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 .
Notice to Applicant
Claims 1-13 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 8, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of co-pending Application No. 19/207,078.
Claims 1-13 recite substantially similar limitations to claims 1-13 of application 19/207,078. The subject matter in the instant application amounts to claims which are generally similar in scope. Both sets of claims of the instant application and the co-pending ‘078 application recite the same process of executing environment interpretation software, executing profile generation software, and executing object tracking software to perform the claimed functions.
This is a provisional nonstatutory double patenting rejection.
Claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of co-pending Application No. 19/176,708.
Claims 1-13 recite substantially similar limitations to claims 1-13 of application 19/207,078. The subject matter in the instant application amounts to claims which are generally similar in scope. Both sets of claims of the instant application and the co-pending ‘078 application recite the same process of executing environment interpretation software, executing profile generation software, and executing object tracking software to perform the claimed functions.
This is a provisional nonstatutory double patenting rejection.
Claims 1-11 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of co-pending Application No. 19/076,195.
Claims 1-11 recite substantially similar limitations to claims 1-11 of application 19/076,195. The subject matter in the instant application amounts to claims which are generally similar in scope. Both sets of claims of the instant application and the co-pending ‘195 application recite the same process of identifying clinical workflows for a medical device based on a comparison of a patient care performance metric with an expected performance range and generating clinical workflow information based on the identified workflow.
This is a provisional nonstatutory double patenting rejection.
Claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of co-pending Application No. 19/281,581.
Claims 1-13 recite substantially similar limitations to claims 1-13 of application 19/281,581. The subject matter in the instant application amounts to claims which are generally similar in scope. Both sets of claims of the instant application and the co-pending ‘581 application recite the same process of identifying clinical workflows for a medical device based on a comparison of a patient care performance metric with an expected performance range and generating clinical workflow information based on the identified workflow.
This is a provisional nonstatutory double patenting rejection.
Claims 1-11 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of co-pending Application No. 19/033,901.
Claims 1-11 recite substantially similar limitations to claims 1-11 of application 19/033,901. The subject matter in the instant application amounts to claims which are generally similar in scope. Both sets of claims of the instant application and the co-pending ‘901 application recite the same process of identifying clinical workflows for a medical device based on a comparison of a patient care performance metric with an expected performance range and generating clinical workflow information based on the identified workflow.
This is a provisional nonstatutory double patenting rejection.
Claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of co-pending Application No. 19/176,340.
Claims 1-13 recite substantially similar limitations to claims 1-13 of application 19/176,340. The subject matter in the instant application amounts to claims which are generally similar in scope. Both sets of claims of the instant application and the co-pending ‘340 application recite the same process of identifying clinical workflows for a medical device based on a comparison of a patient care performance metric with an expected performance range and generating clinical workflow information based on the identified workflow.
This is a provisional nonstatutory double patenting rejection.
Claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of co-pending Application No. 19/263,080.
Claims 1-13 recite substantially similar limitations to claims 1-13 of application 19/263,080. The subject matter in the instant application amounts to claims which are generally similar in scope. Both sets of claims of the instant application and the co-pending ‘080 application recite the same process of identifying clinical workflows for a medical device based on a comparison of a patient care performance metric with an expected performance range and generating clinical workflow information based on the identified workflow.
This is a provisional nonstatutory double patenting rejection.
Claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of co-pending Application No. 19/207,018.
Claims 1-13 recite substantially similar limitations to claims 1-13 of application 19/207,018. The subject matter in the instant application amounts to claims which are generally similar in scope. Both sets of claims of the instant application and the co-pending ‘018 application recite the same process of identifying clinical workflows for a medical device based on a comparison of a patient care performance metric with an expected performance range and generating clinical workflow information based on the identified workflow.
This is a provisional nonstatutory double patenting rejection.
Claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of co-pending Application No. 19/281,577.
Claims 1-13 recite substantially similar limitations to claims 1-13 of application 19/281,577. The subject matter in the instant application amounts to claims which are generally similar in scope. Both sets of claims of the instant application and the co-pending ‘577 application recite the same process of identifying clinical workflows for a medical device based on a comparison of a patient care performance metric with an expected performance range and generating clinical workflow information based on the identified workflow.
This is a provisional nonstatutory double patenting rejection.
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 1-13 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process
(claims 1-13). Accordingly, claims 1-13 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified server Claim 1 as the claim that represents the claimed invention for analysis.
Claim 1:
A computerized method for processing data of a self-forming communication and control system, the method comprising:
executing, by a processor, environment interpretation software from a first non-transitory memory causing the processor to detect and identify a plurality of medical treatment devices of a medical treatment environment based on at least one of environment signaling of the medical treatment environment and premise messages exchanged with another processor to produce an identified medical treatment device identifier for each medical treatment device of the identified plurality of medical treatment devices,
each medical treatment device of the plurality of medical treatment devices comprising at least one of a physical object within the medical treatment environment when the medical treatment environment includes a physical environment and a virtual object within the medical treatment environment when the medical treatment environment includes a virtual environment, the environment signaling comprising at least one of an unencoded direct electromagnetic emission, an unencoded indirect electromagnetic emission, an encoded electromagnetic emission, an encoded electronic signal, an unencoded mechanical wave, and an encoded mechanical wave,
the premise messages comprising object profile information for each identified medical treatment device, the object profile information comprising one or more of object basics, object deployment information, and object availability information;
executing, by the processor, profile generation software from a second non-transitory memory to facilitate intercommunication between the environment interpretation software and the profile generation software causing the processor to exchange prescriptive information associated with at least some of the identified medical treatment device identifiers with an artificial intelligence (Al) memory, the prescriptive information comprising object learnings based on an interpretation of a plurality of historical patient care observations associated with at least one medical treatment device of the plurality of medical treatment devices and a plurality of other medical treatment devices associated with another medical treatment environment; and
executing, by the processor, object tracking software from a third non-transitory memory to facilitate intercommunication between the profile generation software and the object tracking software causing the processor to exchange further environment signaling for at least some of the plurality of medical treatment devices within the medical treatment environment using the object profile information and at least some of the prescriptive information to produce patient care tracking information in response to clinical workflow information for storage within a digital twin memory,
wherein the patient care tracking information is available to be subsequently recovered from the digital twin memory and utilized to virtually represent patient care provided by at least some of the plurality of medical treatment devices within a virtual representation of the medical treatment environment and to subsequently generate the clinical workflow information.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity under managing personal behaviors of people. The claim elements are directed towards providing rules or instructions to track medical devices in an environment and generate clinical workflow information, which are typically human activities performed by medical equipment managers.
Accordingly, the claim recites at least one abstract idea.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the claims:
processor (1-13); environment interpretation software (1,9-10); first non-transitory memory (1,9-10); plurality of medical treatment devices (1-4,6-7,9,12,13); another processor (1); physical object (1,9); virtual object (1,9); virtual environment (1); unencoded direct electromagnetic emission (1,9); unencoded indirect electromagnetic emission (1,9); encoded electromagnetic emission (1,9); encoded electronic signal (1,9); unencoded mechanical wave (1,9); encoded mechanical wave (1,9); profile generation software (1,3,); second non-transitory memory (1,3); artificial intelligence memory (7-8,16-17,20); object tracking software (1,4,11-13); third non-transitory memory (1); digital twin memory (1,4-5,8,9,12); virtual representation (1); fourth non-transitory memory (2); fifth non-transitory memory (4-5); sixth non-transitory memory (6-8); seventh non-transitory memory (11); distributed ledger (11); hashing data (11); eighth non-transitory memory (12); ninth non-transitory memory (13)
Any computing devices that would be able to perform the method (processor, another processor) and their associated additional elements (software, non-transitory memories) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0030] of Applicant specification recites: “Computing devices include portable computing devices and fixed computing devices. Examples of portable computing devices include an embedded controller, a mesh network node device, a smart sensor, a social networking device, a gaming device, a smart phone, a laptop computer, a tablet computer, a video game controller, and/or any other portable device that includes a computing core. Examples of fixed computing devices includes a personal computer, a computer server, a cable set-top box, a fixed display device, an appliance, and industrial controller, a video game counsel, a home entertainment controller, a critical infrastructure controller, and/or any type of home, office or cloud computing equipment that includes a computing core.” [0148] further recites: “One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules, and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof.” No specific, technical improvements are being made to server devices as a generic computing device is applied to perform the abstract idea of managing equipment and clinical workflows.
