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 .
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 13 May 2025, has been 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 1-11 of copending Application No. 19075957 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both the instant application and the ‘957 application are directed toward tracking of objects in an environment to provide to a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of copending Application No. 19176340 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both the instant application and the ‘340 application are directed toward tracking of objects in an environment to provide to a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 of copending Application No. 18586139 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both the instant application and the ‘139 application are directed toward tracking of objects in an environment within a digital twin.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of copending Application No. 19018684 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘684 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of copending Application No. 19033954 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘954 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of copending Application No. 18949840 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘840 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of copending Application No. 18973152 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘152 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of copending Application No. 19018758 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘758 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of copending Application No. 18949810 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘810 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of copending Application No. 19033901 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘901 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of copending Application No. 19629315 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘315 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of copending Application No. 19430932 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘932 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of copending Application No. 19281577 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘577 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of copending Application No. 19456757 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘757 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of copending Application No. 19263080 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘080 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of copending Application No. 19076195 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘195 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of copending Application No. 19176708 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘708 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of copending Application No. 19207078 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘078 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of copending Application No. 19263211 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘211 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of copending Application No. 19281581 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application and the ‘181 application are directed toward tracking of objects in an environment to provide a virtual representation output.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 101
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method for self-communication and control. The limitations of:
[… a human user interacting with …] environment interpretation software […] to detect and identify a plurality of medical [… tools/instruments …] of a medical treatment environment based on at least one of environment signaling of the medical treatment environment and premise messages [… communicated …] to produce an identified medical [… tool/instrument …] identifier for each medical [… tool/instrument …] of the identified plurality of medical [… tools/instruments …], each medical [… tool/instrument …] of the plurality of medical [… tools/instruments …] 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 [… tool/instrument …], the object profile information comprising one or more of object basics, object deployment information, and object availability information; [… a human user interacting with …], profile generation software […] to facilitate intercommunication between the environment interpretation software and the profile generation software […] to exchange prescriptive information associated with each identified medical [… tool/instrument …] identifier with an [… algorithm …], the prescriptive information comprising object learnings based on an interpretation of a plurality of historical object behavior observations associated with at least one medical treatment [… tool/instrument …] of the plurality of medical treatment [… tools/instruments …] and a plurality of other medical [… tools/instruments …] associated with another medical treatment environment; and [… a human user interacting with …], object tracking software […] to facilitate intercommunication between the profile generation software and the object tracking software […] to exchange further environment signaling for the plurality of medical [… tools/instruments …] within the medical treatment environment using the object profile information and at least some of the prescriptive information to produce object tracking information in response to clinical workflow information for [… saving …], wherein the object tracking information is available to be subsequently recovered […] and utilized to [… display …] the plurality of medical [… tools/instruments …] within a […] representation of the medical treatment environment and to subsequently generate the clinical workflow information.
as drafted, is a method, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with a processor and various non-transitory memory, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, but for a processor and various non-transitory memory, the claim encompasses monitoring objects in an environment for a human user, and track the monitored objects to provide to a human user a virtual representation of the monitored environment for the human user to interact with in their workflow. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a processor and various non-transitory memory, which implements the abstract idea. The processor and various non-transitory memory are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 6 paragraphs [0054]-[0058]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of medical treatment devices, “environment signaling… and premise messages exchanged with another processor”, artificial intelligence (AI) memory, “storage within a digital twin memory” and “virtually represent… within a virtual representation”. The medical treatment devices are recited at a high-level of generality (i.e., use of a generic off the shelf medical equipment expected in a medical environment) and amounts to merely linking of the abstract idea to particular technological environment. The “environment signaling… and premise messages exchanged with another processor” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The artificial intelligence (AI) memory is recited at a high-level of generality (i.e., training a generic off the shelf machine learning algorithm to make predictions) and amounts to merely linking of the abstract idea to particular technological environment. The “storage within a digital twin memory” is recited at a high-level of generality (i.e., as a general means of storing data) and amounts to the mere storage of data, which is a form of extra-solution activity. The “virtually represent… within a virtual representation” is recited at a high-level of generality (i.e., as a general displaying data in a VR environment) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a processor and various non-transitory memory, to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of medical treatment devices, “environment signaling… and premise messages exchanged with another processor”, artificial intelligence (AI) memory, “storage within a digital twin memory” and “virtually represent… within a virtual representation” were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The medical treatment devices have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Peterson (20190087544): see below but at least Fig. 5-6, paragraph [0045]; Reiner (20170068792): see below but at least Fig. 7, paragraph [0351]; Schmucker (20140263633): Fig. 7, paragraphs [0003]-[0004]; tracking of medical devices in a medical environment is well-understood, routine, and conventional elements/functions. The “environment signaling… and premise messages exchanged with another processor” steps have been re-evaluated under the "significantly more" analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.0S(d)(II)(i) "Receiving or transmitting data over a network" is well-understood, routine, and conventional. The artificial intelligence (AI) memory has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Peterson (20190087544): see below but at least paragraphs [0093]-[0103]; Reiner (20170068792): see below but at least paragraphs [0187], [0263]; Tiwari (20230402167): paragraphs [0065], [0083]; training and use of AI is well-understood, routine and conventional. The “storage within a digital twin memory” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(iv) “Storing and retrieving information in memory” is well-understood, routine, and conventional. The “virtually represent… within a virtual representation” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Peterson (20190087544): see below but at least Fig. 10, paragraph [0077]; Case (20230069577): Fig. 4, paragraph [0066]; White (12444141): Fig. 11, Col. 2, lines 30-45; use of VR to display data is well-understood, routine, and conventional elements/functions. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-13 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2, 4-8 and 11-13 recite the additional elements of additional non-transitory memories implementing various software, however are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 6 paragraphs [0054]-[0058]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of additional non-transitory memories implementing various software, to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Claim 2 describes making predictions for a human user using obtained data, but does not recite any new additional elements not already considered above and incorporated herein.
