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 .
DETAILED ACTION
In the response filed on 16 April 2025, the following has occurred: claims 1-2 and 13-15 have been amended.
Now claims 1-2, 5-15 and 18-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 5-15 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite system and computer-implemented method for analyzing surgical data. The limitations of:
Claim 1 which is representative of claim 14
[… obtain …], a first collection of unredacted data associated, with a first surgical procedure, wherein the first collection of unredacted data includes first patient personal data, first patient clinical data, and first other patient data; [… record …] the first collection of unredacted data; [… obtain …], a second collection of respective unredacted data associated with a second surgical procedure; [… record …] the second collection of unredacted data; [… obtain a …] model for optimizing clinical outcome using the first collection of unredacted data and the second collection of respective unredacted data; generate first information that optimizes a clinical outcome of a third surgical procedure using the […] model; and [… provide …] the first information […], the first information comprising computer executable instructions and the […] model, the […] model executable […] to, upon conditions of a surgical procedure […] matching a stored pattern, in the […] model, [… provide …] the computer executable instructions […] for execution […], the computer executable instructions causing a control program […] to be modified; […] form redacted first information; and [… provide …] the redacted first information […] outside the local network .
, as drafted, is a system, which under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a processor, computing memory, various surgical hubs or a data system on a local data network, a cloud system, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 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 the processor, computing memory, various surgical hubs or a data system on a local data network, a cloud system, the claim encompasses collection and organization of data to make determinations for a physician in performance of a surgical procedure with providing of information to the physician for the physician to use in their performance of the procedure. 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, computing memory, various surgical hubs or a data system on a local data network, a cloud system, which implements the abstract idea. The a processor, computing memory, various surgical hubs or a data system on a local data network, a cloud system is recited at a high-level of generality (i.e., a general-purpose computers/ computer component implementing generic computer functions; see Applicant’s specification Figure 4, paragraphs [0060]-[0074]) 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 “receive… receive… send… becoming linked… forward… send…”, “save in the computing memory…”, “train a machine learning (ML) model… using the trained ML model”, “a surgical device” and “redact patient data from the first information”. The “receive… receive… send… becoming linked… forward… send…” 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 “save in the computing 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 “train a machine learning (ML) model… using the trained ML model” is recited at a high-level of generality (i.e., a off-the-shelf machine learning algorithm) and amounts to generally linking the abstract idea to a particular technological environment. The “a surgical device” is recited at a high-level of generality (i.e., an off-the-shelf medical instrument) and amounts to generally linking the abstract idea to a particular technological environment. The “redact patient data from the first information” is recited at a high-level of generality (i.e., generic anonymizing data) and amounts to generally linking the abstract idea to a 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, computing memory, various surgical hubs or a data system on a local data network, a cloud system, to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer 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 “receive… receive… send… becoming linked… forward… send…”, “save in the computing memory…”, “train a machine learning (ML) model… using the trained ML model”, “a surgical device” and “redact patient data from the first information” were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The “receive… receive… send… becoming linked… forward… send…” 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 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The “save in the computing 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 “train a machine learning (ML) model… using the trained ML model” 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 2019/0201123 (hereafter “Shelton”): see below but at least paragraph [0281]; 9,788,907 (hereafter “Alvi”): Column 28, lines 1-10, claim 1; training and use of a machine learning model is well-understood, routine and conventional. The “a surgical device” 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 2019/0201123 (hereafter “Shelton”): see below but at least Figure 1, paragraph [0011]; 9,788,907 (hereafter “Alvi”): Figure 6, Column 18, lines 45-end; using surgical instruments is well-understood, routine and conventional. The “redact patient data from the first information” 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 9,788,907 (hereafter “Alvi”): Column 3, line 65-Column 4, line 25; 2020/0285771 (hereafter “Dey”): paragraph [0006]; redacting data is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2, 5-13, 15 and 18-20 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 and 15 describes a request for information, however do not recite any additional elements sufficient to provide a practical application/significantly more.