Emission signals (unencoded direct electromagnetic emission, unencoded indirect electromagnetic emission, encoded electromagnetic emission, encoded electronic signal, unencoded mechanical wave, encoded mechanical wave) are also taught at a high level of generality. [0020] recites: “The environment signaling 30 includes emission (e.g., direct such as from a light emitting diode (LED), or indirect such as a reflection from another source of emission) of all formats like sound, light, other wireless, solids, liquids, and gasses.” No specific technical improvements are being made to signal technologies as any generic signaling method is applied to perform an insignificant extra-solution activity of transmitting data; MPEP 2106.05(g).
Medical treatment devices (physical object) are also taught at a high level of generality. [0109] recites: “The medical treatment device 300 includes a variety of implementations to monitor and treat human patients within a medical treatment environment (e.g., a hospital, a care facility, a home). The monitoring includes the computing entity 20-1 interpreting environment sensor information 150 (e.g., brain waves, heartbeat electrical impulses, temperature, etc.) from the environment sensor module 14 to broadly produce vital signs of a human patient.” [0110] further recites: “The clinical workflow is more specific to the sequence of tasks and activities performed by healthcare professionals in the direct care of the human patients utilizing the medical treatment devices. Clinical workflow information describes all of the tasks and activities of the clinical workflow, which medical treatment devices are required, what parameters the medical treatment devices shall utilize, what expected performance ranges the medical treatment devices should deliver, which record system shall be utilized, record system formats, and which personnel are required.” No specific technical improvements are being made to signal technologies as any generic medical device is applied to perform the abstract idea of managing said devices.
Digital twins/virtual representations are also taught at a high level of generality. [00] recites: “A first sub-step includes the processor obtaining a portion of the patient care tracking information from the digital twin memory. For example, the processor identifies a first medical treatment device (e.g., medical treatment device 300) to be managed (e.g., after receiving an activation signal that the medical treatment device 300 is now being utilized to treat a patient, a schedule, a list).” No specific technical improvements are being made to digital twin technologies as they are only applied to perform the abstract idea of managing equipment.
Artificial intelligence is also taught at a high level of generality. [0059] recites: “From time to time, the processor of the computing entity 21-N recovers a portion of the plurality of historical object behavior observations from the AI memory and infers the object learnings based on an interpretation of the portion of the plurality of historical object behavior observations as prescriptive information, the object learnings predicting future object behavior of the identified plurality of objects. The prescriptive information includes one or more of object learnings based on an interpretation of a plurality of historical object behavior observations associated with a corresponding plurality of other objects each of the other objects associated with the particular identifier value and, in an embodiment, an evaluation of the object learnings against a standard.” [0127] further recites: “The object learnings providing guidance for future patient care provided by the plurality of medical treatment devices.” No specific technical improvements are being made to AI technologies as any generic AI model is applied to perform an abstract idea of guiding patient care, which is a human activity typically performed by doctors for their patients.
The distributed ledger is also taught at a high level of generality. [0041] recites: “Figure 5B illustrates an example where a single blockchain serves as the object distributed ledger linking a series of blocks of the blockchain, where each block is associated with a different owner (e.g., different owners over time for a particular object represented by a nonfungible token).” [0044] further recites: “In an embodiment, a blockchain of the blockchain-encoded records is utilized to record steps of an asset lifecycle for an asset such as creation, initial and subsequent ownership (e.g., by a controlling entity), deployment, configuration, establishing trust, service-life utilization, and decommissioning. For instance, a new blockchain is created when a new computing system is deployed for a new premise to enjoy the benefits of a digital twin solution. A new block representing a new or transferred asset of the computing system is created by an associated computing entity on behalf of an initial owner. The blockchain is updated when the asset transitions through the lifecycle. The blockchain is updated when control (e.g., ownership) of the asset is changed.” [0139] further recites: “Alternatively, or in addition to, the processor facilitates storage of the prescriptive information in the object distributed ledger as yet another next block as previously discussed such that at least one medical treatment device can utilize the object distributed ledger to enhance the patent care.” Although the specification states that the ledger provides a technological improvement over prior art communication and computing systems ([0028]: “A technological improvement is provided over prior art communication and computing systems associated with data management since only the device possessing control over a token may modify the token as part of such a tightly integrated overall data management process. Only a present trusted device may pass the control to a next trusted device that is part of the overall data management process.”), the claimed invention relies on the conventional use of existing blockchain technology and does not offer any specific, technical improvements to the underlying technology of blockchain, as it is generically applied to perform an abstract improvement in patient care.
Hashing is also taught at a high level of generality. [0047] recites: “Figure 5D further includes computing devices 2-3 (e.g., devices #2 and #3) to facilitate illustration of generation of the blockchain. Each device includes a hash function, a signature function, and storage for a public/private key pair generated by the device.” [0048] further recites: “In an example of operation of the generating of the blockchain, when the device 2 has control of the blockchain and is passing control of the blockchain to the device 3 (e.g., the device 3 is transacting a transfer of content from device 2), the device 2 obtains the device 3 public key from device 3, performs a hash function 2 over the device 3 public key and the transaction 2 to produce a hashing resultant (e.g., preceding transaction to device 2) and performs a signature function 2 over the hashing resultant utilizing a device 2 private key to produce a device 2 signature.” No specific, technical improvements are made to hashing functions as they are only applied to perform conventional blockchain construction.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 2: This claim recites the method further comprising: executing, by the processor, object learning software from a fourth non-transitory memory causing the processor to: interpret other environment signaling for the corresponding plurality of other medical treatment devices associated with the other medical treatment environment to produce other patient care tracking information, store the other patient care tracking information in the Al memory as the plurality of historical patient care observations associated with the corresponding plurality of other medical treatment devices, recover a portion of the plurality of historical patient care observations from the Al memory, and infer the object learnings based on an interpretation of the portion of the plurality of historical patient care observations as the prescriptive information, the object learnings providing guidance for future patient care provided by the plurality of medical treatment devices; which teaches an abstract idea of using artificial intelligence at a high level of generality, such that it is only applied to perform the abstract idea of providing guidance for future patient care, which is a human activity typically performed by doctors.
Claim 3: This claim recites the method further comprising: executing, by the processor, further profile generation software from the second non-transitory memory causing the processor to produce updated object profile information for at least some of the plurality of medical treatment devices based on corresponding identified medical treatment device identifiers and updated prescriptive information associated with a particular identified medical treatment device of the plurality of medical treatment devices within the Al memory, the updated object profile information comprising one or more of updated object basics, updated object deployment information, and updated object availability information, the updated prescriptive information comprising one or more of updated object learnings based on another interpretation of the plurality of historical patient care observations associated with the corresponding plurality of other medical treatment devices associated with the other medical treatment environment each of the other medical treatment devices associated with the other medical treatment environment and an evaluation of the updated object learnings against a standard; which teaches an abstract idea of updating the analyzed data.