Claim 3 describes updating of profiles using historical data, but does not recite any new additional elements not already considered above and incorporated herein.
Claims 4-8 recites displaying of data on a dashboard for a human user to use, however display of data was already considered above is incorporated herein.
Claim 9 recites the additional element of a “sensor module”, however this is recited at a high-level of generality (i.e., generic off-the-shelf sensor) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a “sensor module” was considered generally linking the abstract idea to particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Peterson (20190087544): see below but at least paragraph [0032], [0062]; Reiner (20170068792): see below but at least paragraph [0024]; Schmucker (20140263633): Fig. 7, paragraph [0034]; collection of data using a sensor is well-understood, routine, and conventional elements/functions. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claim 10 recites comparison of data, but does not recite any new additional elements not already considered above and incorporated herein.
Claim 11 recites the additional element of using a distributed ledger that hashes and encrypts keys; however, this is recited at a high-level of generality (i.e., a generic off-the-shelf blockchain performing blockchain transactions) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a distributed ledger that hashes and encrypts keys was considered generally linking the abstract idea to particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Tikka (20220051767): see below but at least Fig. 4, paragraph [0010, [0020]]; Casey (20230067537): paragraphs [0001]-[0002], [0022]; Hussain (20240138991): paragraphs [0046], [0093]; use of blockchain that encrypts and hashes using keys is well-understood, routine, and conventional elements/functions. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 12 and 13 recite comparison and organization of data to create and provide workflow information, and amounts to providing instructions, however communication of data was already considered above and is incorporated herein.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-10 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. No. 20190087544 (hereafter “Peterson”), in view of U.S. Patent App. No. 20170068792 (hereafter “Reiner”).
Regarding claim 1, Peterson teaches a computerized method for processing data of a self-forming communication and control system (Peterson: Figs. 1-2, 5-12, paragraph [0006], “a method”, paragraph [0028], “a module, unit, or system may include a computer processor, controller, and/or other logic-based device that performs operations based on instructions stored on a tangible and non-transitory computer readable storage medium, such as a computer memory”), 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 (Peterson: Figs. 1-2, 5-12, paragraph [0009], “a scanner to scan items in its field of view”, paragraph [0016], “an operating room environment”, paragraphs [0046]-[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.”, paragraphs [0049]-[0052], “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”, paragraph [0082], “item number 1218 can also be provided to allow the digital twin 130 to model and plan, order, configure, etc.”, paragraph [0123], “Example processor 1530 includes hardware and/or software configuring the hardware to execute one or more tasks and/or implement a particular system configuration”. Also see, paragraph [0028]. The medical devices are identified and associated with an item number (i.e., a device identifier) which teaches what is required of the claim under the broadest reasonable interpretation),
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 (Peterson: Figs. 1-2, 5-12, paragraphs [0030]-[0032], “digital information can be implemented as a “twin” of a physical device/system/person/process and information associated with and/or embedded within the physical device/system/process… 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… a patient, protocol, and/or other item 110 in a real space 115 providing data 120 to a digital twin 130 in a virtual space 135… Sensors connected to the physical object”, paragraph [0051], “item such as instruments, instrument trays, disposables, etc., in an operating room, surgical suite, surgical field, etc.”),
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 (Peterson: Figs. 1-2, 5-12, paragraph [0051], “scans items in its field of view (e.g., scans barcodes, radiofrequency identifiers (RFIDs), visual profile/characteristics, etc.)”, paragraph [0071], “preferences, reminders, alerts, and/or other instructions, as well as likely outcomes, can be provided”, paragraphs [0080]-[0083], “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, staffing, schedule, etc., for a particular surgeon, other healthcare practitioner, surgical team, etc. … the preference card 1200 includes a plurality of fields to identify information, provide parameters, and/or set other preferences for a surgical procedure by user.”. The Examiner notes RFID is at least an encoded electromagnetic signal/emission under the broadest reasonable interpretation and teaches what is required of the claim as currently drafted);
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 each identified medical treatment device identifier with an artificial intelligence (AI) memory, the prescriptive information comprising object learnings […] (Peterson: Figs. 1-2, 5-12, paragraphs [0038]-[0040], “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.)… one or more reference digital twins represent particular patient(s)/protocol(s)/item(s) 110”, paragraph [0071], “a surgeon's preference cards can be updated/customized for the particular patient and/or procedure based on the digital twin 130”, paragraph [0081], “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, staffing, schedule, etc.,… model one or more preference cards including to update the preference card(s)”, paragraph [0094], “Machine learning techniques, whether deep learning networks or other experiential/observational learning system, can be used to model information”, paragraph [0105], “Deep learning machines can utilize transfer learning when interacting with physicians to counteract the small dataset available in the supervised training. These deep learning machines can improve their protocol adherence over time through training and transfer learning.”