7-8 and 18 are rejected under 35 USC § 103 as being unpatentable over Tiwari et al. (US 20210192436 A1), Breese et al. (US 20210365876 A1) and Wook et al. (KR 20210023641 A) in view of Edgar et al. (US 20210201190 A1).
Regarding Claim 7, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 6 above. Tiwari further discloses:
wherein…the soon to expire analysis model (Tiwari discloses the product expiration management system 140 may continuously review the data provided to the system for storing, along with data previously stored in the system, …to determine if a product is likely to expire prior to being used/consumed, using the machine learning algorithms…[0039].)
Tiwari, Breese, and Wook do not teach the following limitations met by Edgar:
training the …model comprises: determining whether the historical data satisfies a first threshold; (Edgar teaches the performance evaluation can also include a data sufficiency analysis that involve evaluating how the performance of the model would change based on increasing the number of training data samples using an entitlement function. In some implementations, the data sufficiency analysis can result in a determination of a predicted amount of additional data samples needed to achieve a defined performance level (e.g., overall and/or for the respective subgroups) [0029].)
and in response to determining that the historical data satisfies the first threshold, executing the training using the machine learning model. (Edgar teaches data driven method to determine when the model has reached a sufficient level of performance for deployment in the field, and when a sufficient amount of data has been used to train and develop the model, based on the results of the performance evaluation [0030].)
Although Edgar teaches determining the sufficiency of the data after training the machine learning model instead of before, this configuration still provides the same improvements of ensuring the machine learning model performs to a preferred degree of accuracy (see Applicant’s disclosure, ¶ 0031). Since each individual element and its function are shown in the prior art, albeit shown in a different order, this practice of determining data sufficiency for training a machine learning model is well known in the art and would be obvious to try. Therefore, it would have been obvious to try, by one of ordinary skill in the art at the time the invention was made, to incorporate determining that a training dataset satisfies a threshold before training the model into the system of Edgar since there are a finite number of identified, predictable solutions to ensure machine learning model accuracy and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
Therefore, it would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate determining a second threshold if the data fails to satisfy the firs threshold as taught by Edgar. This modification would create a method which includes a standardized objective mechanism to assert that an AI algorithm has achieved a sufficient performance level for regulatory approval (see Edgar, ¶ 0004).
Regarding Claim 8, Tiwari, Breese, Wook, and Edgar teach the limitations as seen in the rejection of Claim 7 above. Tiwari further discloses:
wherein…the soon to expire analysis model (Tiwari discloses the product expiration management system 140 may continuously review the data provided to the system for storing, along with data previously stored in the system, …to determine if a product is likely to expire prior to being used/consumed, using the machine learning algorithms…[0039].)
Tiwari and Breese do not teach the following limitations met by Wook:
…executing the training using one of the one or more heuristic models … (Wook teaches the rule-based model 12 is a physical simulator, an emulator that simulates a physical simulator, and an analytical rule that takes at least a portion of the first input…at least one of a heuristic rule and an experience rule (p. 3, ¶ 0003). The Examiner interprets the rule-based model as the heuristic model.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate the model comprising a heuristic model as taught by Wook. This modification would create a model which minimizes the disadvantages and maximizes the advantages for each model type (see Wook, p. 2, ¶ 0002).
Tiwari, Breese, and Wook do not teach the following limitations met by Edgar:
training the soon to expire analysis model comprises: determining whether the historical data satisfies the first threshold; (Edgar teaches data sufficiency analysis can result in a determination of a predicted amount of additional data samples needed to achieve a defined performance level (e.g., overall and/or for the respective subgroups) [0029]. The amount of data needed for cases where the machine learning model 114.sub.1 has not yet achieved the MAAP can be estimated by finding the number of samples…that makes performance meet the MAAP [0046]. The Examiner interprets the number of data samples needed to reach the desired performance as the threshold.)
