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 .
Priority
This application claims benefit to the U.S. Provisional Application Serial No.
63546960, filed on 11/02/2023, which is hereby incorporated by reference herein in its entirety.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/28/2024, 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 § 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. an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1, 16, and 20 are drawn to methods, a system, and an article of manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recite a system comprising acquire support information comprising: object information about plural objects used in an examination or a procedure, a loading parameter based on at least one of weight, size, or shape of the objects, container information about a container that moves while carrying the objects, and workflow information about a process of the examination or the procedure; and output arrangement information to load the objects into the container, based on the support information.
Independent claim 16 recite a method comprising acquiring support information comprising: object information about plural objects used in an examination or a procedure, a loading parameter based on at least one of weight, size, or shape of the objects, container information about a container that moves while carrying the objects, and workflow information about a process of the examination or the procedure; and outputting arrangement information to load the objects into the container, based on the support information.
Independent claim 20 recites a non-transitory recording medium comprising acquiring support information comprising: object information about plural objects used in an examination or a procedure, a loading parameter based on at least one of weight, size, or shape of the objects, container information about a container that moves while carrying the objects, and workflow information about a process of the examination or the procedure; and outputting arrangement information to load the objects into the container, based on the support information.
These steps amount to certain methods of organizing human activity which includes functions relating to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 1 recites one or more processors comprising hardware. Claim 20 recites a non-transitory recording medium and a computer.
These elements are recited at a high-level of generality such that it amounts to mere instructions to apply the exception because this is an example of applying the abstract idea by use of general-purpose computer which does not integrate the abstract idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component.
Claim 1 recites one or more processors comprising hardware. Claim 20 recites a non-transitory recording medium and a computer.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide generic computer implementation.
For the reasons stated, these claims fail to amount to significantly more
than the abstract idea and are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claim 2 and 17 recite output, as the arrangement information, at least one optimized pattern based on a number of times of movement of the objects at a time of taking out the objects from the container according to the process.
Dependent claim 3 recites wherein the optimized pattern is an arrangement where the number of times of movement is smallest.
Dependent claim 4 and 18 recites output, as the arrangement information, at least one optimized pattern based on a start state being a state of arrangement immediately after the objects are loaded into the container and an end state being a state of arrangement after movement is performed at a predetermined speed from the start state.
Dependent claim 5 recites wherein the at least one optimized pattern is calculated by physical simulation from the start state to the end state.
Dependent claim 6 wherein the at least one optimized pattern is an arrangement that an amount of movement of the objects from the start state to the end state is smaller than a threshold.
Dependent claim 7 recites wherein the container is a cart.
Dependent claim 9 recites wherein the optimized pattern is calculated by an inference model obtained by learning an arrangement pattern when the objects in the object information are loaded into the container in the container information.
Dependent claim 10 and 11 recite wherein the arrangement information is only one arrangement information outputted from the optimized pattern.
Dependent claim 13 and 19 recite acquire identification information of the objects in the container, and determine whether the identification information matches the object information included in the arrangement information.
Dependent claim 14 recite acquire identification information of the objects in the container, and determine whether the identification information matches the object information included in the arrangement information.
Dependent claim 15 recite acquire an arrangement of the objects in the container, and determine whether the arrangement matches the arrangement information.
Each of these steps of the preceding dependent claims 5-6, 10-15, and 17- 24 only serve to further limit or specify the features of independent claim 1 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim.