. Also see, paragraph [0028]); 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 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 object tracking information in response to clinical workflow information for storage within a digital twin memory (Peterson: Figs. 1-2, 5-12, paragraph [0004], “receive input regarding a first item at a first location; compare the first item to the items associated with each task of the first healthcare procedure; and, when the first item matches an item associated with a task of the first healthcare procedure, record the first item and approval for the first healthcare procedure and update the digital twin based on the first item. When the first item does not match an item associated with a task of the first healthcare procedure, the example digital twin is to log the first item”, paragraph [0014], “FIG. 8 illustrates an example ecosystem to facilitate trending and tracking of surgical procedures and other protocol compliance via a digital twin”, paragraphs [0044]-[0046], “the digital twin 130 can be used for improved instrument and/or surgical item tracking/management, etc. … the digital twin 130 can model, track, simulate, track objects in a surgical field, and predict item usage, user preference, probability of being left behind, etc. …Through improved modeling, tracking, predicting/simulating, and reporting via the surgical digital twin 130”, paragraph [0051]-[0053], “The scanner 310 and/or the digital twin 130 can identify and track items within range of the scanner 310, for example. 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… The computing device 410 can be used to house the surgical digital twin 130, update and/or otherwise communicate with the digital twin 130, store preference card(s), store procedure/protocol information, track protocol compliance, generate analytics, etc.”, paragraphs [0153]-[0155], “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… define a clinical workflow for a certain event… Additional workflows can be facilitated”. Also see, paragraph [0028].),
wherein the object tracking information is available to be subsequently recovered from the digital twin memory and utilized to virtually represent the plurality of medical treatment devices within a virtual representation of the medical treatment environment and to subsequently generate the clinical workflow information (Peterson: Figs. 1-2, 5-12, paragraph [0016], “presents an example augmented reality visualization including auxiliary information regarding various aspects of an operating room environment”, paragraph [0051], “The scanner 310 and/or the digital twin 130 can identify and track items within range of the scanner 310, for example. 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”, paragraphs [0077]-[0080], “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. FIG. 10 presents an example AR visualization 1000 including auxiliary information regarding various aspects of an operating room environment in accordance with one or more embodiments described herein”, paragraphs [0153]-[0155], “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… Additional workflows can be facilitated”. The Examiner notes that “to subsequently generate the clinical workflow information” is an intended use of the object tracking information that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of use of the object tracking information).
Peterson may not explicitly teach (underlined below for clarity):
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 each identified medical treatment device identifier with an artificial intelligence (AI) memory, the prescriptive information comprising object learnings based on an interpretation of a plurality of historical object behavior 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;
Reiner teaches 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 each identified medical treatment device identifier with an artificial intelligence (AI) memory, the prescriptive information comprising object learnings based on an interpretation of a plurality of historical object behavior 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 (Reiner: paragraph [0107], “using computerized methods of artificial intelligence”, paragraph [0144], “create a computerized risk/benefit analysis of the planned procedure by correlating medical device, patient profile, and provider profile data… This program 110 derived Medical Device/Procedural Risk Benefit Analysis takes into account the historical performance (i.e., quality and safety) records of the medical device and provider specific to the planned procedure, along with the inherent clinical risk profile of the patient”, paragraph [0154], “provide for historic analysis of the provider performance (i.e., quality and safety metrics) specific to the planned procedure and medical device to be used”, paragraphs [0164]-[0165], “Over time, these sequential measurements of device position will provide one with the ability to track device positional changes over time specific to the individual device, patient, and clinical use… dynamically adjust medical device profile practice guidelines in accordance with general community-based medical standards, along with the specific data measurements and analytics of the individual patient… standardize and streamline medical care, while also providing for consistent outcome data which can be incorporated into the medical device database 113, 114 for outcomes analysis… assessment of device positioning, each individual medical device will have its own unique device profile, which will be in part related to clinical use and individual patient attributes”, paragraph [0263], “the creation of the database 113, 114 also provides for the program's 110 creation of automated authorization of the medical device and procedure using neural networks and other artificial intelligence techniques based upon established best clinical practice guidelines”);
The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) environment interpretation software from a first non-transitory memory (b) profile generation software from a second non-transitory memory, and (c) object tracking software from a third non-transitory memory, the software that is implemented by the systems of Peterson and Reiner in combination teach the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the software stored on non-transitory memory of Peterson and Reiner separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04.