Claims 5 and 18 further describes the surgical procedure, however do not recite any additional elements sufficient to provide a practical application/significantly more.
Claims 6-7 and 19 further describes the first information that is determined, however do not recite any additional elements sufficient to provide a practical application/significantly more.
Claims 8 and 20 describe the use of HIPAA compliant network, however do not recite any additional elements sufficient to provide a practical application/significantly more.
Claims 9-12 further describes the content of the first patient clinical data, however do not recite any additional elements sufficient to provide a practical application/significantly more
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) 1-2, 5-11, 13-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2019/0201123 (hereafter “Shelton”), in view of U.S. Patent No. 9,788,907 (hereafter “Alvi”), in further view of U.S. Patent Pub. No. 2021/0398650 (hereafter “Baker”).
Regarding (Currently Amended) claim 1, Shelton teaches a surgical computing system (Shelton: Figure 1, paragraph [0009], “A surgical feedback system”), the surgical computing system comprising:
--computing memory; and a processor communicatively coupled with the computing memory, (Shelton: Figure 3, paragraph [0207], “a processor module 132, and a storage array 134”, paragraph [0248], “The processor 244 is coupled to a communication module 247, storage 248, memory 249, non-volatile memory 250”) the processor configured to: […];
--receive, from the at least one of the surgical hub or the data system on the local data network, a second collection of unredacted data associated with a second surgical procedure (Shelton: paragraph [0009], “a surgical hub configured to communicably couple to the data source and the surgical instrument”, paragraph [0234], “Data associated with the devices 1a-1n may also be transferred to the local computer system 210 for local data processing and manipulation”, paragraph [0330], “patterns of treatment can include, for example, the type(s) of surgical instrument(s) to use during the procedure, additional procedure(s) to be performed, and/or any concerns that require further monitoring. Data collected during each particular procedure, the treatment performed, and/or the outcome of the particular procedure can be stored in a data bank (e.g., remote server 113) for future analysis”);
--save in the computing memory the second collection of unredacted data (Shelton: paragraphs [0263]-[0265], “storing, manipulating, and communicating data generated based on the operation of various surgical systems. As shown in FIG. 12, access to the cloud 7004 is achieved via the network 7001… aggregate data from specific data generated by various surgical instruments 7012 and their corresponding hubs 7006. Such aggregated data may be stored”, paragraph [0330], “Data collected during each particular procedure, the treatment performed, and/or the outcome of the particular procedure can be stored in a data bank (e.g., remote server 113) for future analysis”);
--train a machine learning (ML) model for optimizing clinical outcome effectiveness using the first collection of unredacted data and the second collection of respective data (Shelton: paragraph [0281], “a machine learning system can be trained to accurately derive contextual information regarding a surgical procedure from the provided inputs”, paragraph [0311], “machine learning analyses of performance and outcomes recorded over more than one procedure”, paragraph [0316], “given a set of training examples”, paragraph [0334], “goal of optimizing an outcome of a surgical procedure”, paragraph [0354], “The surgical hub 206 to create a mathematical model 250840 based off of the previously collected data marked as the training data set 250810”);
--generate first information that optimizes a clinical outcome of a third surgical procedure using the trained ML model (Shelton: Figure 20, paragraph [0275], “predictively model the effects of recommendations on the cost and effectiveness”, paragraph [0335]-[0338], “After receiving the collected structural data, the surgical hub 206 analyzes the collected data against a stored set of structural data… the surgical hub 206 is configured to recommend an action. The recommended action(s) can be directed toward the clinician in the form of a prompt”, paragraph [0341]-[0342], “the processor 244 develops a recommendation to optimize an outcome of the surgical procedure… machine learning technique can allow for the determination of relationships between data pairs and/or the identification of a complication resulting from the performed surgical procedure”); and