Claim 4: This claim recites the method further comprising: executing, by the processor, dashboard software from a fifth non-transitory memory to facilitate intercommunication between the object tracking software and the dashboard software causing the processor to interpret a portion of the patient care tracking information for the plurality of medical treatment devices recovered from the digital twin memory to produce dashboard information, the dashboard information comprising a representation of status of patient care associated with each identified medical treatment device of the plurality of medical treatment devices based on the further environment signaling and in accordance with the object profile information; which teaches the dashboard software at a high level of generality, such that it is only applied by the processor to teach an insignificant extra-solution activity of displaying data.
Claim 5: This claim recites the method further comprising: executing, by the processor, further dashboard software from the fifth non-transitory memory causing the processor to: obtain the portion of the patient care tracking information that corresponds to the further environment signaling for a particular identified medical treatment device recovered from the digital twin memory, and interpret the portion of the patient care tracking information in accordance with the object profile information to produce the dashboard information; which teaches an abstract idea of interpreting information, which is a human activity typically performed by doctors or equipment managers.
Claim 6: This claim recites the method further comprising: executing, by the processor, prescriptive software from a sixth non-transitory memory to facilitate intercommunication between the dashboard software and the prescriptive software causing the processor to process a portion of the dashboard information to produce the prescriptive information within the Al memory, the prescriptive information comprising one or more of an interpretation of the portion of the dashboard information, an evaluation of the portion of the dashboard information against a standard, and adaptive processor-executable instructions for use with the object profile information and the further environment signaling to cause change with regards to the patient care associated with the identified plurality of medical treatment devices within the medical treatment environment; which teaches an abstract idea of causing change with regards to patient care associated with identified treatment devices, which is a human activity typically performed by doctors.
Claim 7: This claim recites the method further comprising: executing, by the processor, further prescriptive software from the sixth non-transitory memory causing the processor to: determine tracking parameters of object tracking of the identified plurality of medical treatment devices based on the object profile information, determine signaling parameters of the further environment signaling based on the identified medical treatment device, and generate the processor-executable instructions based on the tracking parameters and the signaling parameters to facilitate subsequent collection of the further environment signaling associated with the identified medical treatment device to provide the object tracking of the identified plurality of medical treatment devices within the medical treatment environment; which teaches an abstract idea of generating processor-executable instructions in order to provide object tracking, which can be performed by a software engineer to automatically perform the abstract idea of object tracking and management.
Claim 8: This claim recites the method further comprising: executing, by the processor, further prescriptive software from the sixth non-transitory memory causing the processor to: obtain the portion of the dashboard information corresponding to a prescriptive timeframe from the digital twin memory, process the portion of the dashboard information in accordance with the object profile information to produce preliminary prescriptive information, determine a format for the prescriptive information based on the preliminary prescriptive information and an object knowledgebase of the AI memory, interpret the portion of the dashboard information in accordance with the format for the prescriptive information to produce the prescriptive information, and store the prescriptive information within the Al memory; which teaches an abstract idea of interpreting data and producing preliminary prescriptive information, which is a human activity typically performed by doctors. This claim further teaches the processor at a high level of generality such that it is applied to perform an insignificant extra-solution activity of storing data.
Claim 9: This claim recites wherein the processor further executes the environment interpretation software from the first non-transitory memory causing the processor to detect the plurality of medical treatment devices of the medical treatment environment based on the environment signaling of the medical treatment environment to produce the identified plurality of medical treatment devices by: obtaining the environment signaling of the medical treatment environment from an environment sensor module; indicating the physical object as a particular identified medical treatment device when identifying a physical object pattern from at least one of the unencoded direct electromagnetic emission, the unencoded indirect electromagnetic emission, and the unencoded mechanical wave of the environment signaling; and indicating the virtual object as a particular detected object when identifying a virtual object pattern from at least one of the encoded electromagnetic emission, the encoded electronic signal, and the encoded mechanical wave of the environment signaling; which teaches an abstract idea of indicating physical and virtual objects as particular objects, which can be performed by an equipment manager provided with data.
Claim 10: This claim recites wherein the processor further executes the environment interpretation software from the first non-transitory memory causing the processor to: access a portion of the digital twin memory that includes an object knowledgebase based on a particular identified medical treatment device; compare an attribute of detection of the particular identified medical treatment device to the portion of the digital twin memory that includes the object knowledgebase to produce the particular identified medical treatment device; and access the portion of the digital twin memory that includes the object knowledgebase based on the particular identified medical treatment device to produce the object profile information; which teaches an abstract idea of access and comparing sets of data to produce object profile information, which is a human activity typically performed by equipment managers.
Claim 11: This claim recites the method further comprising: executing, by the processor, ledger software from a seventh non-transitory memory to facilitate intercommunication between the object tracking software and the ledger software causing the processor to memorialize the patient care tracking information in an object distributed ledger by: obtaining a portion of the object distributed ledger; hashing a portion of the patient care tracking information utilizing a receiving public key associated with the object distributed ledger to produce a next transaction hash value; encrypting the next transaction hash value utilizing a private key of the processor to produce a next transaction signature; generating a next block of a blockchain of the object distributed ledger to include the portion of the patient care tracking information and the next transaction signature; and causing inclusion of the next block in the object distributed ledger; which teaches data hashing and a distributed ledger at a high level of generality such that it is applied to add security, which does not provide any specific, technical improvements to the underlying blockchain system.
Claim 12: This claim recites the method further comprising: executing, by the processor, object control software from an eighth non-transitory memory to facilitate intercommunication between the object tracking software and the object control software causing the processor to manage the patient care provided by at least some of the plurality of medical treatment devices by: obtaining a portion of the patient care tracking information from the digital twin memory; identifying a historical operational trend for a first medical device of the plurality of medical devices based on the patient care tracking information; detecting a patient care performance metric of the historical operational trend for the first medical device; identifying a new clinical workflow assignment for a second medical device based on a comparison of the patient care performance metric of the historical operational trend for the first medical device compared to an expected performance range; generating the clinical workflow information based on the new clinical workflow assignment for the second medical device; and facilitating communication of the clinical workflow information to the second medical device; which teaches an abstract idea of generating clinical workflows for medical devices, which is a human activity typically performed by doctors or nurses for their patients.
Claim 13: This claim recites the method further comprising: executing, by the processor, Al optimization software from a nineth non-transitory memory to facilitate intercommunication between the object tracking software and the Al optimization software causing the processor to manage the patient care provided by at least some of the plurality of medical treatment devices by: obtaining a portion of recovered prescriptive information associated with a first medical treatment device of the plurality of medical devices from the Al memory; identifying a historical operational trend for the first medical device based on the portion of recovered prescriptive information; detecting a patient care performance metric of the historical operational trend for the first medical device; identifying a new clinical workflow assignment for a second medical device based on a comparison of the patient care performance metric of the historical operational trend for the first medical device compared to an expected performance range and a historical remediation of the portion of recovered prescriptive information that is expected to produce a future patient care performance metric of a future historical operational trend for a combination of the first medical device and the second medical device that is inside of the expected performance range when operating in accordance with the new clinical workflow assignment for the second medical device; generating the clinical workflow information based on the new clinical workflow assignment for the second medical device; and facilitating communication of the clinical workflow information to the first medical device; which teaches an abstract idea of generating clinical workflows for medical devices, which is a human activity typically performed by doctors or nurses for their patients.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as known activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-13 additional limitations which amount to elements that have been recognized as known activities in particular fields, claims 2-13, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-13, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-13 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-10 and 12-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Peterson (US 20190087544).
