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 Nonfinal Office Action is in response to the Application filed 03/03/2025. Claims 1-20 are pending and considered herein.
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 they recite an abstract idea without significantly more.
Claim 1 recites, wherein the abstract idea is not emboldened:
A system, comprising: one or more processors, coupled with memory, to: receive first input comprising: procedure turnover data of a plurality of medical procedures, wherein the procedure turnover data is determined using three-dimensional data of the plurality of medical procedures; and historical volume data of the plurality of medical procedures; determine, using a procedure volume machine-learning model using the first input, projected case volume of each of a plurality of types of medical procedures of the plurality of medical procedures; receive second input comprising: sterile processing turnaround data of the plurality of medical procedures, wherein the sterile processing turnaround data is determined using three- dimensional data of sterile processing for the plurality of medical procedures; and historical inventory usage data for the plurality of medical procedures; and determine, using an inventory machine-learning model using the second input and the projected case volume of each of the plurality of types of the plurality of medical procedures, a prediction of inventory usage for the plurality of medical procedures.
Independent claim 11 and substantially similar limitations. The claimed invention is broadly directed to the abstract idea of collecting surgical procedure information including three-dimensional data, analyzing the information, and determining predictions related to the surgical procedure and sterilization based on the analyses.
The limitations to “receive first input comprising: procedure turnover data of a plurality of medical procedures, wherein the procedure turnover data is determined using three-dimensional data of the plurality of medical procedures; and historical volume data of the plurality of medical procedures; determine, using the first input, projected case volume of each of a plurality of types of medical procedures of the plurality of medical procedures; receive second input comprising: sterile processing turnaround data of the plurality of medical procedures, wherein the sterile processing turnaround data is determined using three- dimensional data of sterile processing for the plurality of medical procedures; and historical inventory usage data for the plurality of medical procedures; and determine, using the second input and the projected case volume of each of the plurality of types of the plurality of medical procedures, a prediction of inventory usage for the plurality of medical procedures,” as drafted, describe a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as certain methods of organizing human activity. For example, but for the generic recitation of a computer device with processor, memory and use of machine learning models, which are recited at a high level of generality, in the context of this claim, the recitations are an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. These recited limitations fall within certain methods of organizing human activity grouping of abstract ideas because the limitations to access patient and/or surgery data, analyze the data, and generate predictions and other surgical determinations based on the analyses. This is a method of managing interactions between people. Under its broadest reasonable interpretation, the limitations are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people including a patient and surgeon. Therefore, the limitation falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). The mere nominal recitation of a generic computer system, processors, memory, and machine learning models does not remove the claims from the method of organizing human interactions grouping. Thus, the claims recite an abstract idea.
The claims also recited an abstract idea including mental processes. But for the generic reciting of a device, processor, computer, model and surgical hub, nothing in the claims is precluded from being performed in the mind. For example, a physician can manually collect the patient data and analyze the parameters events and determine/predict inventory usage for the patient/surgery based on the analyses. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of being implemented by a generic device, processor, computer and machine learning models. The devices in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying or sending selected information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The limitations appear to monopolize the abstract idea of patient surgical analysis and general turnover and surgical techniques between a physician and his patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. The claim is thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of being implemented by a generic device, processor, computer and machine learning models amounts to no more than mere instructions to apply the exception using a computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea and do not overcome the rejection under 35 U.S.C. §101. Claims 2, 5, 12 and 15 recite multi-modal machine learning model, which is recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the machine learning model does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 3, 4, 6-9, 13, 14 and 16-19 describe in further detail institutions or procedures or sterile processes or inventory usage and further limit the abstract idea Claims 10 and 20 describe an API to replenish inventory, which is recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the API does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Therefore, the claims are not patent eligible.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 5-7, 10-12, 15-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2023/0402167 A1 to Tiwary et al., hereinafter “Tiwary,” in view of U.S. 2016/0354155 A1 to Hodges et al., hereinafter “Hodges” and further in view of U.S. 2010/0161345 A1 to Cain et al., hereinafter “Cain.”