in response to determining that the historical data fails to satisfy the first threshold, determining whether the historical data satisfies a second threshold; (Edgar teaches based on a determination that a subgroup of the subgroups has a subgroup performance measure that fails to satisfy the threshold subgroup performance measure, the approval regulation component can disapprove the machine learning model as having the acceptable level of performance for deployment [0010]. The respective training sample sizes … corresponds to a different number of training samples, increased by a defined amount [0047].)
and in response to determining that the historical data satisfies the second threshold,… (Edgar teaches the performance evaluation can also include a data sufficiency analysis that involve evaluating how the performance of the model would change based on increasing the number of training data samples using an entitlement function [0029]. The grouping component 118 can employ a minimum number of samples to constitute a subgroup to minimize outliers [0059]. The Examiner interprets the minimum number of samples as the second threshold.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate determining a second threshold if the data fails to satisfy the firs threshold as taught by Edgar. This modification would create a method which includes a standardized objective mechanism to assert that an AI algorithm has achieved a sufficient performance level for regulatory approval (see Edgar, ¶ 0004).
Regarding Claim 18, Tiwari, Breese, Wook, and Edgar teach the limitations as seen in the rejection of Claim 14 above. Tiwari further discloses:
wherein…the soon to expire analysis model (Tiwari discloses the product expiration management system 140 may continuously review the data provided to the system for storing, along with data previously stored in the system, …to determine if a product is likely to expire prior to being used/consumed, using the machine learning algorithms…[0039].)
Tiwari and Breese do not teach the following limitations met by Wook:
…executing the training using one of the one or more heuristic models … (Wook teaches the rule-based model 12 is a physical simulator, an emulator that simulates a physical simulator, and an analytical rule that takes at least a portion of the first input…at least one of a heuristic rule and an experience rule (p. 3, ¶ 0003). The Examiner interprets the rule-based model as the heuristic model.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate the model comprising a heuristic model as taught by Wook. This modification would create a model which minimizes the disadvantages and maximizes the advantages for each model type (see Wook, p. 2, ¶ 0002).
Tiwari, Breese, and Wook do not teach the following limitations met by Edgar:
training the soon to expire analysis model comprises: determining whether the historical data satisfies the first threshold; (Edgar teaches data sufficiency analysis can result in a determination of a predicted amount of additional data samples needed to achieve a defined performance level (e.g., overall and/or for the respective subgroups) [0029]. The amount of data needed for cases where the machine learning model 114.sub.1 has not yet achieved the MAAP can be estimated by finding the number of samples…that makes performance meet the MAAP [0046]. The Examiner interprets the number of data samples needed to reach the desired performance as the threshold.)
in response to determining that the historical data fails to satisfy the first threshold, determining whether the historical data satisfies a second threshold; (Edgar teaches based on a determination that a subgroup of the subgroups has a subgroup performance measure that fails to satisfy the threshold subgroup performance measure, the approval regulation component can disapprove the machine learning model as having the acceptable level of performance for deployment [0010]. The respective training sample sizes … corresponds to a different number of training samples, increased by a defined amount [0047].)
and in response to determining that the historical data satisfies the second threshold,… (Edgar teaches the performance evaluation can also include a data sufficiency analysis that involve evaluating how the performance of the model would change based on increasing the number of training data samples using an entitlement function [0029]. The grouping component 118 can employ a minimum number of samples to constitute a subgroup to minimize outliers [0059]. The Examiner interprets the minimum number of samples as the second threshold.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate determining a second threshold if the data fails to satisfy the firs threshold as taught by Edgar. This modification would create a method which includes a standardized objective mechanism to assert that an AI algorithm has achieved a sufficient performance level for regulatory approval (see Edgar, ¶ 0004).
Claims 9 and 17 are rejected under 35 USC § 103 as being unpatentable over Tiwari et al. (US 20210192436 A1), Breese et al. (US 20210365876 A1) and Wook et al. (KR 20210023641 A) in view of Divine et al. (US 10991461 B2).