Dependent claim 8 recites a memory storing a table including a plurality of data sets, each of the plurality of data sets including the support information and at least one optimized pattern, wherein the one or more processors are configured to output, as the arrangement information, the at least one optimized pattern of the data set corresponding to the acquired object information, the acquired loading parameter, the acquired container information, and the acquired workflow information. The limitations of storing a table including a plurality of data sets, each of the plurality of data sets including the support information and at least one optimized pattern, to output, as the arrangement information, the at least one optimized pattern of the data set corresponding to the acquired object information, the acquired loading parameter, the acquired container information, and the acquired workflow information are part of the abstract idea. A memory and one or more processors are additional elements, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Dependent claim 12 recite wherein, when two or more pieces of the container information are acquired, the one or more processors are configured to output the arrangement information for each piece of the container information. The limitations of when two or more pieces of the container information are acquired, to output the arrangement information for each piece of the container information are part of the abstract idea. One or more processors are additional elements, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Dependent claim 16 recite further comprising a sensor configured to acquire the identification information, wherein the one or more processors are configured to be provided with the identification information acquired by the sensor, and identify the object loaded into the container. The acquire the identification information, wherein the to be provided with the identification information acquired and identify the object loaded into the container are part of the abstract idea. The one or more processors and the sensor are additional elements, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 7, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pereira (US 20180361055 A1) in view of Ghosh (US 20240173077 A1).
REGARDING CLAIM 1
Pereira teaches a medical support system comprising: one or more processors comprising hardware, the one or more processors being configured to: acquire support information comprising: ([Para. 0003] a processor)
object information about plural objects used in an examination or a procedure, ([Para. 0060] Systems, methods, devices, and kits for the delivery of fluid in fURS procedures through controlled flow rate and sensor feedback. Exemplary embodiments describe a modular system including a pump which is either user controlled or automated, a ureteroscope device such as, for example, a LithoVue™ scope device with sensors at the tip, and a fluid management system. A fluid management system kit which may comprise any combination of any two or more of an irrigation tubing, a tool with a pressure or temperature sensor, a drainage canister and printed material with one or more of storage information and instructions regarding how to set up irrigation tubing. [Para. 0061] The fluid management system 10 also includes a fluid hanger module 100. An exemplary fluid hanger module 100 may include one or more fluid container supports, such as fluid bag hangers 102, each of which supports one or more fluid bags 104.)
a loading parameter based on at least one of weight, size, or shape of the objects, ([Para. 0061] The fluid bag hangers 102 may receive a variety of sizes of fluid bags 104 such as, for example, 1 liter (L) to 5 L bags. [Para. 0074] the fluid deficit monitoring system 130 monitors the amount of fluid (i.e., saline) in a fluid bag 104 through weight. The weight sensor 132 determines a weight of the fluid bag 104 attached to the hanger module 100 to compare an initial amount of fluid in the fluid bag 104 to a current amount of fluid remaining in the fluid bag 104.)
container information about a container that moves while carrying the objects, ([Para. 0095] The fluid management system 10 may be positioned on a modular cart (i.e. container) 630 including a rolling base 608. The modular cart 630 includes a pole 632 coupled to the side of the modular cart 630 and a flat clean top surface 634 for arranging the separate modular units. In Fig. 39, the display unit 616 may be integrated with the modular cart 630 and coupled to the top of the pole 632 with the fluid bangs 104 hanging from the bottom of the display unit 616 for easy access by either the user or the support staff. Hanging the fluid bags 104 from the display unit 616 positions the bags 104 at a preferred loading height. For example, the saline bags may be positioned at approximately 48 inches from the ground.)
and workflow information about a process of the examination or the procedure; ([Para. 0066] The fluid management system 10 may be user selectable between different modes based on the procedure, patient characteristics, etc. For example, different modes may include—i.e., fURS Mode, BPH Mode, Hysteroscopy Mode, Cystoscopy Mode. Once a mode has been selected by the user, mode parameters such as flow rate, pressure, fluid deficit and temperature are provided to the user via the display screen.)
Pereira does not explicitly teach, however Ghosh teaches
and output arrangement information to load the objects into the container, based on the support information. ([Para. 0055] A machine learning model (e.g., artificial intelligence (AI)-based learning model or algorithm) is provided for suggesting and/or auto-loading a surgery tray with needed surgical instruments for a surgical procedure to reduce surgery time. For example, the machine learning model may be utilized to provide suggestions for a surgery plan, provide suggestions for instrument selection, and/or autoload surgical instruments in an order based on various historical parameters of previously performed surgical procedures. [Para. 0064] After the plan is confirmed and/or edited, the processor 104 may preload the surgical instruments (e.g., from within a surgical instrument depository storing a plurality of surgical instruments) and place the surgical instruments in a surgery tray in an order for the surgeon to use for the surgical procedure.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira and incorporate a smart surgical instrument selection and suggestion system as taught by Ghosh, with the motivation of selecting the optimal one or more surgical instruments or tools to conduct surgical procedures (Ghosh Para. 0002).