Regarding claim 1, Tiwary discloses A system, comprising: one or more processors, coupled with memory, to: receive first input comprising: procedure turnover data of a plurality of medical procedures, wherein the procedure turnover data is determined using three-dimensional data of the plurality of medical procedures (See Tiwary at least at Abstract; Paras. [0075] (turnover protocols), [0081]-[0083] (“[W]hether an operating room is ready, whether operating room setup has started, whether a medical staff member (e.g., the surgeon, the scrub nurse, the technician) is donning surgical attire (e.g., masks, gloves, caps, gowns), whether operating room equipment is being set up, whether the patient is brought in to the operating room, whether the patient is ready for intubation or anesthesia, whether a timeout is occurring, whether the timeout has occurred, whether the patient is intubated or anesthetized, whether the patient has been prepped and draped for surgery, whether the patient is ready for surgery, whether a surgery site prep is complete, whether a surgery has started, whether the surgery is closing, whether a dressing is applied to the patient, whether the surgery is stopped, whether the patient is brought out of the operating room, whether the operating room is being cleaned, whether the operating room is clean, or any combination thereof […] configured to use the one or more trained machine learning models to detect one or more detected objects or events, which are in turn used to determine the one or more surgical milestones (e.g., surgical time points, surgical phases). The one or more trained machine learning models can include an object detection algorithm, an object tracking algorithm, a video action detection algorithm, an anomaly detection algorithm, or any combination thereof […] The one or more object detection algorithms can comprise machine-learning models such as a 2D convolutional neural network (CNN) or 3D-CNN (e.g., MobileNetV2, ResNet, MobileNetV3, CustomCNN). After the objects are detected, the system may then use one or more object tracking algorithms to track the movement of the detected objects. The one or more object tracking algorithms can comprise any computer-vision algorithms for tracking objects.”), [0085]-[0086], [0089], [0095]-[0097]; Claims 11-18; Figs. 1-5); receive second input comprising: sterile processing turnaround data of the plurality of medical procedures, wherein the sterile processing turnaround data is determined using three- dimensional data of sterile processing for the plurality of medical procedures (See id. at least at Paras. [0075], [0081]-[0086] (“The plurality of predefined milestones can include: whether an operating room is ready, whether operating room setup has started, whether a medical staff member (e.g., the surgeon, the scrub nurse, the technician) is donning surgical attire (e.g., masks, gloves, caps, gowns), whether operating room equipment is being set up, whether the patient is brought in to the operating room, whether the patient is ready for intubation or anesthesia.”), [0095]-[0097] (“The second set of machine-learning model can be configured to detect any activities from which compliance and/or non-compliance to specific requirements in a surgical protocol can be detected. The one or more activities for example include: linen changing on a surgical table; cleaning of the surgical table; wiping of the surgical table; application of a disinfectant; introduction of a surgical equipment; preparation of the surgical equipment; entrance of a person into the operating room; exiting of the person out of the operating room), [0106]-[0107]; Claim 13 (historical usage of inventory); Figs. 1-5); and historical inventory usage data for the plurality of medical procedures (See id.).