Regarding claim 1, Peterson teaches a computerized method for processing data of a self-forming communication and control system, the method comprising:
executing, by a processor, environment interpretation software from a first non-transitory memory causing the processor to detect and identify a plurality of medical treatment devices of a medical treatment environment based on at least one of environment signaling of the medical treatment environment and premise messages exchanged with another processor to produce an identified medical treatment device identifier for each medical treatment device of the identified plurality of medical treatment devices ([0044], “the digital twin 130 can also model a space, such as an operating room, surgical center, pre-operative preparation room, post-operative recovery room, etc. By modeling an environment, such as a surgical suite, the environment can be made, safer, more reliable, and/or more productive for patients, healthcare professionals (e.g., surgeons, nurses, anesthesiologists, technicians, etc.). For example, the digital twin 130 can be used for improved instrument and/or surgical item tracking/management, etc.” [0051], “an optical head-mounted display (e.g., Google™ Glass, etc.) can be used to scan and record item such as instruments, instrument trays, disposables, etc., in an operating room, surgical suite, surgical field, etc. As shown in the example of FIG. 3, an optical head-mounted display 300 can include a scanner or other sensor 310 that scans items in its field of view (e.g., scans barcodes, radiofrequency identifiers (RFIDs), visual profile/characteristics, etc.). Item identification, photograph, video feed, etc., can be provided by the scanner 310 to the digital twin 130” [0061], “FIG. 7 illustrates an example operating room monitor 700 including a processor 710, a memory 720, an input 730, an output 740, and a surgical materials digital twin 130. The example input 730 can include a sensor 735, for example. The sensor 735 can monitor items, personnel, activity, etc., in an environment 500, 600 such as an operating room 500.” [0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”). Examiner interprets the Google Glass to be another processor that exchanges premise messages with the processor of the system.
each medical treatment device of the plurality of medical treatment devices comprising at least one of a physical object within the medical treatment environment when the medical treatment environment includes a physical environment and a virtual object within the medical treatment environment when the medical treatment environment includes a virtual environment ([0030], “the digital twin includes a physical object in real space, a digital twin of that physical object that exists in a virtual space, and information linking the physical object with its digital twin” [0045], “a cart, table, and/or other set of surgical tools/instructions is brought into an operating room in preparation for surgery” [0046], “a digital twin 130 can be used to model the cart and associated items.”),
the environment signaling comprising at least one of an unencoded direct electromagnetic emission, an unencoded indirect electromagnetic emission, an encoded electromagnetic emission, an encoded electronic signal, an unencoded mechanical wave, and an encoded mechanical wave, the premise messages comprising object profile information for each identified medical treatment device ([0051], “FIG. 3, an optical head-mounted display 300 can include a scanner or other sensor 310 that scans items in its field of view (e.g., scans barcodes, radiofrequency identifiers (RFIDs), visual profile/characteristics, etc.).”). [0021] of Applicant specification notes that an “unencoded direct electromagnetic emission includes a light source or radio frequency”.
the object profile information comprising one or more of object basics, object deployment information, and object availability information ([0081], “At block 1104, the update is processed to determine its impact on the modeled preference card of the digital twin 130. For example, a preference card can provide a logical set of instructions for item and personnel positioning for a surgical procedure, equipment and/or other supplies to be used in the surgical procedure,…, etc.” [0083], “At block 1106, a user, application, device, etc., is notified of the update. For example, a message regarding the update and an indication of the impact of the update on the modeled preference card of the digital twin 130 are generated and provided to the user (e.g., a surgeon, nurse, other healthcare practitioner, administrator, supplier, etc.), application (e.g., scheduling application, ordering/inventory management application, radiology information system, practice management application, electronic medical record application, etc.), device (e.g., cart tablet 410, optical device 300, etc.), etc.”). Examiner interprets the functions of an ordering/inventory management application to encompass object availability information, as objects that are not available would require ordering.
executing, by the processor, profile generation software from a second non-transitory memory to facilitate intercommunication between the environment interpretation software and the profile generation software causing the processor to exchange prescriptive information associated with at least some of the identified medical treatment device identifiers with an artificial intelligence (Al) memory ([0094], “Machine learning techniques, whether deep learning networks or other experiential/observational learning system, can be used to model information in the digital twin 130” [0105], “Deep learning machines can provide computer aided detection support to improve item identification, relevance evaluation, and tracking, for example.” [0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”),
the prescriptive information comprising object learnings based on an interpretation of a plurality of historical patient care observations associated with at least one medical treatment device of the plurality of medical treatment devices and a plurality of other medical treatment devices associated with another medical treatment environment ([0070], “matching pre-op data, procedure data, post-op data, procedure guidelines, patient history, practitioner preferences, and the digital twin 130 can identify potential problems for a procedure, item tracking, and/or post-procedure recovery and develop or enhance smart protocols for recovery crafted for the particular procedure, practitioner, facility, and/or patient, for example. The digital twin 130 continues to learn and improve as it receives and models feedback throughout the pre-procedure, during procedure, and post-procedure process including information regarding items used, items unused, items left, items missing, items broken, etc.”); and
executing, by the processor, object tracking software from a third non-transitory memory to facilitate intercommunication between the profile generation software and the object tracking software causing the processor to exchange further environment signaling for at least some of the plurality of medical treatment devices within the medical treatment environment using the object profile information and at least some of the prescriptive information to produce patient care tracking information in response to clinical workflow information for storage within a digital twin memory ([0044], “the digital twin 130 can also model a space, such as an operating room, surgical center, pre-operative preparation room, post-operative recovery room, etc... For example, the digital twin 130 can be used for improved instrument and/or surgical item tracking/management, etc.” [0047], “a device, such as an optical head-mounted display (e.g., Google Glass, etc.,) can be used with augmented reality to identify and quantify items (e.g., instruments, products, etc.) in the surgical field, operating room, etc... The device can be used to pull in scanner details from all participants in a surgery, for example, modeled via the digital twin 130 and verified according to equipment list, surgical protocol, personnel preferences, etc.” [0070], “matching pre-op data, procedure data, post-op data, procedure guidelines, patient history, practitioner preferences, and the digital twin 130 can identify potential problems for a procedure, item tracking, and/or post-procedure recovery” [0153], “Clinical workflows are typically defined to include one or more steps or actions to be taken in response to one or more events and/or according to a schedule… The defined clinical workflows may include manual actions or steps to be taken by, for example, an administrator or practitioner, electronic actions or steps to be taken by a system or device, and/or a combination of manual and electronic action(s) or step(s).” [0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”),
wherein the patient care tracking information is available to be subsequently recovered from the digital twin memory and utilized to virtually represent patient care provided by at least some of the plurality of medical treatment devices within a virtual representation of the medical treatment environment and to subsequently generate the clinical workflow information ([0073], “At block 906, the procedure is modeled for the patient using the digital twin 130. For example, based on the identified procedure, the digital twin 130 can model the procedure to facilitate practice for healthcare practitioners to be involved in the procedure, predict staffing and care team make-up associated with the procedure, improve team efficiency, improve patient preparedness, etc.” [0074], “At block 910, the digital twin 130 is updated based on the monitored procedure execution. For example, the object position, time, state, condition, and/or other aspect captured by the sensor 735, optics 300, tablet 410, etc., is provided via the input 730 to be modeled by the digital twin 130… For example, the digital twin 130 can include a plurality of models or twins focusing on particular aspects of the environment 500, 600 such as surgical instruments, disposables/implants, patient, surgeon, equipment, etc. Alternatively or in addition, the digital twin 130 can model the overall environment 500, 600.”).