One of ordinary skill in the art before the effective filing date would have found it obvious to include using historical performance (i.e., behavior observations) with machine learning for generating update profiles as taught by Reiner within the use of machine learning for tracking and providing a virtual environment as taught by Peterson with the motivation of “standardize and streamline medical care, while also providing for consistent outcome data which can be incorporated into the medical device database 113, 114 for outcomes analysis” (Reiner: paragraphs [0164]-[0165]).
Regarding claim 2, Peterson and Reiner teach the limitations of claim 1, and further teach 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 object tracking information, store the other object tracking information in the AI memory as the plurality of historical object behavior observations associated with the corresponding plurality of other medical treatment devices (Peterson: paragraph [0050]-[0053], “scan and record item such as instruments, instrument trays, disposables, etc., in an operating room, surgical suite, surgical field, etc. … update and/or otherwise communicate with the digital twin 130, store preference card(s), store procedure/protocol information, track protocol compliance, generate analytics, etc.”; Reiner: paragraphs [0085]-[0087], “Clinical Performance data, Historical usage of Device and/or Procedure to be performed”, paragraph [0164], “Over time, these sequential measurements of device position will provide one with the ability to track device positional changes over time specific to the individual device, patient, and clinical use”, paragraph [0175], “historical device data use within comparable peer groups”. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) environment interpretation software from a first non-transitory memory (b) profile generation software from a second non-transitory memory, (c) object tracking software from a third non-transitory memory, and (d) the object learning software from a fourth non-transitory memory, the software that is implemented by the systems of Peterson and Reiner in combination teach the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the software stored on non-transitory memory of Peterson and Reiner separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04.),
recover a portion of the plurality of historical object behavior observations from the AI memory, and infer the object learnings based on an interpretation of the portion of the plurality of historical object behavior observations as the prescriptive information, the object learnings predicting future object behavior of the plurality of medical treatment devices (Peterson: paragraph [0035], “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.”, paragraph [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.”; Reiner: paragraph [0107], “using computerized methods of artificial intelligence”, paragraph [0144], “This program 110 derived Medical Device/Procedural Risk Benefit Analysis takes into account the historical performance (i.e., quality and safety) records of the medical device and provider specific to the planned procedure”, paragraph [0170], “recorded by the program 110 at baseline and subsequently reanalyzed by the program 110 over time to assess changes in device position and/or functionality”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 3, Peterson and Reiner teach the limitations of claim 1, and further teach 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 AI memory, the updated object profile information comprising one or more of updated object basics, updated object deployment information, and updated object availability information (Peterson: paragraph [0005], “update the digital twin based on the first item”, paragraph [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.)”, paragraph [0075], “update a central inventory based on item usage during a procedure”, paragraphs [0081]-[0083], “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, staffing, schedule, etc., for a particular surgeon, other healthcare practitioner, surgical team, etc. The digital twin 130 can model one or more preference cards including to update the preference card(s), simulate using the preference card(s), predict using the preference card(s)”, paragraph [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”; Reiner: paragraphs [0164]-[0165], “Over time, these sequential measurements of device position will provide one with the ability to track device positional changes over time specific to the individual device, patient, and clinical use… dynamically adjust medical device profile practice guidelines in accordance with general community-based medical standards, along with the specific data measurements and analytics of the individual patient… standardize and streamline medical care, while also providing for consistent outcome data which can be incorporated into the medical device database 113, 114 for outcomes analysis… assessment of device positioning, each individual medical device will have its own unique device profile, which will be in part related to clinical use and individual patient attributes”),
the updated prescriptive information comprising one or more of updated object learnings based on another interpretation of the plurality of historical object behavior 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 (Peterson: paragraph [0005], “update the digital twin based on the first item”, paragraph [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.)”, paragraphs [0081]-[0083], “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, staffing, schedule, etc., for a particular surgeon, other healthcare practitioner, surgical team, etc. The digital twin 130 can model one or more preference cards including to update the preference card(s), simulate using the preference card(s), predict using the preference card(s)”; Reiner: paragraph [0144], “create a computerized risk/benefit analysis of the planned procedure by correlating medical device, patient profile, and provider profile data… This program 110 derived Medical Device/Procedural Risk Benefit Analysis takes into account the historical performance (i.e., quality and safety) records of the medical device and provider specific to the planned procedure, along with the inherent clinical risk profile of the patient”, paragraph [0154], “provide for historic analysis of the provider performance (i.e., quality and safety metrics) specific to the planned procedure and medical device to be used”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 4, Peterson and Reiner teach the limitations of claim 1, and further