REGARDING CLAIM 7
Pereira/ Ghosh teach the medical support system according to claim 1, Pereia further teaches wherein the container is a cart. ([Para. 0095] As shown in FIGS. 39-42, the fluid management system 10 may be positioned on a modular cart 630 including a rolling base 608.)
REGARDING CLAIM 16
Pereira teaches a medical support method comprising: acquiring support information comprising:
object information about plural objects used in an examination or a procedure, ([Para. 0060] Systems, methods, devices, and kits for the delivery of fluid in fURS procedures through controlled flow rate and sensor feedback. Exemplary embodiments describe a modular system including a pump which is either user controlled or automated, a ureteroscope device such as, for example, a LithoVue™ scope device with sensors at the tip, and a fluid management system. A fluid management system kit which may comprise any combination of any two or more of an irrigation tubing, a tool with a pressure or temperature sensor, a drainage canister and printed material with one or more of storage information and instructions regarding how to set up irrigation tubing. [Para. 0061] The fluid management system 10 also includes a fluid hanger module 100. An exemplary fluid hanger module 100 may include one or more fluid container supports, such as fluid bag hangers 102, each of which supports one or more fluid bags 104.)
a loading parameter based on at least one of weight, size, or shape of the objects, ([Para. 0061] The fluid bag hangers 102 may receive a variety of sizes of fluid bags 104 such as, for example, 1 liter (L) to 5 L bags. [Para. 0074] the fluid deficit monitoring system 130 monitors the amount of fluid (i.e., saline) in a fluid bag 104 through weight. The weight sensor 132 determines a weight of the fluid bag 104 attached to the hanger module 100 to compare an initial amount of fluid in the fluid bag 104 to a current amount of fluid remaining in the fluid bag 104.)
container information about a container that moves while carrying the objects,([Para. 0095] The fluid management system 10 may be positioned on a modular cart (i.e. container) 630 including a rolling base 608. The modular cart 630 includes a pole 632 coupled to the side of the modular cart 630 and a flat clean top surface 634 for arranging the separate modular units. In Fig. 39, the display unit 616 may be integrated with the modular cart 630 and coupled to the top of the pole 632 with the fluid bangs 104 hanging from the bottom of the display unit 616 for easy access by either the user or the support staff. Hanging the fluid bags 104 from the display unit 616 positions the bags 104 at a preferred loading height. For example, the saline bags may be positioned at approximately 48 inches from the ground.)
and workflow information about a process of the examination or the procedure; ([Para. 0066] The fluid management system 10 may be user selectable between different modes based on the procedure, patient characteristics, etc. For example, different modes may include—i.e., fURS Mode, BPH Mode, Hysteroscopy Mode, Cystoscopy Mode. Once a mode has been selected by the user, mode parameters such as flow rate, pressure, fluid deficit and temperature are provided to the user via the display screen.)
Pereira does not explicitly teach, however Ghosh teaches
and outputting arrangement information to load the objects into the container, based on the support information. ([Para. 0055] a machine learning model (e.g., artificial intelligence (AI)-based learning model or algorithm) is provided for suggesting and/or auto-loading a surgery tray with needed surgical instruments for a surgical procedure to reduce surgery time. For example, the machine learning model may be utilized to provide suggestions for a surgery plan, provide suggestions for instrument selection, and/or autoload surgical instruments in an order based on various historical parameters of previously performed surgical procedures. [Para. 0064] After the plan is confirmed and/or edited, the processor 104 may preload the surgical instruments (e.g., from within a surgical instrument depository storing a plurality of surgical instruments) and place the surgical instruments in a surgery tray in an order for the surgeon to use for the surgical procedure.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira and incorporate a smart surgical instrument selection and suggestion system as taught by Ghosh, with the motivation of selecting the optimal one or more surgical instruments or tools to conduct surgical procedures (Ghosh Para. 0002).