Regarding claim 2, Peterson teaches the method of claim 1, as described above. Peterson further teaches the method further comprising:
executing, by the processor, object learning software from a fourth non-transitory memory ([0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”) causing the processor to:
interpret other environment signaling for the corresponding plurality of other medical treatment devices associated with the other medical treatment environment to produce other patient care tracking information ([0070], “matching pre-op data, procedure data, post-op data, procedure guidelines, patient history, practitioner preferences, and the digital twin 130 can identify potential problems for a procedure, item tracking, and/or post-procedure recovery and develop or enhance smart protocols for recovery crafted for the particular procedure, practitioner, facility, and/or patient, for example.” [0044], “the digital twin 130 can also model a space, such as an operating room, surgical center, pre-operative preparation room, post-operative recovery room, etc.” [0042], “the digital twin environment 135 can be divided into multiple virtual spaces 150-154.”),
store the other patient care tracking information in the Al memory as the plurality of historical patient care observations associated with the corresponding plurality of other medical treatment devices ([0062], “Object(s) detected by the sensor 735 can be provided as input 730 to be stored in memory 720 and/or processed by the processor 710, for example.” [0069], “The digital twin 130 can be stored at the monitor 700, healthcare facility 810, and/or health cloud 820, for example.” [0070], “The digital twin 130 continues to learn and improve as it receives and models feedback throughout the pre-procedure, during procedure, and post-procedure process including information regarding items used, items unused, items left, items missing, items broken, etc.” [0071], “improved modeling of a procedure via the digital twin 130 can reduce or avoid post-op complications and/or follow-up visits.” [0094], “Machine learning techniques, whether deep learning networks or other experiential/observational learning system, can be used to model information in the digital twin 130 and/or leverage the digital twin 130 to analyze and/or predict an outcome of a procedure”),
recover a portion of the plurality of historical patient care observations from the Al memory ([0041], “The digital twin 130 can also be interrogated or queried in the digital twin environment 135 to retrieve and/or analyze current information 140, past history, etc.”), and
infer the object learnings based on an interpretation of the portion of the plurality of historical patient care observations as the prescriptive information, the object learnings providing guidance for future patient care provided by the plurality of medical treatment devices ([0070], “The digital twin 130 continues to learn and improve as it receives and models feedback throughout the pre-procedure, during procedure, and post-procedure process including information regarding items used, items unused, items left, items missing, items broken, etc.” [0071], “improved modeling of a procedure via the digital twin 130 can reduce or avoid post-op complications and/or follow-up visits… Instruments and/or other equipment used in procedures can be modeled, tracked, etc., with respect to the patient and the patient's procedure via the digital twin 130, for example.”).
Regarding claim 3, Peterson teaches the method of claim 1, as described above. Peterson further teaches the method further comprising:
executing, by the processor, further profile generation software from the second non-transitory memory causing the processor to produce updated object profile information for at least some of the plurality of medical treatment devices based on corresponding identified medical treatment device identifiers and updated prescriptive information associated with a particular identified medical treatment device of the plurality of medical treatment devices within the Al memory, the updated object profile information comprising one or more of updated object basics, updated object deployment information, and updated object availability information, the updated prescriptive information comprising one or more of updated object learnings based on another interpretation of the plurality of historical patient care observations associated with the corresponding plurality of other medical treatment devices associated with the other medical treatment environment each of the other medical treatment devices associated with the other medical treatment environment ([0074], “At block 910, the digital twin 130 is updated based on the monitored procedure execution. For example, the object position, time, state, condition, and/or other aspect captured by the sensor 735, optics 300, tablet 410, etc., is provided via the input 730 to be modeled by the digital twin 130. A new model can be created and/or an existing model can be updated using the information.”)and
an evaluation of the updated object learnings against a standard ([0038], “The digital twin 130 can also be used for comparison (e.g., to the patient/protocol/item 110, to a “normal”, standard, or reference patient, set of clinical criteria/symptoms, best practices, protocol steps, etc.). In certain examples, the digital twin 130 of the patient/protocol/item 110 can be used to measure and visualize an ideal or “gold standard” value state for that patient/protocol/item, a margin for error or standard deviation around that value (e.g., positive and/or negative deviation from the gold standard value, etc.), an actual value, a trend of actual values, etc.”).
Regarding claim 4, Peterson teaches the method of claim 1, as described above. Peterson further teaches the method further comprising:
executing, by the processor, dashboard software from a fifth non-transitory memory to facilitate intercommunication between the object tracking software and the dashboard software causing the processor to interpret a portion of the patient care tracking information for the plurality of medical treatment devices recovered from the digital twin memory to produce dashboard information ([0058], “the device 300 and/or 410 can provide a display window including information regarding instruments, protocol actions, implants, items, etc.” [0077], “FIG. 10, information from the digital twin 130 can be provided via augmented reality (AR) such as via the glasses 300 to a user, such as a surgeon, etc., in the operating room… One or more aspects of the example AR visualization 1000 demonstrate the features and functionalities of systems 100-800 (and additional systems described herein) with respect to equipment/supplies assessment and employee assessment, for example.” [0116], “Certain examples provide one or more dashboards for specific sets of patients and/or practitioners, such as surgeons, surgical technicians, nurses, assistants, radiologists, administrators, etc.” [0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”),
the dashboard information comprising a representation of status of patient care associated with each identified medical treatment device of the plurality of medical treatment devices based on the further environment signaling and in accordance with the object profile information ([0079], “The example AR visualization 1000 further includes overlay data including information associated with various supplies, equipment and people (e.g., the physicians and the patient) included in the operating room 500 such as determined by the sensor 310, for example. Example information represented in the overlay data includes utilization and performance information associated with the various supplies, equipment and people, that have been determined to be relevant to the context of the user 1002.” [0071], “Instruments and/or other equipment used in procedures can be modeled, tracked, etc., with respect to the patient and the patient's procedure via the digital twin 130, for example.”).
Regarding claim 5, Peterson teaches the method of claims 1 and 4, as described above. Peterson further teaches the method further comprising:
executing, by the processor, further dashboard software from the fifth non-transitory memory ([0116], “Certain examples provide one or more dashboards for specific sets of patients and/or practitioners, such as surgeons, surgical technicians, nurses, assistants, radiologists, administrators, etc.” [0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”) causing the processor to:
obtain the portion of the patient care tracking information that corresponds to the further environment signaling for a particular identified medical treatment device recovered from the digital twin memory, and interpret the portion of the patient care tracking information in accordance with the object profile information to produce the dashboard information ([0058], “the device 300 and/or 410 can provide a display window including information regarding instruments, protocol actions, implants, items, etc.” [0041], “The digital twin 130 can also be interrogated or queried in the digital twin environment 135 to retrieve and/or analyze current information 140, past history, etc.” [0071], “Instruments and/or other equipment used in procedures can be modeled, tracked, etc., with respect to the patient and the patient's procedure via the digital twin 130, for example.”).
Regarding claim 6, Peterson teaches the method of claims 1 and 4, as described above. Peterson further teaches the method further comprising:
executing, by the processor, prescriptive software from a sixth non-transitory memory to facilitate intercommunication between the dashboard software and the prescriptive software causing the processor to process a portion of the dashboard information to produce the prescriptive information within the Al memory ([0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”),
the prescriptive information comprising one or more of an interpretation of the portion of the dashboard information ([0122], “Example output 1520 can provide a display generated by processor 1530 for visual illustration on a monitor or the like.” [0123], “example processor 1530 can take object detection information provided by the sensor 310, 735 via input 1510 with respect to items in the surgical field 520 and can generate a report and/or other guidance regarding the items and protocol adherence via the output 1520.”). [0027] of Applicant specification notes that “prescriptive information includes one or more of an interpretation of a portion of the dashboard information (e.g., a summary), an evaluation of some of the dashboard information vs a standard (e.g., achieving goals), and advice and/or instructions”.
an evaluation of the portion of the dashboard information against a standard ([0038], “The digital twin 130 can also be used for comparison (e.g., to the patient/protocol/item 110, to a “normal”, standard, or reference patient, set of clinical criteria/symptoms, best practices, protocol steps, etc.).”), and
adaptive processor-executable instructions for use with the object profile information and the further environment signaling to cause change with regards to the patient care associated with the identified plurality of medical treatment devices within the medical treatment environment ([0087], “At block 1304, the scanned item is evaluated to determine whether it is included in a list or set of items for the procedure for the patient (e.g., on the preference card 1200 and/or otherwise included in the protocol and/or best practices for the procedure, etc.). At block 1306, if the item is not on the list for the particular patient's procedure, then a warning is generated and logged to indicate that the item might be in the wrong location.”). Examiner interprets providing a warning that a necessary item is missing to encompass directing a change with regards to patient care, as the item would have to be acquired for further patient care.