teach 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 object 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 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 (Peterson: paragraph [0016], “presents an example augmented reality visualization including auxiliary information regarding various aspects of an operating room environment”, paragraph [0058], “the device 300 and/or 410 can provide a display window including information regarding instruments, protocol actions, implants, items, etc.”, paragraph [0062], “update the surgical materials digital twin 130 based on the object(s) detected by the sensor 735 and identified by the processor 710”, paragraphs [0077]-[0080], “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. FIG. 10 presents an example AR visualization 1000 including auxiliary information regarding various aspects of an operating room environment in accordance with one or more embodiments described herein”, paragraphs [0115]-[0116], “a variety of user interface frameworks… provide one or more dashboards for specific sets of patients and/or practitioners, such as surgeons, surgical technicians, nurses, assistants, radiologists, administrators, etc. Dashboard(s) can be based on condition, role, and/or other criteria to indicate variation(s) from a desired practice, for example”, paragraph [0135], “implement a user interface 1626 to enable a healthcare practitioner and/or administrator to interact with healthcare system 1600”, paragraph [0141], “providing a status”. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) environment interpretation software from a first non-transitory memory (b) profile generation software from a second non-transitory memory, (c) object tracking software from a third non-transitory memory, and (d) dashboard software from a fifth non-transitory memory, the software that is implemented by the systems of Peterson and Reiner in combination teach the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the software stored on non-transitory memory of Peterson and Reiner separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04.).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 5, Peterson and Reiner teach the limitations of claim 4, and further teach executing, by the processor, further dashboard software from the fifth non-transitory memory causing the processor to: obtain the portion of the object 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 object tracking information in accordance with the object profile information to produce the dashboard information (Peterson: paragraph [0016], “presents an example augmented reality visualization including auxiliary information regarding various aspects of an operating room environment”, paragraph [0023], “the collection of real-time medical device data… which can facilitate real-time data analysis and communication between the involved parties”, paragraph [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.)… 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”, paragraph [0046], “the digital twin 130 can model, track, simulate, track objects in a surgical field, and predict item usage, user preference, probability of being left behind, etc.”, paragraph [0058], “the device 300 and/or 410 can provide a display window including information regarding instruments, protocol actions, implants, items, etc.”, paragraph [0062], “update the surgical materials digital twin 130 based on the object(s) detected by the sensor 735 and identified by the processor 710”, paragraph [0247], “an electronic display on a display device 102, which highlights the predefined sensors… an implanted sensor activation system which has the program 110 emit a visual display of the specific sensors”; Reiner: paragraph [0144]).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 6, Peterson and Reiner teach the limitations of claim 4, and further teach 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 AI 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 identified plurality of medical treatment devices within the medical treatment environment (Peterson: paragraph [0016], “presents an example augmented reality visualization including auxiliary information regarding various aspects of an operating room environment”, paragraph [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.)… 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”, paragraph [0046], “the digital twin 130 can model, track, simulate, track objects in a surgical field, and predict item usage, user preference, probability of being left behind, etc.”, paragraph [0071], “preferences, reminders, alerts, and/or other instructions, as well as likely outcomes, can be provided via the digital twin 130”, paragraph [0087], “the surgeon and/or other healthcare practitioner is alerted to warn them”, paragraph [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”; Reiner: paragraph [0144]. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) environment interpretation software from a first non-transitory memory (b) profile generation software from a second non-transitory memory, (c) object tracking software from a third non-transitory memory, (d) dashboard software from a fifth non-transitory memory, and (e) prescriptive software from a sixth non-transitory memory, the software that is implemented by the systems of Peterson and Reiner in combination teach the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the software stored on non-transitory memory of Peterson and Reiner separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04.).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 7, Peterson and Reiner teach the limitations of claim 6, and further teach 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 (Peterson: paragraph [0071], “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… 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”, paragraphs [0080]-[0083], “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, staffing, schedule, etc., for a particular surgeon, other healthcare practitioner, surgical team, etc.”, paragraph [0121], “communicate an instruction or data to system”, paragraphs [0125]-[0126], “Communication via communication interface 1550 can be implemented using one or more protocols. In some examples, communication via communication interface 1550 occurs according to one or more standards… facilitate access to information, protocol library”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 8, Peterson and Reiner teach the limitations of claim 6, and further teach 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 (Peterson: paragraph [0059], “during a pre-operative (“pre-op”) period”, paragraph [0064], “help a surgeon and/or other healthcare personnel plan for a surgical procedure”; Reiner: paragraph [0170], “continuously recording this sensor positional over a defined period of time”, paragraph [0290], “the provider could simply select the “Edit Timeline” option and insert and/or edit the missing and/or erroneous data, along with accompanying information for explanation”, paragraph [0281], “the program 110 will provide a recommendation in an attempt to optimize clinical outcomes based upon the available data and established practice standards. Alternative recommendations could include alteration of the planned medical procedure, replacement of the medical device, or referral to an alternative healthcare provider”, paragraph [0300], “Classification of these “higher risk” patients is typically identified by the healthcare professional prior to the planned procedure and recorded in the Patient Profile in the form of a standardized Patient Morbidity Score”),