REGARDING CLAIM 20
Pereira teaches a non-transitory recording medium recording a support program that cause a computer to perform: acquiring support information comprising:
object information about plural objects used in an examination or a procedure, ([Para. 0060] Systems, methods, devices, and kits for the delivery of fluid in fURS procedures through controlled flow rate and sensor feedback. Exemplary embodiments describe a modular system including a pump which is either user controlled or automated, a ureteroscope device such as, for example, a LithoVue™ scope device with sensors at the tip, and a fluid management system. A fluid management system kit which may comprise any combination of any two or more of an irrigation tubing, a tool with a pressure or temperature sensor, a drainage canister and printed material with one or more of storage information and instructions regarding how to set up irrigation tubing. [Para. 0061] The fluid management system 10 also includes a fluid hanger module 100. An exemplary fluid hanger module 100 may include one or more fluid container supports, such as fluid bag hangers 102, each of which supports one or more fluid bags 104.)
a loading parameter based on at least one of weight, size, or shape of the objects, ([Para. 0061] The fluid bag hangers 102 may receive a variety of sizes of fluid bags 104 such as, for example, 1 liter (L) to 5 L bags. [Para. 0074] the fluid deficit monitoring system 130 monitors the amount of fluid (i.e., saline) in a fluid bag 104 through weight. The weight sensor 132 determines a weight of the fluid bag 104 attached to the hanger module 100 to compare an initial amount of fluid in the fluid bag 104 to a current amount of fluid remaining in the fluid bag 104.)
container information about a container that moves while carrying the objects, ([Para. 0095] The fluid management system 10 may be positioned on a modular cart (i.e. container) 630 including a rolling base 608. The modular cart 630 includes a pole 632 coupled to the side of the modular cart 630 and a flat clean top surface 634 for arranging the separate modular units. In Fig. 39, the display unit 616 may be integrated with the modular cart 630 and coupled to the top of the pole 632 with the fluid bangs 104 hanging from the bottom of the display unit 616 for easy access by either the user or the support staff. Hanging the fluid bags 104 from the display unit 616 positions the bags 104 at a preferred loading height. For example, the saline bags may be positioned at approximately 48 inches from the ground.)
and workflow information about a process of the examination or the procedure; ([Para. 0066] The fluid management system 10 may be user selectable between different modes based on the procedure, patient characteristics, etc. For example, different modes may include—i.e., fURS Mode, BPH Mode, Hysteroscopy Mode, Cystoscopy Mode. Once a mode has been selected by the user, mode parameters such as flow rate, pressure, fluid deficit and temperature are provided to the user via the display screen.)
Pereira does not explicitly teach, however Ghosh teaches
and outputting arrangement information to load the objects into the container, based on the support information. ([Para. 0055] a machine learning model (e.g., artificial intelligence (AI)-based learning model or algorithm) is provided for suggesting and/or auto-loading a surgery tray with needed surgical instruments for a surgical procedure to reduce surgery time. For example, the machine learning model may be utilized to provide suggestions for a surgery plan, provide suggestions for instrument selection, and/or autoload surgical instruments in an order based on various historical parameters of previously performed surgical procedures. [Para. 0064] After the plan is confirmed and/or edited, the processor 104 may preload the surgical instruments (e.g., from within a surgical instrument depository storing a plurality of surgical instruments) and place the surgical instruments in a surgery tray in an order for the surgeon to use for the surgical procedure.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira and incorporate a smart surgical instrument selection and suggestion system as taught by Ghosh, with the motivation of selecting the optimal one or more surgical instruments or tools to conduct surgical procedures (Ghosh Para. 0002).
Claim(s) 2-6, 8-15, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pereira (US 20180361055 A1) in view of Ghosh (US 20240173077 A1) in view of Gerstner (US 20250331945 A1).