Regarding Claim 9, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 6 above. Tiwari and Breese do not teach the following limitations met by Wook:
the one or more heuristic models comprise a model… (Wook teaches the rule-based model 12 is a physical simulator, an emulator that simulates a physical simulator, and an analytical rule that takes at least a portion of the first input…at least one of a heuristic rule and an experience rule (p. 3, ¶ 0003). The Examiner interprets the rule-based model as the heuristic model.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate the model comprising a heuristic model as taught by Wook. This modification would create a model which minimizes the disadvantages and maximizes the advantages for each model type (see Wook, p. 2, ¶ 0002).
Tiwari, Breese, and Wook do not teach the following limitations met by Divine:
…defining an association between an item unit value and an item usage rate, an item value and the item usage rate, or an item unit value and a distance to earliest expiration date. (Divine teaches the resource utilization information comprises supply usage information regarding usage of the one or more medical supplies, and wherein the cost information comprises supply cost information associated with the usage of the one or more medical supplies,… (Claim 1).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate defining an association between an items value and usage as taught by Divine. This modification would improve the clinical and financial performance of a healthcare organization (see Divine, col. 55, lines 9-25).
Regarding Claim 17, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 14 above. Tiwari and Breese do not teach the following limitations met by Wook:
the soon to expire analysis model comprises a machine learning model and one or more heuristic models, wherein the one or more heuristic models comprise… (Wook teaches the hybrid model 10 according to an exemplary embodiment of the present disclosure combines the rule-based model 12 and the machine learning model 14 to provide the rule-based model 12 (p. 3, ¶ 0001). The Examiner interprets the rule-based model as the heuristic model.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate the model comprising both a machine learning model and a heuristic model as taught by Wook. This modification would create a model which minimizes the disadvantages and maximizes the advantages for each model type (see Wook, p. 2, ¶ 0002).
Tiwari, Breese, and Wook do not teach the following limitations met by Divine:
…a model defining an association between an item unit value and an item usage rate, an item value and the item usage rate, or an item unit value and a distance to earliest expiration date. (Divine teaches the resource utilization information comprises supply usage information regarding usage of the one or more medical supplies, and wherein the cost information comprises supply cost information associated with the usage of the one or more medical supplies,… (Claim 1).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate defining an association between an items value and usage as taught by Divine. This modification would improve the clinical and financial performance of a healthcare organization (see Divine, col. 55, lines 9-25).
Claim 11 is rejected under 35 USC § 103 as being unpatentable over Tiwari et al. (US 20210192436 A1), Breese et al. (US 20210365876 A1), and Wook et al. (KR 20210023641 A) in view of Jain et al. (US 20240007414 A1).
Regarding Claim 11, Tiwari, Breese, and Wook teach the limitations as seen in the rejection of Claim 6 above. Tiwari does not disclose the following limitations met by Breese:
training the soon to expire analysis model comprises: obtaining training item data; generating a first soon to expire analysis model including a first processing pipeline wherein the machine learning model receives the training item data (Breese teaches machine learning/prediction module 110 can obtain data from training data set(s) such as those stored in predictive inventory purchasing database 112. Machine learning/prediction module 110 can create a predictive inventory purchasing model from training data from predictive inventory purchasing database 112 [0027]. [W]herein the training data comprises a set of historical purchasing and sales transactions (Claim 4).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate training the machine learning model using training data as taught by Breese. This modification would make inventory management more efficient by predicting inventory needs (see Breese, ¶ 0004-5).
Tiwari and Breese do not teach the following limitations met by Wook:
generating a second soon to expire analysis model including a second processing pipeline wherein the heuristic model receives the training item data and wherein the machine learning model receive, as a second input, at least a portion of a second output from the heuristic model; (Wook teaches referring to FIG. 8, an operation of providing a first input IN1 to the rule-based model 22 may be performed in step S81, and a first output OUT1 is obtained from the rule-based model 22 in step S82. The operation can be performed. Then, in step S83, an operation of providing the first input IN1, the second input IN2, and the first output OUT1 to the machine learning model 24 (p. 7, ¶ 0002). The Examiner interprets this limitation as the output of the heuristic model being used as input for the machine learning model.)