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 AI memory (Peterson: paragraph [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”, paragraph [0144], “healthcare information can be distributed among multiple applications using a variety of database and storage technologies and data formats”, paragraph [0151], “structuring data logically”; Reiner: paragraph [0260], “record accurate and reproducible data into the database 113, 114 in a standardized format”, paragraph [0290], “All recorded data would be presented by the program 110 to the provider for verification at that time, which may be displayed on a timeline which tracks all sequential registered events. In the event that some data point was missing or determined to be erroneous by program 110 analysis, the provider could simply select the “Edit Timeline” option and insert and/or edit the missing and/or erroneous data, along with accompanying information for explanation.”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 9, Peterson and Reiner teach the limitations of claim 1, and further teach 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 (Peterson: Fig. 3, paragraph [0009], “FIG. 3 shows an example optical head-mounted display including a scanner to scan items in its field of view”, paragraph [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.”);
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 (Peterson: Figs. 1-2, 5-12, paragraphs [0030]-[0032], “digital information can be implemented as a “twin” of a physical device/system/person/process and information associated with and/or embedded within the physical device/system/process… 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… a patient, protocol, and/or other item 110 in a real space 115 providing data 120 to a digital twin 130 in a virtual space 135… Sensors connected to the physical object”, paragraphs [0049]-[0052], “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”, paragraph [0082], “item number 1218 can also be provided to allow the digital twin 130 to model and plan, order, configure, etc.”,).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 10, Peterson and Reiner teach the limitations of claim 1, and further teach 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 (Peterson: paragraph [0124], “memory 1540 can include a relational database”; Reiner: paragraph [0105], “Embedded (i.e., identification data specific to each individual device is directly embedded within each device, which can be retrieved by the program 110 from a universal “look up” database 113,114 for device specific data”);
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 (Peterson: paragraph [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.)”, paragraphs [0080]-[0083], “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, staffing, schedule, etc., for a particular surgeon, other healthcare practitioner, surgical team, etc. The digital twin 130 can model one or more preference cards”; Reiner: paragraph [0175], “perform comparative analysis with “comparable” data contained within the database 113, 114. “Comparable” data can correspond to the specific device attributes (e.g., manufacturer, model, device category), patient profile (e.g., age, size, comorbidities), clinical use (e.g., indication for use, underlying disease, severity of illness), individual provider profile (e.g., clinical experience, education/training, technical skills), and institutional provider profile (e.g., type of institution, patient population served, support staff, technology in use).”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 12, Peterson and Reiner teach the limitations of claim 1, and further teach 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 plurality of medical treatment devices by: obtaining a portion of the object 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 object tracking information (Peterson: paragraph [0035], “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.”, paragraphs [0038]-[0041], “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… 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.”; Reiner: paragraph [0107], “using computerized methods of artificial intelligence”, paragraph [0144], “This program 110 derived Medical Device/Procedural Risk Benefit Analysis takes into account the historical performance (i.e., quality and safety) records of the medical device and provider specific to the planned procedure”, paragraph [0170], “recorded by the program 110 at baseline and subsequently reanalyzed by the program 110 over time to assess changes in device position and/or functionality”. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) environment interpretation software from a first non-transitory memory (b) profile generation software from a second non-transitory memory, (c) object tracking software from a third non-transitory memory, and (d) object control software from an eighth non-transitory memory, the software that is implemented by the systems of Peterson and Reiner in combination teach the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the software stored on non-transitory memory of Peterson and Reiner separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04.);