REGARDING CLAIM 2
Pereira/ Ghosh teach The medical support system according to claim 1, however Gerstner further teaches wherein the one or more processors are configured to output, as the arrangement information, at least one optimized pattern based on a number of times of movement of the objects at a time of taking out the objects from the container according to the process. ([Para. 0041] Storing, by the computing system: (1) a first time that the first particular actual surgical instrument was determined to have been removed from the first actual surgical instrument tray and a second time that the first particular actual surgical instrument was determined to have been returned to the first actual surgical instrument tray; or (2) a duration of time between the first particular actual surgical instrument being determined to have been removed from the first actual surgical instrument tray and the first particular actual surgical instrument determined to have been returned to the first actual surgical instrument tray. [Para. 0198] the smart tool integration functionality of the software 16 may determine if the tray 19 can or should be reconfigured or relocated to improve efficiency for surgeons, for example by suggesting tray arrangements that consolidate frequently-used instruments in to one or more trays, and/or relocating trays on the vertical rack 12, back table, or other setup configuration to ensure that the most-frequently used instruments are also the most easily accessible.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 3
Pereira/ Ghosh/ Gerstner teach the medical support system according to claim 2, Gerstner further teaches teach wherein the optimized pattern is an arrangement where the number of times of movement is smallest. ([Para. 0203] The software 16 can track and analyze movements within the field of view 702 to determine for each instrument, component, or tray: 1) a verified instrument inventory in the trays, 2) that the instruments are in the correct location within the tray, and 3) the frequency of use of each instrument and tray to optimize tray configuration and placement within the vertical rack 12. [Para. 0198] The smart tool integration functionality of the software 16 may determine if the tray 19 can or should be reconfigured or relocated to improve efficiency for surgeons, for example by suggesting tray arrangements that consolidate frequently-used instruments in to one or more trays, and/or relocating trays on the vertical rack 12, back table, or other setup configuration to ensure that the most-frequently used instruments are also the most easily accessible.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 4
Pereira/ Ghosh teach the medical support system according to claim 1, Ghosh further teaches wherein the one or more processors are configured to output, as the arrangement information, at least one optimized pattern based on a start state being a state of arrangement immediately after the objects are loaded into the container and an end state being a state of arrangement after movement is performed at a predetermined speed from the start state. ([Para. 0259] Responsive to determining that the particular instrument has been returned, the computing system may store a duration time that the instrument was in use. The duration of time may be calculated from the removal and return times, or may be determined based on starting a timer when the instrument was removed from a tray and stopping the timer when the instrument is determined to have been returned to the tray. [Para. 0297] The display device presents a recommend instrument and/or tray arrangement. For example, based on the above-discussed usage data, the computing system may perform operations to identify different arrangements of trays and/or instruments for one or more planograms. Such different arrangements may re-order the trays and/or instruments within a planogram to present trays and/or instruments that have been most-heavily used proximate a certain location (e.g., a proximal portion of a shelf assembly, and/or a center portion of the shelf assembly).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 5
Pereira/ Ghosh teach the medical support system according to claim 4, Gerstner further teaches wherein the at least one optimized pattern is calculated by physical simulation from the start state to the end state. ([Para. 0197] The smart tool integration functionality of the software 16 may comprise the use of artificial intelligence (“A.I.”) and/or machine learning in combination with a camera 700 in data communication with a processor (e.g., processor 9802, 9852 described below) that captures the entire surgical instrument inventory setup for a particular procedure within its field of view 702 to increase the efficiency of the surgical tray efficiency system 10 described above by monitoring and tracking actual usage and/or non-usage of the surgical instruments in the trays 19. [Para. 0294] The display device presents an amount of time that instruments satisfying the filtering/selection/searching criteria were in use and/or were in a staging area. For example, the display device may present a number that specifies an amount of time a particular instrument was in use (e.g., not located on a tray) during a surgical procedure, or some graphical indication of such an amount of time (e.g., a bar in a bar graph of times that various instruments were in use). [Para. 0295] The display device presents indications of least and/or most-used instruments that satisfy the