Tiwary may not specifically describe but Hodges teaches historical volume data of the plurality of medical procedures (See Hodges at least at Abstract; Paras. [0095], [0111]-[0125] (“Determination of historical treatment plans may be carried out by the informatics system 700 querying its own database of historical data and/or databases belonging to one or more hospital systems 710. Generally, historical treatment plans may include treatment plans that were carried out previously for the current patient or a different patient […] the informatics system 700 may determine all historical treatment plans that match the patient age, patient gender, pathology conditions and region of interest. In some examples, the informatics system 700 may also filter historical data based on treatment outcomes, such that only treatment plans associated with desirable treatment outcomes are considered. In some examples, a nearest neighbor algorithm may be carried out to determine which historical treatment plans are relevant (e.g., to determine treatment plans with similar regions of interest to the currently planned treatment) […] The pathology correlator 726 may also perform a similarity comparison (e.g., using appropriate algorithms, such as computer learning algorithms) against historical image data (typically from the same imaging modality), which may be retrieved from a historical database.”); Claims 15-16; Figs. 2-4, 10; and determine, using a procedure volume machine-learning model using the first input, projected case volume of each of a plurality of types of medical procedures of the plurality of medical procedures (See Hodges at least at Abstract; Paras. [0095] (“The informatics system 700 may carry out data processing on historical data, for example using computer learning algorithms, in order to assist in characterizing and/or guiding planning and/or diagnosis of a current treatment.”), [0111]-[0125] (“Determination of historical treatment plans may be carried out by the informatics system 700 querying its own database of historical data and/or databases belonging to one or more hospital systems 710. Generally, historical treatment plans may include treatment plans that were carried out previously for the current patient or a different patient […] The pathology correlator 726 may also perform a similarity comparison (e.g., using appropriate algorithms, such as computer learning algorithms) against historical image data (typically from the same imaging modality), which may be retrieved from a historical database.”); Claims 15-16; Figs. 2-4, 10; See also Tiwary at least at Abstract; Paras. [0004]-[0005], [0020]-[0023], [0028], [0031], [0075]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tiwary to incorporate the teachings of Hodges and provide volume data and algorithms for medical inventory assessment after surgery. Hodges is directed to a system for health imaging informatics. Incorporating the health imaging informatics of Hodges with the systems and methods for non-compliance detection in a surgical environment as in Tiwary would thereby improve the functionality and applicability of the claimed AI-based inventory prediction and optimization for medical procedures.
The references may not specifically describe but Cain teaches to determine, using an inventory machine-learning model using the second input and the projected case volume of each of the plurality of types of the plurality of medical procedures, a prediction of inventory usage for the plurality of medical procedures (See Cain at least at Abstract; Paras. [0002]-[0003] (inventory management for medical facilities), [0045]-[0046] (“[A] data tracking and aggregation system in which data relating to various components and events is tracked and processed, resulting in powerful data mining and aggregation, and incorporating the use of predictive algorithms and the presentation of processed information in a unique manner […] the system and method described herein will often be described in the context of the acquisition, storage, use, tracking and reporting of medical events and items in various medical procedures.”), [0094]-[0099] (projected levels of needed medical items for medical procedures), [0148]-[0152], [0168]-[0182] (“the system tracks all of the items utilized or consumed in a procedure (in particular, by the data preference form, with any exceptions entered therein), and since the inventory database provides the costs associated with the listed items, the total costs for a procedure can be instantaneously generated, as shown in FIG. 20. The data can be generated from a data preference form that has been modified with variances, or from a procedure tracking form […] In this manner, the system provides the underlying logic to track, maintain, update, use, receive, reorder and report on the entire lift cycle of item and procedures. Data from suppliers, surgical log data, physician preference data, implant data, and inventory details are also integrated into the system to provide a seamless and wide-ranging system to track and integrate disparate types of information into a single system.”), [0200]-[0202], [0207]-[0213], [0219]-[0220] (“[A]n inventory module for tracking the levels of inventory of items, a facility planning module for reserving the use of facilities for procedures, a predictive usage module for predicting the usage of inventoried items for scheduled procedures.”); Claim 2; Figs. 1-7, 15-26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tiwary and Hodges to incorporate the teachings of Cain and provide historic case volume data and analyses and algorithms for medical inventory assessment after surgery. Cain relates to medical data tracking, analysis and aggregation systems for tracking medical items and procedures. Incorporating the health imaging informatics of Hodges with the tracking medical items and procedures of Cain and the systems and methods for non-compliance detection in a surgical environment as in Tiwary would thereby improve the functionality and applicability of the claimed AI-based inventory prediction and optimization for medical procedures.