--send the first information to a first surgical hub from the at least one of the surgical hub or the data system (Shelton: Figure 20, paragraph [0206], “The hub 106 is also configured to coordinate information flow to a display”, paragraph [0269]-[0270], “display screens that display data or recommendations provided by the cloud 7004… the recommendations module 7030 could transmit recommendations to a surgical hub 7006”. Also see, Figure 2, 9, paragraph [0245]),
the first information comprising computer executable instructions […], the trained ML model executable by the surgical hub to, upon conditions of a surgical procedure at the first surgical hub matching a stored pattern in the trained ML model, and a surgical device becoming linked to the surgical hub, forward the computer executable instructions to the surgical device for execution by the surgical device, the computer executable instructions causing a control program of the surgical device to be modified (Shelton: Figures 1-3, 8-13, 20, paragraph [0009], “A surgical feedback system is disclosed. The surgical feedback system comprises a surgical instrument, a data source, and a surgical hub configured to communicably couple to the data source and the surgical instrument”, paragraph [0024], “a plurality of smart surgical instruments coupled to surgical hubs that may connect to the cloud component of the cloud computing system”, paragraph [0192], “as illustrated in FIG. 1, the surgical system 102 includes a visualization system 108, a robotic system 110, and a handheld intelligent surgical instrument 112, which are configured to communicate with one another and/or the hub 106”, paragraphs [0263]-[0265], “Surgical instruments 7012 are paired with the surgical hubs 7006 for control and implementation of various surgical procedures or operations… surgical instruments 7012 may comprise transceivers for data transmission to and from their corresponding surgical hubs… host and supply shared capabilities to software applications (e.g. hub applications) executed by surgical hubs 7006”, paragraphs [0269]-[0271], “specific recommendations regarding parameters or configurations of various surgical instruments 7012 can also be provided… control program updating module 7026 could be configured to implement various surgical instrument 7012 recommendations when corresponding control programs are updated. For example, the patient outcome analysis module 7028 could identify correlations linking specific control parameters with successful (or unsuccessful) results. Such correlations may be addressed when updated control programs are transmitted to surgical instruments 7012 via the control program updating module 7026. Updates to instruments 7012 that are transmitted via a corresponding hub 7006”, paragraph [0281], “The situational awareness system of the surgical hub 5104… includes a further machine learning system, lookup table, or other such system, which generates or retrieves one or more control adjustments for one or more modular devices 5102 when provided the contextual information as input”, paragraph [0294], “adjust modular devices based on the context (e.g., activate monitors, adjust the field of view (FOV) of the medical imaging device, or change the energy level of an ultrasonic surgical instrument or RF electrosurgical instrument)”, paragraphs [0310]-[0313], “control programs of OR systems can be adjusted based on a remote analysis involving supervised and/or unsupervised learning techniques… the analysis could be done within the local network of facility-linked devices or could be exported to a remote location for compilation and returned to the network”, paragraph [0330], “Patterns of treatment may be recognized”, paragraph [0334], “machine learning analysis can be performed locally at the surgical hub level”, paragraph [0342], “the surgical hub 206 is configured to automatically update a surgical instrument's settings based on the unsupervised machine learning analysis”. Also see, paragraphs [0383]-[0385]); […];
--send the […] first information to a cloud system outside the local data network (Shelton: Figure 9, 12, paragraph [0192], “a cloud-based system”, paragraphs [0234]-[0235], “Data associated with the devices 1a-1n may be transferred to cloud-based computers via the router for remote data processing and manipulation”, paragraph [0275], “predictively model the effects of recommendations on the cost and effectiveness”).
Shelton may not explicitly teach (underlined below for clarity): --receive, from at least one of the surgical hub or the data system on a local data network, a first collection of unredacted data associated, with a first surgical procedure, wherein the first collection of unredacted data includes first patient personal data, first patient clinical data, and first other patient data;
--save in the computing memory the first collection of unredacted data;
--redact patient data from the first information to form redacted first information; send the redacted first information to a cloud system outside the local data network.