detecting a performance metric of the historical operational trend for the first medical device; identifying a clinical workflow reassignment for the first medical device based on a comparison of the performance metric of the historical operational trend for the first medical device compared to an expected performance range (Peterson: paragraph [0005], “update the digital twin based on the first item”, paragraph [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.)”, paragraph [0043], “digital twin 130 can model execution of such a plan/protocol, simulate impact on the patient condition, predict next step(s) in patient care, suggest next action(s) to facilitate patient compliance, etc.”, paragraphs [0081]-[0083], “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, staffing, schedule, etc., for a particular surgeon, other healthcare practitioner, surgical team, etc. The digital twin 130 can model one or more preference cards including to update the preference card(s), simulate using the preference card(s), predict using the preference card(s)”, paragraph [0103], “operate according to a specified threshold, etc.)”; Reiner: paragraph [0144], “create a computerized risk/benefit analysis of the planned procedure by correlating medical device, patient profile, and provider profile data… This program 110 derived Medical Device/Procedural Risk Benefit Analysis takes into account the historical performance (i.e., quality and safety) records of the medical device and provider specific to the planned procedure, along with the inherent clinical risk profile of the patient”, paragraphs [0153]-[0155], “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… Additional workflows can be facilitated”; Reiner: paragraph [0159], “using outcomes data from the database 113, 114 is the ability to modify the selection of different procedures and devices in an attempt to improve the Risk/Benefit Analysis… modification of the procedure could yield an improved risk/benefit score (e.g., changing the procedure from a biopsy under fluoroscopic guidance to a biopsy under CT guidance)… stick with the procedure as planned, but switch to an alternative biopsy device with a higher risk/benefit profile”, paragraph [0263], “modify the procedure/device in keeping with the established standards”, paragraph [0281], “Alternative recommendations could include alteration of the planned medical procedure, replacement of the medical device, or referral to an alternative healthcare provider”);
generating the clinical workflow information based on the clinical workflow reassignment for the first medical device; and facilitating communication of the clinical workflow information to the first medical device (Peterson: paragraphs [0038]-[0043], “and the digital twin 130 can model execution of such a plan/protocol, simulate impact on the patient condition, predict next step(s) in patient care, suggest next action(s) to facilitate patient compliance, etc.”, paragraph [0058], “provide a display window including information regarding instruments, protocol actions, implants, items, etc.”, paragraphs [0153]-[0155], “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… Additional workflows can be facilitated”; Reiner: paragraph [0159], “using outcomes data from the database 113, 114 is the ability to modify the selection of different procedures and devices in an attempt to improve the Risk/Benefit Analysis… modification of the procedure could yield an improved risk/benefit score (e.g., changing the procedure from a biopsy under fluoroscopic guidance to a biopsy under CT guidance)… stick with the procedure as planned, but switch to an alternative biopsy device with a higher risk/benefit profile”, paragraph [0263], “modify the procedure/device in keeping with the established standards”, paragraph [0281], “Alternative recommendations could include alteration of the planned medical procedure, replacement of the medical device, or referral to an alternative healthcare provider”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 13, Peterson and Reiner teach the limitations of claim 1, and further teach executing, by the processor, AI optimization software from a nineth non-transitory memory to facilitate intercommunication between the object tracking software and the AI optimization software causing the processor to manage 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 AI memory; identifying a historical operational trend for the first medical device based on the portion of recovered prescriptive information (Peterson: paragraph [0035], “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.”, paragraphs [0038]-[0041], “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… 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.”; Reiner: paragraph [0107], “using computerized methods of artificial intelligence”, paragraph [0144], “This program 110 derived Medical Device/Procedural Risk Benefit Analysis takes into account the historical performance (i.e., quality and safety) records of the medical device and provider specific to the planned procedure”, paragraph [0170], “recorded by the program 110 at baseline and subsequently reanalyzed by the program 110 over time to assess changes in device position and/or functionality”. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) environment interpretation software from a first non-transitory memory (b) profile generation software from a second non-transitory memory, (c) object tracking software from a third non-transitory memory, and (d) AI optimization software from a nineth non-transitory memory, the software that is implemented by the systems of Peterson and Reiner in combination teach the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the software stored on non-transitory memory of Peterson and Reiner separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04.);
detecting a performance metric of the historical operational trend for the first medical device; identifying a clinical workflow reassignment for the first medical device based on a comparison of the 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 performance metric of a future historical operational trend for the first medical device that is inside of the expected performance range when operating in accordance with the clinical workflow reassignment (Peterson: paragraph [0005], “update the digital twin based on the first item”, paragraph [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.)”, paragraph [0043], “digital twin 130 can model execution of such a plan/protocol, simulate impact on the patient condition, predict next step(s) in patient care, suggest next action(s) to facilitate patient compliance, etc.”, paragraphs [0081]-[0083], “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, staffing, schedule, etc., for a particular surgeon, other healthcare practitioner, surgical team, etc. The digital twin 130 can model one or more preference cards including to update the preference card(s), simulate using the preference card(s), predict using the preference card(s)”, paragraph [0103], “operate according to a specified threshold, etc.)”; Reiner: paragraph [0144], “create a computerized