Regarding claim 2, Tiwary as modified by Hodges and Cain teaches the limitations of claim 1, and Tiwary further discloses wherein the procedure turnover data is determined by a multi- modal machine-learning model using as input the three-dimensional data of the plurality of medical procedures (See Tiwary at least at Abstract; Paras. [0075] (turnover protocols), [0081]-[0083] (“[W]hether an operating room is ready, whether operating room setup has started, whether a medical staff member (e.g., the surgeon, the scrub nurse, the technician) is donning surgical attire (e.g., masks, gloves, caps, gowns), whether operating room equipment is being set up, whether the patient is brought in to the operating room, whether the patient is ready for intubation or anesthesia, whether a timeout is occurring, whether the timeout has occurred, whether the patient is intubated or anesthetized, whether the patient has been prepped and draped for surgery, whether the patient is ready for surgery, whether a surgery site prep is complete, whether a surgery has started, whether the surgery is closing, whether a dressing is applied to the patient, whether the surgery is stopped, whether the patient is brought out of the operating room, whether the operating room is being cleaned, whether the operating room is clean, or any combination thereof […] configured to use the one or more trained machine learning models to detect one or more detected objects or events, which are in turn used to determine the one or more surgical milestones (e.g., surgical time points, surgical phases). The one or more trained machine learning models can include an object detection algorithm, an object tracking algorithm, a video action detection algorithm, an anomaly detection algorithm, or any combination thereof […] The one or more object detection algorithms can comprise machine-learning models such as a 2D convolutional neural network (CNN) or 3D-CNN (e.g., MobileNetV2, ResNet, MobileNetV3, CustomCNN). After the objects are detected, the system may then use one or more object tracking algorithms to track the movement of the detected objects. The one or more object tracking algorithms can comprise any computer-vision algorithms for tracking objects.”), [0085]-[0086], [0089], [0095]-[0097]; Claims 11-18; Figs. 1-5).
Regarding claim 5, Tiwary as modified by Hodges and Cain teaches the limitations of claim 1, and Tiwary further discloses wherein the sterile processing turnaround data is determined by a multi-modal machine-learning model using as input the three-dimensional data of the sterile processing for the plurality of medical procedures. (See id.).
Regarding claim 6, Tiwary as modified by Hodges and Cain teaches the limitations of claim 1, and Cain further teaches wherein the historical inventory usage data includes surgeon equipment usage data (See Cain at least at Abstract; Paras. [0002]-[0003] (inventory management for medical facilities), [0045]-[0046] (“[A] data tracking and aggregation system in which data relating to various components and events is tracked and processed, resulting in powerful data mining and aggregation, and incorporating the use of predictive algorithms and the presentation of processed information in a unique manner […] the system and method described herein will often be described in the context of the acquisition, storage, use, tracking and reporting of medical events and items in various medical procedures.”), [0073]-[0075], [0094]-[0099] (projected levels of needed medical items for medical procedures), [0148]-[0152], [0168]-[0182] (“the system tracks all of the items utilized or consumed in a procedure (in particular, by the data preference form, with any exceptions entered therein), and since the inventory database provides the costs associated with the listed items, the total costs for a procedure can be instantaneously generated, as shown in FIG. 20. The data can be generated from a data preference form that has been modified with variances, or from a procedure tracking form […] In this manner, the system provides the underlying logic to track, maintain, update, use, receive, reorder and report on the entire lift cycle of item and procedures. Data from suppliers, surgical log data, physician preference data, implant data, and inventory details are also integrated into the system to provide a seamless and wide-ranging system to track and integrate disparate types of information into a single system.”), [0200]-[0202], [0207]-[0213], [0219]-[0220] (“[A]n inventory module for tracking the levels of inventory of items, a facility planning module for reserving the use of facilities for procedures, a predictive usage module for predicting the usage of inventoried items for scheduled procedures.”); Claim 2; Figs. 1-7, 15-26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tiwary and Hodges to incorporate the teachings of Cain and provide historic use data and analyses for medical inventory assessment after surgery. Cain relates to medical data tracking, analysis and aggregation systems for tracking medical items and procedures. Incorporating the health imaging informatics of Hodges with the tracking medical items and procedures of Cain and the systems and methods for non-compliance detection in a surgical environment as in Tiwary would thereby improve the functionality and applicability of the claimed AI-based inventory prediction and optimization for medical procedures.