Regarding claim 7, Peterson teaches the method of claims 1, 4, and 6, as described above. Peterson further teaches the method further comprising:
executing, by the processor, further prescriptive software from the sixth non-transitory memory ([0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”) causing the processor to:
determine tracking parameters of object tracking of the identified plurality of medical treatment devices based on the object profile information ([0071], “improved modeling of a procedure via the digital twin 130 can reduce or avoid post-op complications and/or follow-up visits. Instead, preferences, reminders, alerts, and/or other instructions, as well as likely outcomes, can be provided via the digital twin 130.”),
determine signaling parameters of the further environment signaling based on the identified medical treatment device ([0047], “The device can be used to pull in scanner details from all participants in a surgery, for example, modeled via the digital twin 130 and verified according to equipment list, surgical protocol, personnel preferences, etc.” [0074], “At block 910, the digital twin 130 is updated based on the monitored procedure execution. For example, the object position, time, state, condition, and/or other aspect captured by the sensor 735, optics 300, tablet 410, etc., is provided via the input 730 to be modeled by the digital twin 130. A new model can be created and/or an existing model can be updated using the information.”), and
generate the processor-executable instructions based on the tracking parameters and the signaling parameters to facilitate subsequent collection of the further environment signaling associated with the identified medical treatment device to provide the object tracking of the identified plurality of medical treatment devices within the medical treatment environment ([0071], “improved modeling of a procedure via the digital twin 130 can reduce or avoid post-op complications and/or follow-up visits. Instead, preferences, reminders, alerts, and/or other instructions, as well as likely outcomes, can be provided via the digital twin 130. Through digital twin 130 modeling, simulation, prediction, etc., information can be communicated to practitioner, patient, supplier, insurance company, administrator, etc., to improve adherence to pre- and post-op instructions and outcomes, for example... Instruments and/or other equipment used in procedures can be modeled, tracked, etc., with respect to the patient and the patient's procedure via the digital twin 130, for example. Alternatively or in addition, parameters, settings, and/or other configuration information can be pre-determined for the provider, patient, and a particular procedure based on modeling via the digital twin 130, for example.” [0074], “At block 910, the digital twin 130 is updated based on the monitored procedure execution.”). Examiner interprets the parameters, settings and other configuration information to encompass process-executable instructions.
Regarding claim 8, Peterson teaches the method of claims 1, 4, and 6, as described above. Peterson further teaches the method further comprising:
executing, by the processor, further prescriptive software from the sixth non-transitory memory ([0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”) causing the processor to:
obtain the portion of the dashboard information corresponding to a prescriptive timeframe from the digital twin memory ([0116], “provide an intuitive user interface to help minimize end-user training. Certain examples facilitate user-initiated launching of third-party applications directly from a desktop interface to help provide a seamless workflow by sharing user, patient, and/or other contexts. Certain examples provide real-time (or at least substantially real time assuming some system delay) patient data from one or more information technology (IT) systems and facilitate comparison(s) against evidence-based best practices. Certain examples provide one or more dashboards for specific sets of patients and/or practitioners” [0070], “matching pre-op data, procedure data, post-op data, procedure guidelines, patient history, practitioner preferences, and the digital twin 130 can identify potential problems for a procedure, item tracking, and/or post-procedure recovery and develop or enhance smart protocols for recovery crafted for the particular procedure, practitioner, facility, and/or patient”). Examiner interprets the duration of a surgical procedure to encompass a prescriptive timeframe.
process the portion of the dashboard information in accordance with the object profile information to produce preliminary prescriptive information ([0070], “matching pre-op data, procedure data, post-op data, procedure guidelines, patient history, practitioner preferences, and the digital twin 130 can identify potential problems for a procedure, item tracking, and/or post-procedure recovery and develop or enhance smart protocols for recovery crafted for the particular procedure, practitioner, facility, and/or patient”),
determine a format for the prescriptive information based on the preliminary prescriptive information and an object knowledgebase of the AI memory, interpret the portion of the dashboard information in accordance with the format for the prescriptive information to produce the prescriptive information, and store the prescriptive information within the Al memory ([0070], “The digital twin 130 continues to learn and improve as it receives and models feedback throughout the pre-procedure, during procedure, and post-procedure process including information regarding items used, items unused, items left, items missing, items broken, etc. ” [0094], “Machine learning techniques, whether deep learning networks or other experiential/observational learning system, can be used to model information in the digital twin 130 and/or leverage the digital twin 130 to analyze and/or predict an outcome of a procedure” [0134], “If necessary (e.g., when different formats of the received information are incompatible), interface unit 1610 translates or reformats (e.g., into Structured Query Language (“SQL”) or standard text) the medical information, such as medical reports, to be properly stored at data center 1612.”).
Regarding claim 9, Peterson teaches the method of claim 1, as described above. Peterson further teaches wherein the processor further executes the environment interpretation software from the first non-transitory memory causing the processor to detect the plurality of medical treatment devices of the medical treatment environment based on the environment signaling of the medical treatment environment to produce the identified plurality of medical treatment devices by:
obtaining the environment signaling of the medical treatment environment from an environment sensor module ([0051], “an optical head-mounted display (e.g., Google™ Glass, etc.) can be used to scan and record item such as instruments, instrument trays, disposables, etc., in an operating room, surgical suite, surgical field, etc.”);
indicating the physical object as a particular identified medical treatment device when identifying a physical object pattern from at least one of the unencoded direct electromagnetic emission, the unencoded indirect electromagnetic emission, and the unencoded mechanical wave of the environment signaling ([0051], “FIG. 3, an optical head-mounted display 300 can include a scanner or other sensor 310 that scans items in its field of view (e.g., scans barcodes, radiofrequency identifiers (RFIDs), visual profile/characteristics, etc.). Item identification, photograph, video feed, etc., can be provided by the scanner 310 to the digital twin 130, for example. The scanner 310 and/or the digital twin 130 can identify and track items within range of the scanner 310, for example.”). Examiner interprets tracked items to be particular identified medical treatment devices.
and indicating the virtual object as a particular detected object when identifying a virtual object pattern from at least one of the encoded electromagnetic emission, the encoded electronic signal, and the encoded mechanical wave of the environment signaling ([0051], “The digital twin 130 can then model the viewed environment and/or objects in the viewed environment based at least in part on input from the scanner 310, for example.” [0162], “The interface circuit 1820 may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface.”). [0021] of Applicant specification notes that “encoded electronic signal includes a signal on a wire that is modulated with information (e.g., an ethernet cable communication data packets).”