Although Wook teaches the machine learning model receiving input from the heuristic model, it does not teach the heuristic model receiving an input from the machine learning model.
Since each individual element and its function are shown in the prior art, albeit shown in a different order of model inputs, this combine approach to create a hybrid model wherein the output of the first model becomes the input of the second is well known in the art and would be obvious to try. Therefore, it would have been obvious to try, by one of ordinary skill in the art at the time the invention was made, to incorporate the output of the machine learning model feeding the input of the heuristic model into the system of Wook since there are a finite number of identified, predictable solutions to ensure machine learning model accuracy and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate the model comprising both a machine learning model and a heuristic model with one model’s output being the other’s input as taught by Wook. This modification would create a model which minimizes the disadvantages and maximizes the advantages for each model type (see Wook, p. 2, ¶ 0002).
Tiwari, Breese, and Wook do not teach the use of determining resource utilization which is met by Jain:
measuring resource utilization for processing at least a portion of the training item data using the first soon to expire analysis model and second soon to expire analysis model; (Jain teaches the analyzer circuit ID3_428 may determine to either continue modifying resource utilization or to not continue modifying resource utilization (e.g., based on measured performances of the AI models ID3_404a-c) [0347]. The controller circuit ID3_420 collects resource utilization data and generates candidate models based on expected resource utilization data that is likely to achieve a desired performance…. if the system flow of FIG. ID3_6 is a closed-loop process, the controller circuit ID3_420 collects actual resource utilization data and generates subsequent candidate models for the AI models ID3_404a-c in a recurring fashion [0314].)
and selecting one of the first soon to expire analysis model and second soon to expire analysis model as the soon to expire analysis model based on the resource utilization. (Jain teaches the selected candidate model ID3_512a may be selected based on its expected resource utilization data satisfying a desired performance for the AI model ID3_404a [0299].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving inventory information, using a machine learning model to determine that a specific item is likely to remain unused past a target data, and providing an output of an action to perform to prevent the item from remaining unused past the date as disclosed by Tiwari to incorporate measuring resource utilization to select a model as taught by Jain. This modification would provide proper selection of candidate models which is useful in many ways to improve performances of workload execution (see Jain, ¶ 0277).
Response to Arguments
Regarding objections to Claims 1, 11, and 12 Applicant’s amendments have been considered and are persuasive. Therefore, the objections have been withdrawn.
Regarding rejections under 35 USC 101 to Claims 1-20, Applicant’s arguments have been considered but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues the claims are not directed to an abstract idea and are not properly characterized as “organizing human activity” or “managing personal behavior”. The claims are directed to a specific, technical solution and improvement to the technology of training and deploying soon-to-expire analysis models (see Applicant’s Remarks, p. 8-9).
Regarding (a), Examiner respectfully disagrees. Under Step 2A, Prong 1 and 2, the limitations of the claim are analyzed under two classifications (1) the abstract idea of the claim and (2) additional elements of the claim. Examiner asserts that while the processors, machine learning model, and inventory controller of Claim 1 are not a part of the abstract idea and instead are additional elements, the other limitations of claim 1 are identified as being a part of the abstract idea.
Applicant argues the claims are integrated into a practical application under Step 2A, Prong 2. The Federal Circuit has held that claims directed to improving a technological process, even where the improvement is implemented using known computer components, are not abstract. Examiner is reminded of Ex Parte Desjardins. As discussed in the December 2025 Notice, eligibility of claims may be dependent on whether they are directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field. The claims recite a specific, concrete improvement to the technology of training and deploying soon-to-expire analysis models. The specification identifies a specific technical problem: conventional medical inventory management applications perform soon- to-expire analysis based solely on the earliest expiration date of current inventory, a value that "is often incorrect" and that "trigger[s] reactive measures" that "thwart efforts to reduce operational costs and waste" (spec. [0008], [0018]) (see Remarks, p. 9).