Alvi teaches receive, from at least one of the surgical hub or the data system on a local data network, a first collection of unredacted data associated, with a first surgical procedure, wherein the first collection of unredacted data includes first patient personal data, first patient clinical data, and first other patient data (Alvi: Figure 8, Column 3, lines 20-40, “systems and methods used to process live data to identify a real-time stage of a surgery… A surgical data structure, that represents various stage in a surgery and corresponding information, can be locally retrieved or received. During a surgery, live data can be collected… Procedural metadata associated with the state… can be retrieved”, Column 5, lines 10-30, “The local server 170 receives the live data from the wireless hub 160 over a connection 162”, Column 7, lines 10-25, “access to data such as patient records”. Also see, Column 4, lines 40-60, Column 26, lines 20-30);
--save in the computing memory the first collection of unredacted data (Alvi: Column 4, lines 35-55, “the data may be stored”, Column 5, lines 35-55, “surgical procedures, described using surgical data structures, may be stored”);
--redact patient data from the first information to form redacted first information; send the redacted first information to a cloud system outside the local data network (Alvi: Column 3, line65-Column 4, line 25, “The live surgical data may be encrypted, as necessary, in order to protect the privacy of individually identifiable health information in compliance with the Health Insurance Portability and Accountability Act of 1996. Third, a processing device may receive the live surgical data. The processing device can be located in or near a surgery location or in the cloud”. Also see, Column 4, line 40-45).
One of ordinary skill in the art before the effective filing date would have found it obvious to include receiving and using a first collection of unredacted data associated with a first procedure and encrypting data to remove identifying features as taught by Alvi within the training and generating of information to optimize clinical outcome and cost effectiveness of a surgical procedure as taught by Shelton with the motivation of “improve procedural consistency across entities involved in performance of a surgery” (Alvi: Column 5, lines 1-5).
Shelton and Alvi may not explicitly teach (underlined below for clarity):
--send the first information to a first surgical hub from the at least one of the surgical hub or the data system, the first information comprising computer executable instructions and the trained ML model, the trained ML model executable by the surgical hub to, upon conditions of a surgical procedure at the first surgical hub matching a stored pattern in the trained ML model, and a surgical device becoming linked to the surgical hub, forward the computer executable instructions to a surgical device for execution by the surgical device;
Baker teaches send the first information to a surgical hub from the at least one of the surgical hub or the data system, the first information comprising computer executable instructions and the trained ML model, the trained ML model executable by the surgical hub to, upon conditions of a surgical procedure at the first surgical hub matching a stored pattern in the trained ML model, and a surgical device becoming linked to the surgical hub, forward the computer executable instructions to a surgical device for execution by the surgical device (Baker: Figures 1-3, 7, 11. Paragraph [0022], “inference engine enables distributed execution of AI models and algorithms at different locations of a distributed computing system, such as among a cloud platform, on-premise hardware, client computing systems, and the like”, paragraph [0026], “the medical imaging procedure data can be captured and communicated to the subject AI models, which are executed on-premise”);
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a cloud-network to distribute models as taught by Baker within the surgical hub cloud network for remote or local processing using a machine learning model as taught by Shelton with the motivation of “improve the accuracy and operation of the processing algorithm 234” (Baker: paragraph [0047]).