Regarding claim 7, Tiwary as modified by Hodges and Cain teaches the limitations of claim 1, and Cain further teaches the one or more processors to generate additional data based on the prediction of inventory usage for the plurality of medical procedures (See id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tiwary and Hodges to incorporate the teachings of Cain and provide historic use data and analyses for medical inventory assessment after surgery. Cain relates to medical data tracking, analysis and aggregation systems for tracking medical items and procedures. Incorporating the health imaging informatics of Hodges with the tracking medical items and procedures of Cain and the systems and methods for non-compliance detection in a surgical environment as in Tiwary would thereby improve the functionality and applicability of the claimed AI-based inventory prediction and optimization for medical procedures.
Regarding claim 10, Tiwary as modified by Hodges and Cain teaches the limitations of claim 1, and Cain further teaches wherein the additional data includes an API call to replenish an inventory for the plurality of medical procedures (See id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tiwary and Hodges to incorporate the teachings of Cain and provide applications and inventory for medical procedures. Cain relates to medical data tracking, analysis and aggregation systems for tracking medical items and procedures. Incorporating the health imaging informatics of Hodges with the tracking medical items and procedures of Cain and the systems and methods for non-compliance detection in a surgical environment as in Tiwary would thereby improve the functionality and applicability of the claimed AI-based inventory prediction and optimization for medical procedures.
Regarding claims 11, 12 and 15-17 and 20, the claims recite substantially the same limitations as recited in claims 1, 2 and 5-7 and 10, respectively. Therefore, the claims are rejected under the same grounds of rejection and for the same reasoning as applied to claims 1, 2 and 5-7 and 10, above.
Claims 3, 4, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Tiwary, in view of Hodges, in view of Cain and further in view of U.S. 2020/0168334 A1 to Mowery, hereinafter “Mowery.”
Regarding claim 3, Tiwary as modified by Hodges and Cain teaches the limitations of claim 1. The references may not specifically describe but Mowery teaches wherein the historical volume data of the plurality of medical procedures includes first historical volume data of a first institution associated with the procedure turnover data and second historical volume data of a plurality of other institutions different from the first institution (See Mowery at least at Abstract; Paras. [0005]-[0006] (“[H]ealthcare is widely founded on statistics and data. Every procedure done had to be successfully conducted thousands of times in order to be trusted and continuously be used. Surgeons and doctors use this historical data before beginning any procedure. Likewise, deep learning works in the sense that it interprets data representation and mass data to build highly accurate models.”), [0012] (“[A] real time predictive system that optimizes surgical decisions based on historic patient data, real time operating data, cost, prior surgical decisions, and provides an autonomous surgical prediction based on real time and historical data like previous operating procedures captured with video.”), [0023]-[0029], [0048] (historical data between hospitals, geography); Claims 6-10; Figs. 1-3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tiwary, Hodges and Cain to incorporate the teachings of Mowery and provide historical procedural data from surgeries and over years and different institutions. Mowery is directed to systems for surgical decisions using deep learning. Incorporating the surgical procedures estimations and hospital historical data as in Mowery with the health imaging informatics of Hodges, the tracking medical items and procedures of Cain and the systems and methods for non-compliance detection in a surgical environment as in Tiwary would thereby improve the functionality and applicability of the claimed AI-based inventory prediction and optimization for medical procedures.
Regarding claim 4, Tiwary as modified by Hodges, Cain and Mowery teaches the limitations of claim 3, and Mowery further teaches wherein the plurality of other institutions include institutions within a geographical region in which the first institution is located (See id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tiwary, Hodges and Cain to incorporate the teachings of Mowery and provide historical data from different regions/institutions. Mowery is directed to systems for surgical decisions using deep learning. Incorporating the surgical procedures estimations and hospital historical data as in Mowery with the health imaging informatics of Hodges, the tracking medical items and procedures of Cain and the systems and methods for non-compliance detection in a surgical environment as in Tiwary would thereby improve the functionality and applicability of the claimed AI-based inventory prediction and optimization for medical procedures.