Regarding claim 10, Peterson teaches the method of claim 1, as described above. Peterson further teaches wherein the processor further executes the environment interpretation software from the first non-transitory memory causing the processor to:
access a portion of the digital twin memory that includes an object knowledgebase based on a particular identified medical treatment device ([0124], “Example memory 1540 can include a relational database, an object-oriented database,…, etc… Example memory 1540 can store data and/or instructions for access by the processor 1530 (e.g., including the digital twin 130).”);
compare an attribute of detection of the particular identified medical treatment device to the portion of the digital twin memory that includes the object knowledgebase to produce the particular identified medical treatment device ([0030], “The digital twin is linked with the physical system through the lifecycle of the physical system. In certain examples, the digital twin includes a physical object in real space, a digital twin of that physical object that exists in a virtual space, and information linking the physical object with its digital twin.”); and
access the portion of the digital twin memory that includes the object knowledgebase based on the particular identified medical treatment device to produce the object profile information ([0124], “Example memory 1540 can include a relational database, an object-oriented database,…, etc… Example memory 1540 can store data and/or instructions for access by the processor 1530 (e.g., including the digital twin 130).” [0037], “Rather than reading a report, a healthcare practitioner can view and simulate with the digital twin 130 to evaluate a condition, progression, possible treatment, etc., for the patient/protocol/item 110. In certain examples, features, conditions, trends, indicators, traits, etc., can be tagged and/or otherwise labeled in the digital twin 130 to allow the practitioner to quickly and easily view designated parameters, values, trends, alerts, etc.”). Examiner interprets features of an item to encompass the object profile information.
Regarding claim 12, Peterson teaches the method of claim 1, as described above. Peterson further teaches the method further comprising:
executing, by the processor, object control software from an eighth non-transitory memory to facilitate intercommunication between the object tracking software and the object control software causing the processor to manage the patient care provided by at least some of the plurality of medical treatment devices by ([0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”):
obtaining a portion of the patient care tracking information from the digital twin memory ([0041], “The digital twin 130 can also be interrogated or queried in the digital twin environment 135 to retrieve and/or analyze current information 140, past history, etc.” [0070], “matching pre-op data, procedure data, post-op data, procedure guidelines, patient history, practitioner preferences, and the digital twin 130 can identify potential problems for a procedure, item tracking, and/or post-procedure recovery and develop or enhance smart protocols for recovery crafted for the particular procedure, practitioner, facility, and/or patient, for example.”);
identifying a historical operational trend for a first medical device of the plurality of medical devices based on the patient care tracking information ([0041], “The digital twin 130 can also be interrogated or queried in the digital twin environment 135 to retrieve and/or analyze current information 140, past history, etc.” [0070], “matching pre-op data, procedure data, post-op data, procedure guidelines, patient history, practitioner preferences, and the digital twin 130 can identify potential problems for a procedure, item tracking, and/or post-procedure recovery and develop or enhance smart protocols for recovery crafted for the particular procedure, practitioner, facility, and/or patient, for example.”);
detecting a patient care performance metric of the historical operational trend for the first medical device ([0071], “The example AR visualization 1000 further includes overlay data including information associated with various supplies, equipment and people (e.g., the physicians and the patient) included in the operating room 500 such as determined by the sensor 310, for example. Example information represented in the overlay data includes utilization and performance information associated with the various supplies, equipment and people, that have been determined to be relevant to the context of the user 1002.”);
identifying a new clinical workflow assignment for a second medical device based on a comparison of the patient care performance metric of the historical operational trend for the first medical device compared to an expected performance range; generating the clinical workflow information based on the new clinical workflow assignment for the second medical device ([0074], “At block 910, the digital twin 130 is updated based on the monitored procedure execution. For example, the object position, time, state, condition, and/or other aspect captured by the sensor 735, optics 300, tablet 410, etc., is provided via the input 730 to be modeled by the digital twin 130. A new model can be created and/or an existing model can be updated using the information.” [0076], “At block 914, periodic redeployment of the updated digital twin 130 is triggered. For example, feedback provided to and/or generated by the digital twin 130 can be used to update a model forming the digital twin 130. When a certain threshold of new data is reached, for example, the digital twin 130 can be retrained, retested, and redeployed to better mimic real-life surgical procedure information including items, instruments, personnel, protocol, etc. In certain examples, updated protocol/procedure information, new best practice, new instrument and/or personnel, etc., can be provided to the digital twin 130, resulting in an update and redeployment of the updated digital twin 130.”); and
facilitating communication of the clinical workflow information to the second medical device ([0146], “Data is then shared with authorized users, and any gathered and/or gleaned intelligence is fed back into the machines 1710-1712.” [0144], “a plurality of devices (e.g., information systems, imaging modalities, etc.) 1710-1712 can access a cloud 1720, which connects the devices 1710-1712 with a server 1730 and associated data store 1740… Other devices, such as medical imaging scanners, patient monitors, object scanners, location trackers, etc., can be outfitted with sensors and communication interfaces to enable them to communicate with each other and with the server 1730 via the cloud 1720.”).
Regarding claim 13, Peterson teaches the method of claim 1, as described above. Peterson further teaches the method further comprising:
executing, by the processor, Al optimization software from a nineth non-transitory memory to facilitate intercommunication between the object tracking software and the Al optimization software causing the processor to manage the patient care provided by at least some of the plurality of medical treatment devices by ([0158], “FIGS. 1-17 can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium”):
obtaining a portion of recovered prescriptive information associated with a first medical treatment device of the plurality of medical devices from the Al memory ([0035], “The digital twin 130 of the patient/protocol/item 110 can be used for monitoring, diagnostics, and prognostics for the patient/protocol/item 110. Using sensor data in combination with historical information, current and/or potential future conditions of the patient/protocol/item 110 can be identified, predicted, monitored, etc., using the digital twin 130.”);
identifying a historical operational trend for the first medical device based on the portion of recovered prescriptive information ([0035], “The digital twin 130 of the patient/protocol/item 110 can be used for monitoring, diagnostics, and prognostics for the patient/protocol/item 110. Using sensor data in combination with historical information, current and/or potential future conditions of the patient/protocol/item 110 can be identified, predicted, monitored, etc., using the digital twin 130.” [0037], “Rather than reading a report, a healthcare practitioner can view and simulate with the digital twin 130 to evaluate a condition, progression, possible treatment, etc., for the patient/protocol/item 110. In certain examples, features, conditions, trends, indicators, traits, etc., can be tagged and/or otherwise labeled in the digital twin 130 to allow the practitioner to quickly and easily view designated parameters, values, trends, alerts, etc.”);
detecting a patient care performance metric of the historical operational trend for the first medical device ([0038], “The digital twin 130 can also be used for comparison (e.g., to the patient/protocol/item 110, to a “normal”, standard, or reference patient, set of clinical criteria/symptoms, best practices, protocol steps, etc.). In certain examples, the digital twin 130 of the patient/protocol/item 110 can be used to measure and visualize an ideal or “gold standard” value state for that patient/protocol/item, a margin for error or standard deviation around that value (e.g., positive and/or negative deviation from the gold standard value, etc.), an actual value, a trend of actual values, etc. A difference between the actual value or trend of actual values and the gold standard (e.g., that falls outside the acceptable deviation) can be visualized as an alphanumeric value, a color indication, a pattern, etc.”);
identifying a new clinical workflow assignment for a second medical device based on a comparison of the patient care performance metric of the historical operational trend for the first medical device compared to an expected performance range and a historical remediation of the portion of recovered prescriptive information that is expected to produce a future patient care performance metric of a future historical operational trend for a combination of the first medical device and the second medical device that is inside of the expected performance range when operating in accordance with the new clinical workflow assignment for the second medical device; generating the clinical workflow information based on the new clinical workflow assignment for the second medical device ([0038], “A difference between the actual value or trend of actual values and the gold standard (e.g., that falls outside the acceptable deviation) can be visualized as an alphanumeric value, a color indication, a pattern, etc.” [0070], “The digital twin 130 continues to learn and improve as it receives and models feedback throughout the pre-procedure, during procedure, and post-procedure process including information regarding items used, items unused, items left, items missing, items broken, etc.” [0074], “At block 910, the digital twin 130 is updated based on the monitored procedure execution. For example, the object position, time, state, condition, and/or other aspect captured by the sensor 735, optics 300, tablet 410, etc., is provided via the input 730 to be modeled by the digital twin 130. A new model can be created and/or an existing model can be updated using the information.” [0076], “At block 914, periodic redeployment of the updated digital twin 130 is triggered. For example, feedback provided to and/or generated by the digital twin 130 can be used to update a model forming the digital twin 130. When a certain threshold of new data is reached, for example, the digital twin 130 can be retrained, retested, and redeployed to better mimic real-life surgical procedure information including items, instruments, personnel, protocol, etc. In certain examples, updated protocol/procedure information, new best practice, new instrument and/or personnel, etc., can be provided to the digital twin 130, resulting in an update and redeployment of the updated digital twin 130.”); and
facilitating communication of the clinical workflow information to the first medical device ([0146], “Data is then shared with authorized users, and any gathered and/or gleaned intelligence is fed back into the machines 1710-1712.” [0144], “a plurality of devices (e.g., information systems, imaging modalities, etc.) 1710-1712 can access a cloud 1720, which connects the devices 1710-1712 with a server 1730 and associated data store 1740… Other devices, such as medical imaging scanners, patient monitors, object scanners, location trackers, etc., can be outfitted with sensors and communication interfaces to enable them to communicate with each other and with the server 1730 via the cloud 1720.”).