filtering/selection/searching criteria. For example, the display device may list all instruments from a tray, planogram, and/or collection of planograms in a ranked order regarding greatest and/or least amount of use, for example, accompanied by numerical listings of amounts of time in use and or quantity of times used. [Para. 0297] The display device presents a recommend instrument and/or tray arrangement. For example, based on the above-discussed usage data, the computing system may perform operations to identify different arrangements of trays and/or instruments for one or more planograms. Such different arrangements may re-order the trays and/or instruments within a planogram to present trays and/or instruments that have been most-heavily used proximate a certain location (e.g., a proximal portion of a shelf assembly, and/or a center portion of the shelf assembly).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 6
Pereira/ Ghosh/ Gerstner teach the medical support system according to claim 5, Gerstner further teaches wherein the at least one optimized pattern is an arrangement that an amount of movement of the objects from the start state to the end state is smaller than a threshold. ([Para. 0237] The computing system may designate an actual tray as of a type specified by a planogram if the recognized surgical instruments on the actual tray depicted in the image match the surgical instruments specified by a type of tray in the planogram by at least a threshold amount (e.g., at least 85% of tools specified by a particular tray type are depicted as on an actual tray in an image). [Para. 0297] The display device presents a recommend instrument and/or tray arrangement. For example, based on the above-discussed usage data, the computing system may perform operations to identify different arrangements of trays and/or instruments for one or more planograms. Such different arrangements may re-order the trays and/or instruments within a planogram to present trays and/or instruments that have been most-heavily used proximate a certain location (e.g., a proximal portion of a shelf assembly, and/or a center portion of the shelf assembly).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 8
Pereira/ Ghosh teach the medical support system according to claim 1, however Gerstner teaches further comprising: a memory storing a table including a plurality of data sets, each of the plurality of data sets including the support information and at least one optimized pattern, wherein the one or more processors are configured to output, as the arrangement information, the at least one optimized pattern of the data set corresponding to the acquired object information, the acquired loading parameter, the acquired container information, and the acquired workflow information. ([Para. 0156] The surgical tray efficiency system 10 is an integrated workflow management system designed to help perioperative staff standardize and improve their processes. While the efficiency gains manifest themselves during the surgical procedure, the groundwork is laid during the setup process. The setup process has two main goals: first, to ensure that all instruments needed for the surgery are present and accounted for, and second, to arrange the various instrument trays according to the planogram functionality such that their location is appropriate and optimized for all stakeholders. [Para. 0240] The computing system detects a tool location and shape. For example, the computing system may identify a portion of an image as representing a single surgical instrument (i.e. acquired object information), analyze a shape of the instrument (including size) (i.e. acquired loading parameter), and determine a tray (i.e. acquired container information) on which the tool is located (e.g., a tray location and/or tray type). [Para. 0297] The display device presents a recommend instrument and/or tray arrangement. For example, based on the above-discussed usage data, the computing system may perform operations to identify different arrangements of trays and/or instruments for one or more planograms. Such different arrangements may re-order the trays and/or instruments within a planogram to present trays and/or instruments that have been most-heavily used proximate a certain location (e.g., a proximal portion of a shelf assembly, and/or a center portion of the shelf assembly).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 9
Pereira/ Ghosh/ Gerstner teach the medical support system according to claim 2, Ghosh teaches wherein the optimized pattern is calculated by an inference model obtained by learning an arrangement pattern when the objects in the object information are loaded into the container in the container information. ([Para. 0055] A machine learning model (e.g., artificial intelligence (AI)-based learning model or algorithm) is provided for suggesting and/or auto-loading a surgery tray with needed surgical instruments for a surgical procedure to reduce surgery time. For example, the machine learning model may be utilized to provide suggestions for a surgery plan, provide suggestions for instrument selection, and/or autoload surgical instruments in an order based on various historical parameters of previously performed surgical procedures.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira and incorporate a smart surgical instrument selection and suggestion system as taught by Ghosh, with the motivation of selecting the optimal one or more surgical instruments or tools to conduct surgical procedures (Ghosh Para. 0002).