Regarding (b), Examiner respectfully disagrees. First, Examiner notes that data used to perform the soon-to-expire analysis has been found to be a part of the abstract idea and is therefore not a part of any technical solution to a technical problem. Furthermore, the specification does not provide any evidence as to how the soon-to-expire analysis model is carried out in a manner such that it solves the identified problems with conventional systems. MPEP 2106.05(f) states when determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following: (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In this case, the claims merely recite that the model is trained, receives information, processes, and outputs the action to prevent an item from remaining unused. The claims do not provide any evidence that the invention utilizes any particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome.
Applicant argues a technical problem is that machine learning models require substantial quantities of historical training data that are frequently unavailable for a given item or stocking location - for example, "short-dated" compounded or repackaged drugs having expiration timeframes of "hours, days, or weeks" ([0017]) and newly stocked or infrequently dispensed items. This problem can be addressed with a particular technical solution: a hybrid model comprising a machine learning model and one or more heuristic models, each associated with a different requirement for the quantity of training data, together with a training process that selects which constituent model to train by testing the available historical data against successive thresholds (spec. [0043]-[0045], FIG. 2A). This arrangement enables accurate soon-to- expire prediction even under data-scarce conditions and avoids expending computational resources training an under-fit machine learning model where insufficient data exists thus providing a concrete improvement in the functioning of the model and the underlying computer system. In particular, [0045] describes that the hybrid model 200 "includes a machine learning model 210, a first heuristic model 220, and a second heuristic model 230, each of which having a different requirement for the quantity of training data," and that "in cases where the quantity of the training data available satisfies a first threshold (e.g., more than 6 months of historical data is available), the hybrid model 200 may be trained by training the machine learning model 210 based on the training data," "[w]here the quantity of the training data fails to satisfy the first threshold but satisfies a second threshold (e.g., less than 6 months but more than 2 months of data is available), the hybrid model 200 may be trained by training the first heuristic model 220," and "[i]n cases where the quantity of the training data fails to satisfy the second threshold ... the hybrid model 200 may be trained by training the second heuristic model 230." Paragraph [0043] further describes training and deploying the heuristic and machine learning models "based on the quantity of the data available for training" (p. 9-10).
Regarding (c), Examiner respectfully disagrees. Firstly, Examiner notes that monitoring the supply and usage of stored medications is not a technical problem. That is because the concept of managing medications’ expirations has existed since long before the advent of computer technology, and thus cannot properly be considered a technological improvement and/or an improvement to the computer itself.
Assuming, arguendo, that the problem was technical in nature, as stated in (a) above, there are no details in the claims that indicate how the algorithm works such that it solves a problem in an unconventional manner. Examiner notes that the heuristic model is directed to the abstract idea, and therefore such limitation cannot provide a practical application. The claim merely recites “apply it” with the machine learning model to output the recommendation. Merely training a model with training data and determining if the data satisfies a threshold amount to generally linking the additional element of the machine learning model to a technological field of use or technical environment because there is nothing in the claims or the specifications that provide an explanation as to how such training is unconventional or different than the standard manner upon which machine learning models are commonly trained.
Regarding rejections under 35 USC 103 to Claims 1-20, Applicant’s arguments have been considered and are persuasive in light of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new rejection has been made, rejecting the independent claim over Tiwari in view of Breese and Wook.
Relevant Art Not Currently Being Applied
The following references are considered pertinent to Applicant’s disclosure but are not currently being applied:
Ward et al. (US 20230101734 A1) teaches the implementation of machine learning models with heuristic models using historical examples to train the machine learning models.
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 OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H Choi can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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