risk/benefit analysis of the planned procedure by correlating medical device, patient profile, and provider profile data… This program 110 derived Medical Device/Procedural Risk Benefit Analysis takes into account the historical performance (i.e., quality and safety) records of the medical device and provider specific to the planned procedure, along with the inherent clinical risk profile of the patient”, paragraphs [0153]-[0155], “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… Additional workflows can be facilitated”; Reiner: paragraph [0159], “using outcomes data from the database 113, 114 is the ability to modify the selection of different procedures and devices in an attempt to improve the Risk/Benefit Analysis… modification of the procedure could yield an improved risk/benefit score (e.g., changing the procedure from a biopsy under fluoroscopic guidance to a biopsy under CT guidance)… stick with the procedure as planned, but switch to an alternative biopsy device with a higher risk/benefit profile”, paragraph [0263], “modify the procedure/device in keeping with the established standards”, paragraph [0281], “Alternative recommendations could include alteration of the planned medical procedure, replacement of the medical device, or referral to an alternative healthcare provider”);
generating the clinical workflow information based on the clinical workflow reassignment for the first medical device; and facilitating communication of the clinical workflow information to the first medical device (Peterson: paragraphs [0038]-[0043], “and the digital twin 130 can model execution of such a plan/protocol, simulate impact on the patient condition, predict next step(s) in patient care, suggest next action(s) to facilitate patient compliance, etc.”, paragraph [0058], “provide a display window including information regarding instruments, protocol actions, implants, items, etc.”, paragraphs [0153]-[0155], “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… Additional workflows can be facilitated”; Reiner: paragraph [0159], “using outcomes data from the database 113, 114 is the ability to modify the selection of different procedures and devices in an attempt to improve the Risk/Benefit Analysis… modification of the procedure could yield an improved risk/benefit score (e.g., changing the procedure from a biopsy under fluoroscopic guidance to a biopsy under CT guidance)… stick with the procedure as planned, but switch to an alternative biopsy device with a higher risk/benefit profile”, paragraph [0263], “modify the procedure/device in keeping with the established standards”, paragraph [0281], “Alternative recommendations could include alteration of the planned medical procedure, replacement of the medical device, or referral to an alternative healthcare provider”).
The motivation to combine is the same as in claim 1, incorporated herein.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. No. 20190087544 (hereafter “Peterson”) and U.S. Patent App. No. 20170068792 (hereafter “Reiner”) as applied to claim 1 above, and further in view of U.S. Patent App. No. 20220051767 (hereafter “Tikka”).
Regarding claim 11, Peterson and Reiner teach the limitations of claim 1, but may not explicitly teach 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 object tracking information in an object distributed ledger by: obtaining a portion of the object distributed ledger;
hashing a portion of the object 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 object tracking information and the next transaction signature; and causing inclusion of the next block in the object distributed ledger.
Tikka teaches 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 object tracking information in an object distributed ledger by: obtaining a portion of the object distributed ledger (Tikka: paragraph [0019], “aspects of a surgical procedure may be tracked using a ledger”, paragraph [0036], “the ledger 202 may be distributed”. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) environment interpretation software from a first non-transitory memory (b) profile generation software from a second non-transitory memory, (c) object tracking software from a third non-transitory memory, and (d) ledger software from a seventh non-transitory memory, the software that is implemented by the systems of Peterson, Reiner and Tikka in combination teach the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the software stored on non-transitory memory of Peterson, Reiner and Tikka separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04.);
hashing a portion of the object 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 (Tikka: paragraph [0020], “blocks in the ledger may be hashed. Each block may have its own encryption scheme… each block may be encrypted with a public key, only decryptable with a private key for that block”, paragraph [0037], “each block may be hashed such that an entity creating a next block can save the hashed previous block but cannot access its contents”);
generating a next block of a blockchain of the object distributed ledger to include the portion of the object tracking information and the next transaction signature; and causing inclusion of the next block in the object distributed ledger (Tikka: Fig. 5, paragraph [0039], “he ledger 400 stores data through block 406 with entry 4A, with all four blocks 402, 403, 404, and 408 being signed, encrypted, or otherwise closed… creating new block”, paragraphs [0043]-[0046], “store the patient information for the treatment event in a new block. The patient information may include data from an event (e.g., surgery) … the new block may be appended to the ledger… store the ledger including the new block. Storing the ledger may include saving the ledger, distributing an update”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a ledger for surgical item tracking as taught by Tikka with the use of machine learning for tracking surgical objects and providing a virtual environment as taught by Peterson and Reiner with the motivation of “allows retention of information… but prevents exposure of patient data” (Tikka: paragraphs [0045]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Pub. No. 20140263633 (hereafter “Schmucker”) teaches tracking of surgical instruments in a surgical environment and providing guidance to a user based on analysis of the tracked instruments.
U.S. Patent Pub. No. 20200395118 (hereafter “Codd”) teaches tagging of surgical equipment to for tracking and building profiles for comparison to past procedures for surgical compliance.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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