REGARDING CLAIM 10
Pereira/ Ghosh/ Gerstner teach the medical support system according to claim 2, Ghosh further teaches wherein the arrangement information is only one arrangement information outputted from the optimized pattern. ([Para. 0004] Receive a set of inputs for a surgical procedure for a patient; determine one or more potential plans for the surgical procedure based at least in part on the set of inputs and a machine learning model; receive a selection of a plan from the one or more potential plans; determine a plurality of surgical instruments corresponding to the plan from the selection; and provide an output that indicates the plurality of surgical instruments to load in a surgical tray.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira and incorporate a smart surgical instrument selection and suggestion system as taught by Ghosh, with the motivation of selecting the optimal one or more surgical instruments or tools to conduct surgical procedures (Ghosh Para. 0002).
REGARDING CLAIM 11
Pereira/ Ghosh/ Gerstner teach the medical support system according to claim 4, Ghosh further teaches wherein the arrangement information is only one arrangement information outputted from the optimized pattern. ([Para. 0004] Receive a set of inputs for a surgical procedure for a patient; determine one or more potential plans for the surgical procedure based at least in part on the set of inputs and a machine learning model; receive a selection of a plan from the one or more potential plans; determine a plurality of surgical instruments corresponding to the plan from the selection; and provide an output that indicates the plurality of surgical instruments to load in a surgical tray.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira and incorporate a smart surgical instrument selection and suggestion system as taught by Ghosh, with the motivation of selecting the optimal one or more surgical instruments or tools to conduct surgical procedures (Ghosh Para. 0002).
REGARDING CLAIM 12
Pereira/ Ghosh teaches the medical support system according to claim 1, however Gerstner teaches wherein, when two or more pieces of the container information are acquired, the one or more processors are configured to output the arrangement information for each piece of the container information. ([Para. 0104] The vertical rack assembly 12 shown by way of example in FIGS. 2-6 includes a first shelf 30, a second shelf 32, a third shelf 34, and a fourth shelf 36, each of which are configured to hold and display at least two standard sized (e.g. 23×11 inches) surgical instrument trays (e.g. “double-wide”). [Para. 0236] The computing system identifies each tray in the image data and their locations. Such tray-identification operations may be performed as part of, separate from, or in conjunction with the one or more computational models described with respect to box 8324. For example, the computing system may perform one or more object recognition processes to identify shapes in an image that represent trays. The computing system may identify a type of each tray (e.g., based on identifying content on a tag attached to each tray, content affixed to each tray such as an etched number, or based on the content of the instruments included on each tray). [Para. 0297] the display device presents a recommend instrument and/or tray arrangement. For example, based on the above-discussed usage data, the computing system may perform operations to identify different arrangements of trays and/or instruments for one or more planograms. Such different arrangements may re-order the trays and/or instruments within a planogram to present trays and/or instruments that have been most-heavily used proximate a certain location (e.g., a proximal portion of a shelf assembly, and/or a center portion of the shelf assembly).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 13
Pereira/ Ghosh teach the medical support system according to claim 1, however Gerstner teaches wherein the one or more processors are configured to: acquire identification information of the objects in the container, and determine whether the identification information matches the object information included in the arrangement information. ([Para. 0103] The vertical rack assembly 12 (i.e. cart) is ergonomically designed to utilize vertical space in the operating room by having a plurality of angled shelves that each support one or more surgical instrument trays. [Para. 0032] Receiving, by the computing system, image data depicting multiple actual surgical instrument trays (i.e. acquire an arrangement of the objects in the container) that are located on one or more surfaces configured to hold surgical instrument trays, the image data depicting actual surgical instruments located on the multiple actual surgical instrument trays; analyzing, by the computing system, the image data to determine an extent to which the multiple actual surgical instrument trays and the actual surgical instruments located on the multiple actual surgical instrument trays match the selected arrangement of surgical instrument trays and the surgical instruments assigned to the selected arrangement of surgical instrument trays. [Para. 0230] the computing system performs the analysis by providing the image data to a computational model that has been trained to identify surgical instruments located on surgical instrument trays. The computational model may be trained based on machine learning processes in which images are applied to the computational model to train the computational model. The images that are applied to the computational model may each be accompanied by a tag that designate one or more features of the respective image, such as a presence of a particular surgical instrument, set of surgical instruments, and/or trays of surgical instruments depicted by the image. The images used to train the computational model may be photographs of real-world surgical instruments, sets of surgical instruments, and/or trays of surgical instruments.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 14