Regarding claims 13 and 14, the claims recite substantially the same limitations as recited in claims 3 and 4, respectively. Therefore, the claims are rejected under the same grounds of rejection and for the same reasoning as applied to claims 3 and 4, above.
Claims 8, 9, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Tiwary, in view of Hodges, in view of Cain and further in view of U.S. 2012/0101842 A1 to Montano, hereinafter “Montano.”
Regarding claim 8, Tiwary as modified by Hodges and Cain teaches the limitations of claim 7. The references may not specifically describe but Montano teaches wherein the additional data includes a modification to sterile processing time requirements (See Montano at least at Paras. [0047]-[0050] (“[A] sterilization profile may include a first autoclave sterilization profile, which may automatically generate time, pressure and temperature, number of cycles and cool down instructions to an operator for the given individual sterilization unit or grouping of sterilization units. Similarly, such a sterilization profile may include the step of generating a first heat sterilization profile which may automatically provide time and temperature instructions, and/or a generating a first chemical sterilization profile, which may automatically generate, according to the type of chemical selected, exposure duration, concentration, and decontamination procedure and other special instructions. Still further embodiments may similarly generate a first decontamination profile. Such a profile may provide instruction on the type, time, method and other special considerations of the necessary decontamination procedures needed for an individual instrument, sterilization unit or group of sterilization units), [0056]-[0059]; Claims 14-16; Figs. 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tiwary, Hodges and Cain to incorporate the teachings of Montano and provide modification to sterilization and turnover. Montano is directed to a system for high-efficiency instrument sterilization. Incorporating the sterilization updates and improvements and times of Montano with the health imaging informatics of Hodges, the tracking medical items and procedures of Cain and the systems and methods for non-compliance detection in a surgical environment as in Tiwary would thereby improve the functionality and applicability of the claimed AI-based inventory prediction and optimization for medical procedures.
Regarding claim 9, Tiwary as modified by Hodges and Cain teaches the limitations of claim 7. The references may not specifically describe but Montano teaches wherein the additional data includes a modification to a queue order for sterile processing (See id. at least at Paras. [0047]-[0050] (“[A] sterilization profile may include a first autoclave sterilization profile, which may automatically generate time, pressure and temperature, number of cycles and cool down instructions to an operator for the given individual sterilization unit or grouping of sterilization units. Similarly, such a sterilization profile may include the step of generating a first heat sterilization profile which may automatically provide time and temperature instructions, and/or a generating a first chemical sterilization profile, which may automatically generate, according to the type of chemical selected, exposure duration, concentration, and decontamination procedure and other special instructions. Still further embodiments may similarly generate a first decontamination profile. Such a profile may provide instruction on the type, time, method and other special considerations of the necessary decontamination procedures needed for an individual instrument, sterilization unit or group of sterilization units), [0056]-[0059]; Claims 14-16; Figs. 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tiwary, Hodges and Cain to incorporate the teachings of Montano and provide modification to sterilization and turnover. Montano is directed to a system for high-efficiency instrument sterilization. Incorporating the sterilization updates and improvements and times of Montano with the health imaging informatics of Hodges, the tracking medical items and procedures of Cain and the systems and methods for non-compliance detection in a surgical environment as in Tiwary would thereby improve the functionality and applicability of the claimed AI-based inventory prediction and optimization for medical procedures.
Regarding claims 18 and 19, the claims recite substantially the same limitations as recited in claims 3 and 4, respectively. Therefore, the claims are rejected under the same grounds of rejection and for the same reasoning as applied to claims 3 and 4, above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. 2022/0223268 A1 to Masson et al., U.S. 2020/0377301 A1 to Chilla et al., U.S. 2023/0402166 A1 to Tiwary et al., U.S. 2020/0143195 A1 to Montano.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
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/WILLIAM T. MONTICELLO/Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682