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.
The factual inquiries 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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Peterson (US 20190087544) in view of Yadav-Ranjan (WO 2023151829).
Regarding claim 11, Peterson teaches the method of claim 1, as described above. Peterson does not teach the method further comprising: executing, by the processor, ledger software from a seventh non-transitory memory to facilitate intercommunication between the object tracking software and the ledger software causing the processor to memorialize the patient care tracking information in an object distributed ledger by: obtaining a portion of the object distributed ledger; hashing a portion of the patient care tracking information utilizing a receiving public key associated with the object distributed ledger to produce a next transaction hash value; encrypting the next transaction hash value utilizing a private key of the processor to produce a next transaction signature; generating a next block of a blockchain of the object distributed ledger to include the portion of the patient care tracking information and the next transaction signature; and causing inclusion of the next block in the object distributed ledger.
However, Yadav-Ranjan does teach the method further comprising:
executing, by the processor, ledger software from a seventh non-transitory memory to facilitate intercommunication between the object tracking software and the ledger software causing the processor to memorialize the patient care tracking information in an object distributed ledger by (pg. 12, lines 31-33, “Transactions are added to a blockchain by a large, distributed, and uninterested network of computers (nodes). This has two significant advantages. First, the blockchain functions as a private, permission-based “shared ledger” that provides trust through immutability.” Pg. 23, lines 31-35, “Although various embodiments are described herein above in terms of methods, apparatus, devices, computer-readable medium and receivers, the person of ordinary skill will readily comprehend that such methods can be embodied by various combinations of hardware and software in various systems, communication devices, computing devices, control devices, apparatuses, non-transitory computer-readable media, etc.” pg. 30, lines 1-4, “Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1612, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE.” Pg. 30, lines , “A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer,… and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot.”):
obtaining a portion of the object distributed ledger (pg. 4, lines 25-26, “these exemplary methods can also include receiving, from the secure client, a request for user access to at least a portion of a registered dataset.” Pg. 12, lines 17-19, “A blockchain is a sequence of records (or “blocks”) that are linked together using cryptography. Each block contains a cryptographic hash (or hash value) of the previous block, a timestamp, and transaction data of some type.” Pg. 16, lines 9-12, “A validator node validates this information based on the user’s public key, and then provides it to the block generator nodes, which combine the information along with other transaction data (e.g., other AI/ML models and datasets from other users) to form a new block.”);
hashing a portion of the patient care tracking information utilizing a receiving public key associated with the object distributed ledger to produce a next transaction hash value (pg. 12, lines 25-30, “A hash function is used to compress the relatively large amount of data comprising a block into a much smaller hash value. A hash value is a substantially unique representation of the data comprising the block, such that any changes to the data will result (with very high probability) of a corresponding change to the resulting hash value. Hash functions can be made with asymmetric cryptography, which means only certain private keys can break them. In other words, hashes can facilitate keeping transaction data on the blockchain private and secure from tampering.” Pg. 16, lines 9-14, “A validator node validates this information based on the user’s public key, and then provides it to the block generator nodes, which combine the information along with other transaction data (e.g., other AI/ML models and datasets from other users) to form a new block. Each block generator combines the transaction data with a current timestamp and a hash of the previous block, then computes a hash of this combined information (“block hash”).”);
encrypting the next transaction hash value utilizing a private key of the processor to produce a next transaction signature (pg. 16, lines 6-13, “Figure 5 shows an example of blockchain generation for the trusted data layer according to some embodiments of the present disclosure. A user (e.g., data scientist) can provide a model and related datafile to be included as transaction data in a block. This information can be encrypted with the user’s private key.”);
generating a next block of a blockchain of the object distributed ledger to include the portion of the patient care tracking information and the next transaction signature; and causing inclusion of the next block in the object distributed ledger (pg. 16, lines 12-16, “Each block generator combines the transaction data with a current timestamp and a hash of the previous block, then computes a hash of this combined information (“block hash”). Note that this block has will be added to the next subsequent block, i.e., as the hash of the previous block. The validator node will also perform a consensus protocol on the respective results from the block generator nodes for each block.” Pg. 24, lines 7-10, “The network nodes 1510 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 1512a, 1512b, 1512c, and 1512d (one or more of which may be generally referred to as UEs 1512) to the core network 1506 over one or more wireless connections.” Pg. 30, lines 1-7, “ Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., an alert is sent when moisture is detected), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).” Pg. 12, lines 31-32, “Transactions are added to a blockchain by a large, distributed, and uninterested network of computers (nodes).”).
Peterson in view of Yadav-Ranjan are considered analogous to the claimed invention because they are in the field of processing patient data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Peterson with Yadav-Ranjan for the advantage of “giv[ing] data providers (e.g., network operators) a secure audit trail with transparency and adherence to privacy regulations” (Yadav-Ranjan; pg. 12, lines 15-16).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Extended Intelligence Ecosystem For Soft Tissue Luminal Applications (US 20230157762) teaches techniques for implementing an intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem for soft tissue luminal applications. In various embodiments, a computing system analyzes first layer input data (indicating movement, position, and/or relative distance for a person(s) and object(s) in a room) and second layer input data. The second layer input data includes sensor and/or imaging data of a patient. Based on the analysis, the computing system generates one or more recommendations for guiding a medical professional in navigating a surgical device(s) with respect to one or more soft tissue luminal portions of the patient. The recommendation(s) include at least one mapped guide toward, in, and/or around the one or more soft tissue luminal portions. The mapped guide can include data corresponding to at least three dimensions, e.g., a 3D image/video. The computing system can present the recommendation(s) as image-based output, using a user experience device.
Viewing System For Use In A Surgical Environment (US 20210145525) teaches a viewing system for use in a surgical environment. Various real object detection devices detect locations of real objects in a real environment, such as a patient and body part of patient, medical staff, robots, a cutting tool on a robot, implant transferred by robot into body part, surgical tools, and disposable items. A map generator generates a map that forms a digital representation or a digital twin of the real environment. Various guiding modules including a room setup module, an anatomy registration module, a surgical planning module, and a surgical execution module make use of the digital representation to guide virtual or real objects based on the digital representation.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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