Regarding (Currently Amended) claim 2, Shelton, Alvi and Baker teach the limitations of claim 1, and further teach wherein the processor is further configured to: receive, from the at least one of the surgical hub or the data system, a request for the first information that optimizes the clinical outcome of the third surgical procedure, wherein in response the processor is further configured to send the first information to the surgical hub (Shelton: Figure 20, paragraph [0264]-[0265], “monitoring requests by client surgical hubs 7006 and managing the processing capacity of the cloud 7004 for executing the requests… For example, the hub application servers 7002 may manage requests made by the hub applications through the hubs 7006, control access to the aggregated medical data databases 7011, and perform load balancing”, paragraph [0275], “predictively model the effects of recommendations on the cost and effectiveness”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 5, Shelton, Alvi and Baker teach the limitations of claim 1, and further teach wherein the first surgical procedure, the second surgical procedure, and the third surgical procedure are a same type of surgical procedure (Shelton: paragraph [0280], “The contextual information inferred from the received data can include, for example, the type of surgical procedure being performed”, paragraph [0290]-[0291], “determine the type of surgical procedure being performed, retrieve the corresponding checklists, product location, or setup needs (e.g., from a memory), and then… compare the steps being performed or the equipment being used during the course of the surgical procedure to the expected steps or equipment for the type of surgical procedure that the surgical hub 5104 determined is being performed”. Also see, paragraphs [0316] and [0330]. The Examiner notes the retrieved data is for the same type of procedure),
--wherein the first surgical procedure and the second surgical procedure are past surgical procedures, and wherein the third surgical procedure is a future surgical procedure (Shelton: paragraph [0010], “the stored set of data comprises data collected during previous surgical procedures… to optimize an outcome of a surgical procedure”. Also see, paragraph [0193], [0280]. The Examiner notes the outcome for a procedure reads on a procedure in the future under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 6, Shelton, Alvi and Baker teach the limitations of claim 1, and further teach wherein the first information that optimizes the clinical outcome of the third surgical procedure includes one or more of aspects of a surgical procedure plan associated with the third surgical procedure, such as a surgical choice, a surgical instrument selection, or a post-surgery care choice (Shelton: paragraph [0285], “the type of procedure being performed can affect the optimal energy level for an ultrasonic surgical instrument or radio frequency (RF) electrosurgical instrument to operate a”, paragraph [0346], “A suitable staple cartridge is selected based on the condition of the patient and/or the condition of the lung tissue to be stapled by the surgical stapler”. Also see, paragraph [0278]).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 7, Shelton, Alvi and Baker teach the limitations of claim 6, and further teach wherein the first information that optimizes the clinical outcome of the third surgical procedure further includes an operational parameter adjustment for a surgical instrument associated with the surgical instrument selection (Shelton: paragraphs [0283]-[0285], “the type of tissue being operated can affect the adjustments that are made to the compression rate and load thresholds of a surgical stapling and cutting instrument for a particular tissue gap measurement… type of body cavity being operated in during an insufflation procedure can affect the function of a smoke evacuator… the type of procedure being performed can affect the optimal energy level for an ultrasonic surgical instrument or radio frequency (RF) electrosurgical instrument to operate at”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 8, Shelton, Alvi and Baker teach the limitations of claim 1, and further teach wherein the surgical computing system is located on the local data network, and where the local data network is within a boundary protected by health insurance portability and accountability act (HIPAA) data rules (Shelton: paragraph [0233]-[0234], “The modular communication hub 203 also can be coupled to a local computer system 210 to provide local computer processing and data manipulation”, paragraph [0264], “operating theaters in healthcare facilities (e.g., hospitals), for providing medical operations.”, paragraph [0313], “the analysis could be done within the local network of facility-linked devices”. The Examiner notes the local network of a hospital is HIPAA protected under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 9, Shelton, Alvi and Baker teach the limitations of claim 1, and further teaches each of the first patient personal data, the first patient clinical data, and the first other patient data includes a patient identifier (Baker: paragraph [0032], “The metadata within each imaging data file may include identification information such as a patient identifier and an identifier of the series of images, in addition to information about the type of imaging modality and the techniques used to obtain the images”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 10, Shelton, Alvi and Baker teach the limitations of claim 1, and further teach wherein the first patient personal data includes one or more of demographics information, such as age, gender, place of residence, occupation, or family status (Alvi: Column 26, lines 20-30, “Patient information such as age, weight, prior surgical interventions and health history may further inform the calculation of surgical risk”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 11, Shelton, Alvi and Baker teach the limitations of claim 1, and further teach wherein the first patient clinical data includes one or more of pre-surgery data, in-surgery data, and post-surgery data (Alvi: Figure 8, Column 24, lines 5-50, “live surgical data is received at block 804”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 13, Shelton, Alvi and Baker teach the limitations of claim 1, and further teach wherein second information that optimizes the clinical outcome of the third surgical procedure is received from a cloud computing system that is coupled with the surgical computing system, and wherein the trained ML model is a part of the second information (Shelton: Figure 9, 12, paragraph [0192], “a cloud-based system”, paragraphs [0234]-[0235], “Data associated with the devices 1a-1n may be transferred to cloud-based computers via the router for remote data processing and manipulation”, paragraph [0275], “predictively model the effects of recommendations on the cost and effectiveness”, paragraph [0281], “includes a further machine learning system, lookup table, or other such system, which generates or retrieves one or more control adjustments for one or more modular devices”, paragraph [0313], “the machine learning analysis could be based on supervised or unsupervised learning. In one aspect, the analysis could be done within the local network of facility-linked devices”, paragraph [0328], “automatically update their settings based on the correlations learned by a machine learning performed by the cloud-based system”. The Examiner notes the algorithm is developed in the cloud and deployed locally which teaches what is required of the claim under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
REGARDING CLAIM(S) 14-15 and 18-20
Claim(s) 14-15 and 18-20 is/are analogous to Claim(s) 1-2, 5-6 and 8, thus Claim(s) 14-15 and 18-20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1-2, 5-6 and 8.