Pereira/ Ghosh teaches the medical support system according to claim 1, however Gerstner teaches wherein the one or more processors are configured to: acquire an arrangement of the objects in the container, and determine whether the arrangement matches the arrangement information. ([Para. 0103] The vertical rack assembly 12 (i.e. cart) is ergonomically designed to utilize vertical space in the operating room by having a plurality of angled shelves that each support one or more surgical instrument trays. [Para. 0032] Receiving, by the computing system, image data depicting multiple actual surgical instrument trays (i.e. acquire an arrangement of the objects in the container) that are located on one or more surfaces configured to hold surgical instrument trays, the image data depicting actual surgical instruments located on the multiple actual surgical instrument trays; analyzing, by the computing system, the image data to determine an extent to which the multiple actual surgical instrument trays and the actual surgical instruments located on the multiple actual surgical instrument trays match the selected arrangement of surgical instrument trays and the surgical instruments assigned to the selected arrangement of surgical instrument trays; and presenting, by the display device, a graphical indication of the extent to which the multiple actual surgical instrument trays and the actual surgical instruments located on the multiple actual surgical instrument trays match the selected arrangement of surgical instrument trays and the surgical instruments assigned to the selected arrangement of surgical instrument trays (i.e. determine whether the arrangement matches the arrangement information).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 15
Pereira/ Ghosh/ Gerstner teaches the medical support system according to claim 13, Gerstner teaches further comprising a sensor configured to acquire the identification information, wherein the one or more processors are configured to be provided with the identification information acquired by the sensor, and identify the object loaded into the container. ([Para. 0103] The vertical rack assembly 12 (i.e. cart) is ergonomically designed to utilize vertical space in the operating room by having a plurality of angled shelves that each support one or more surgical instrument trays. [Para. 0210] A computing system can compare such images to pre-stored images of the surgical instrument, to identify which item located on a tram is the surgical instrument. Alternatively or additionally, the robotic device can include other types of sensors for use in determining or verifying the identity of a surgical instrument. For example, the surgical instrument may include unique identifiers that can be read by infrared sensors or RFID systems.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automated fluid management system as taught by Pereira, a smart surgical instrument selection and suggestion system as taught by Ghosh, and incorporate a surgical tray efficiency system as taught by Gerstner, with the motivation of providing effective organization and use of surgical instrumentation for a given surgical procedure (Gerstner Para. 0001).
REGARDING CLAIM 17
Claim(s) 17 is/are analogous to Claim(s) 2, thus Claim(s) 17 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
REGARDING CLAIM 18
Claim(s) 18 is/are analogous to Claim(s) 4, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4.
REGARDING CLAIM 19
Claim(s) 19 is/are analogous to Claim(s) 13, thus Claim(s) 19 is/are similarly analyzed
and rejected in a manner consistent with the rejection of Claim(s) 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bailey et al (US 20160379504 A1), which discloses a method of setting up an operating room including placing at least one surgical device on at least one surface in the operating room, capturing an image of the at least one surgical device with a camera, comparing actual attributes of the at least one surgical device determined using the image captured by the camera with desired attributes of the at least one surgical device stored in a digital preference storage using a computer system, and issuing instruction information of the at least one surgical device in the operating room, the instruction information being dependent on results of the step of comparing.
Vann et al (US 20160300030 A1), which discloses providing medical equipment support to a hospital physician, the system comprising a remote server system for storing medical equipment information, a number of remote support terminals each including video and audio input and output components for allowing remote medical equipment support representatives to communicate with the hospital physician and provide medical equipment information to the hospital physician, and on-site support portals each including video and audio input and output components for allowing the hospital physician to communicate with the remote medical equipment support representatives and provide context of medical procedure to the medical equipment support representatives in real time.
Toor et al, Optimizing the surgical instrument tray to immediately increase efficiency and lower costs in the operating room, which discloses conducting an observational study to determine if the use of a customized mathematical inventory optimization model would result in a greater reduction in the number of instruments on a surgical tray than a clinician review of the tray.
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/P.K.E./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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