Claim(s) 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2019/0201123 (hereafter “Shelton”), U.S. Patent No. 9,788,907 (hereafter “Alvi”) and U.S. Patent Pub. No. 2021/0398650 (hereafter “Baker”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 2021/0005321 (hereafter “Hwang”).
Regarding (Previously Presented) claim 12, Shelton, Alvi and Baker teach the limitations of claim 1, but may not explicitly teach:
--wherein the first other patient data includes one or more of billing data associated with the first surgical procedure, payment data associated with the first surgical procedure, or reimbursement data associated with the first surgical procedure.
Hwang teaches wherein the first other patient data includes one or more of billing data associated with the first surgical procedure, payment data associated with the first surgical procedure, or reimbursement data associated with the first surgical procedure (Hwang: Figure 1, paragraph [0022], “The inputs may include medical history information 10, test results 20, social factors 30, medical insurance information 40”, paragraph [0026], “The medical insurance information 40 may include types of medical insurance coverage, prescription information, payment plans, claims history, pre-existing conditions, Medicare information, pre-authorizations, qualifications, and other types of information relating to the medical insurance of a patient under consideration”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using insurance information as taught by Hwang with use of patient information to train a machine learning model as taught by Shelton, Alvi and Baker with the motivation of “improve the accuracy of the predictions relative to complications that may likely occur for a given procedure” (Hwang: paragraph [0056]).
Response to Arguments
Applicant's arguments filed on 16 April 2025 have been fully considered but they are not persuasive. Applicant's arguments will be addressed herein below in the order in which they appear in the response filed on 16 April 2025.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claims 1-2, 5-15, and 18-20, the Examiner has considered the applicant's arguments; however, the arguments are not persuasive in view of the new grounds of rejection as necessitated by amendment. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons:
Applicant argues:
The cloud in Shelton may be accessed to provide data and to provide access to applications. But Shelton does not disclose "sending ... first information comprising computer executable instructions and the trained ML model." Moreover, the cloud in Shelton does not… The distributed processing of an AI mode in Baker certainly does not disclose "forward[ing] the computer executable instructions to the surgical device for execution by the surgical device, the computer executable instructions causing a control program of the surgical device to be modified," as in the claim… Therefore, because the cited reference does not disclose or suggest at least the above emphasized claim language, the reference does not anticipate the combination recited in claim 1.
The Examiner respectfully disagrees.
It is respectfully submitted, it is the combination of Baker within Shelton that teaches the argued limitation, in particular Shelton explicitly teaches that when an instrument is coupled (i.e., linked; see above but at least Figures 1, 12, paragraphs [0024] and [0192]) to a hub, a setting on the surgical instrument is updated via control programs (i.e., computer executable instructions; see above but at least [0269]-[0271] and [0342]), although Shelton may not explicitly recite sharing of the machine learning algorithm this is taught by Baker (see above, but at least Figure 1, paragraph [0022]) teaches distributing a machine learning algorithm (i.e., sending a trained model), and would be prima facie obvious to include within teachings of Shelton with the motivation of “improve the accuracy and operation of the processing algorithm 234” (Baker: paragraph [0047]).
In addition, the Examiner respectfully notes that the cited reference was never applied as a reference under 35 U.S.C. 102 against the pending claims. As such, the Examiner respectfully submits that the issue at hand is not whether the applied prior art specifically teaches the claimed features, per se, but rather, whether or not the prior art, when taken in combination with the knowledge of average skill in the art, would put the artisan in possession of these features. Regarding this issue, it is well established that references are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969). The issue of obviousness is not determined by what the references expressly state but by what they would reasonably suggest to one of ordinary skill in the art, as supported by decisions in In re DeLisle 406 Fed 1326, 160 USPQ 806; In re Kell, Terry and Davies 208 USPQ 871; and In re Fine, 837 F.2d 1071, 1074, 5 USPQ 2d 1596, 1598 (Fed. Cir. 1988) (citing In re Lalu, 747 F.2d 703, 705, 223 USPQ 1257, 1258 (Fed. Cir. 1988)). Further, it was determined in In re Lamberti et al, 192 USPQ 278 (CCPA) that:
(i) obviousness does not require absolute predictability;
(ii) non-preferred embodiments of prior art must also be considered; and
(iii) the question is not express teaching of references, but what they would suggest.
According to In re Jacoby, 135 USPQ 317 (CCPA 1962), the skilled artisan is presumed to know something more about the art than only what is disclosed in the applied references. In In re Bode, 193 USPQ 12 (CCPA 1977), every reference relies to some extent on knowledge of persons skilled in the art to complement that which is disclosed therein.
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-2, 5-15, and 18-20, the Examiner has considered the Applicant's arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Claim 1 is directed to a "surgical computing system," which is NOT managing personal behavior or interaction between people, as alleged. Moreover, the claimed surgical computing system is configured to operate in a specific manner that does not involve organizing humans or managing personal behavior… it is not "organizing human activity" or "managing personal behavior" to "receive, from at least one of a surgical hub or a data system on a local data network… and "store in the computing memory… Likewise, it is not "organizing human activity" or "managing personal behavior" to "receive… and "store… it is not possible for a human to "train a machine learning (ML) model ... " Likewise, it is not possible for a human to "generate first information ... using the trained ML model… The claimed surgical computing system is not a collection of generic hardware components. On the contrary, the claim recites a particular surgical system configured to operate in a specific manner… Therefore, because the specific requirements of the claims demonstrate the claim is not a collection of generic hardware and does not recite a mental process, the claim is not directed to an abstract idea.
The Examiner respectfully disagrees.
It is respectfully submitted, that no particular hardware is claimed, the claims and Applicant’s specification (see at least Figure 4, paragraphs [0060]-[0074]), describe that the claimed hardware amounts to generic off-the-shelf well-understood, routine and conventional computer components that are being used to apply the abstract idea. The claims under the broadest reasonable interpretation are directed toward collection of data about surgical procedures performed by humans, organization of this collected data to construct and use a model, and providing a result of the organization to the human user for the human user to use, which as stated in 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”. The claim is directed toward the abstract idea of certain methods of organizing human activity.
Applicant argues that limitations are “not "organizing human activity" or "managing personal behavior"”, however all of these argued limitations, are limitations which have already been considered to be additional elements to the identified abstract idea and found to be well-understood, routine and conventional and either extra-solution activity and/or generally linking the abstract idea to particular technological environment. Applicant does not argue any technical solutions to a technical problem recited in Applicant’s specification nor do they argue any improvements to the functionality of a computer. All of the identified limitations may be additional elements but generic well-known use of transmission/storage of data between generic computer components, to train/use a generic well-understood, routine and conventional machine learning model, does not improve the performance of the computer nor does it recite a technical solution to a technical problem recited in Applicant’s specification, therefore the claim is not subject matter eligible and the argument is unpersuasive.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.L./ Examiner, Art Unit 3684
/RAJESH KHATTAR/ Primary Examiner, Art Unit 3684