DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Rejections under 35 USC 101
Applicant's arguments filed 03/02/2026 with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues that the amended claims are not directed to an abstract idea/mental process because they contain limitations that cannot be practically performed in the human mind, including generating a control signal, sending and receiving communications with a surgical instrument, etc. Applicant also argues that the claimed invention reflects an improvement in technology or a technical field by filtering data to a lower data set which may allow the neural network to filter out the collection or storage of future data to a smaller dataset to limit unrelated data.
Examiner respectfully disagrees and argues that the limitations cited by Applicant that cannot be practically performed in the human mind are directed to extra-solution activity of data gathering or necessary data output of the judicial exception. Neither of these are enough to integrate the abstract idea into practical application.
MPEP 2106.05(a) states that: ‘It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)).’
Examiner asserts that the claimed invention as currently presented relies upon the judicial exception to provide the improvement, wherein the judicial exception is the abstract idea/mental process of making an observation, evaluation, and/or judgement. Therefore, the claimed invention does not provide improvement to a technology or technical field and is not patentable under 35 USC 101.
Further grounds of rejection of the amended claims under 35 USC 101 are detailed below.
Rejections under 35 USC 102/103
Applicant’s arguments, see Remarks dated 03/02/2026, with respect to the rejection(s) of claims 1-20 under 35 USC 102/103 in view of Farley have been fully considered and are persuasive. Farley fails to disclose all of the limitations of the amended claims, including generating an initial control signal for performing a surgical task and generating an updated control signal for updating the performance of the surgical task using the second data set. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Shelton, IV et al (US 20210196386 A1). New grounds of rejection detailed below.
Information Disclosure Statement
The Information Disclosure Statements (IDS) dated 10/11/2024 and 03/02/2026 have been considered by the Examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 8, and 15 are directed to the abstract idea/mental process of determining a second data set based on an evaluation from a first data set and generating a control signal based on the second data set.
Step 1
Claim 1 recites a machine, claims 8 and 15 recite a method.
Step 2A, Prong 1
Claims 1, 8, and 15 recite the limitations of generating an initial control signal for performing a surgical task,
evaluating a first data set for performing the surgical task,
based on the evaluation of the first data set for performing the surgical task, filtering data from the first data set to determine a second data set for updating the performance of the surgical task determine the second data set for updating the performance of the surgical task, wherein the second data set has a lower amount of data than the first data set; and
generating an updated control signal for updating the performance of the surgical task using the second data set.
These steps, under their broadest reasonable interpretation can be practically performed in the human mind and are thereby considered to be directed to an abstract idea/mental process. A human could give a control signal to an instrument, evaluate a first data set for performing the surgical task, and filter data from the first data set into a second data set which has a lower amount of data than the first data set by discarding irrelevant data, and giving an updated control signal based on the filtered, smaller second data set comprising relevant data.
Step 2A, Prong 2
Claims 1, 8, and 15, do not include any additional elements that integrate the abstract idea into a practical application.
Claims 1, 8, and 15 include the additional elements of a processor,
receiving a first data set for performing the surgical task from the surgical instrument,
a neural network trained to filter the data from the first data set, and
outputting the second data set for updating the performance of the surgical task.
The additional element of a processor merely amounts to generic computer implementation of the abstract idea.
Receiving a first data set for performing the surgical task from the surgical instrument amounts to insignificant extra-solution activity of data gathering, in the form of performing clinical tests, in this case a first data set for performing a surgical task, to obtain input for an equation, wherein the equation is the determination of the second data set for updating the performance of the surgical task. See MPEP 2106.05(g), In re Grams, 888 F.2d 835.
The neural network trained to filter data from the first data set is also generic computer implementation of the abstract idea, wherein the neural network is generically used to filter out irrelevant data from the first data set in order to produce a more concise second data set.
Outputting the second data set for updating the performance of the surgical task is extra-solution activity that merely amounts to necessary data output. See MPEP 2106.05(g), Mayo, 566 U.S. at 79, 101 USPQ2d at 1968.
Step 2B
Claims 1, 8, and 15 do not include any additional elements that amount to significantly more than the abstract idea.
Claims 1, 8, and 15 include the additional elements of a processor, receiving a first data set for performing the surgical task from the surgical instrument, a neural network trained to filter the data from the first data set, and outputting the second data set for updating the performance of the surgical task, which have been discussed in Step 2A, Prong 2 above. Additionally, the additional elements of the processor and neural network (machine learning model) can be held to be well-understood, routine, and conventional in the art; and they are claimed with a high level of generality which does not amount to significantly more than the abstract idea itself.
Claims 2-7, 9-14, and 16-20 amount to further defining the abstract idea itself.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shelton, IV et al (US 20210196386 A1), hereinafter referred to as Shelton.
Regarding claims 1 and 8, Shelton teaches a surgical computing device (see [0212]; situational awareness system which may be executed by any of surgical hubs 2106, 2236, 2404 in surgical systems 2100, 2200, 2400), comprising:
a processor (2232) configured to perform a method associated with a surgical computing device, and:
generate an initial control signal for performing a surgical task; send, to a surgical instrument, the initial control signal for performing the surgical task (see [0220]; situationally aware surgical hub 2404 could determine what step of the surgical procedure is being performed or will subsequently be performed and then update the control algorithms for the generator and/or ultrasonic surgical instrument or RF electrosurgical instrument to set the energy level at a value appropriate for the expected tissue type according to the surgical procedure step);
receive, from the surgical instrument, a first data set for performing the surgical task (see [0214]; surgical hub receives data about the surgical procedure being performed based on data received from various data sources 2426);
evaluate the first data set for performing the surgical task (see [0215]; the surgical hub derives or infers information related to the surgical procedure from received data in a processes referred to as situational awareness);
based on the evaluation of the first data set for performing the surgical task,
filter data from the first data set to determine a second data set updating the performance of the surgical task via a neural network (see [0216]; the situational awareness system includes a pattern recognition system, or machine learning system, e.g. an artificial neural network, that has been trained on training data to correlate various inputs from data sources 2426 to corresponding contextual information regarding a surgical procedure),
wherein the neural network is trained to filter the data from the first data set to determine the second data set for updating the performance of the surgical task (see [0216]; correlate various inputs from data sources 2426 to corresponding contextual information regarding a surgical procedure, wherein the corresponding contextual information is considered to be the second data set); and
wherein the second data set has a lower amount of data than the first data set (see [0215]; contextual information inferred from the received data can include, for example, the type of surgical procedure being performed, the particular step of the surgical procedure that the surgeon is performing, the type of tissue being operated on, or the body cavity that is the subject of the procedure);
output the second data set for updating the performance of the surgical task (see [0215]; inferred contextual information);
generate an updated control signal for updating the performance of the surgical task using the second data set (see [0216]; the contextual information received by the situational awareness system of the surgical hub 2404 is associated with a particular control adjustment or set of control adjustments for one or more modular devices 2402); and
send, to the surgical instrument, the updated control signal for updating the performance of the surgical task using the second data set (see [0214]; the surgical hub is configured to infer information about the surgical procedure from received data and then control the modular devices paired to the surgical hub based upon the inferred context of the surgical procedure).
Regarding claims 2 and 9, Shelton teaches the device of claim 1 and the method of claim 8, wherein:
the neural network is trained to identify patterns and trends within the first data set to determine key areas within the first data set (see [0216]; the situational awareness system includes a pattern recognition system, or machine learning system, e.g. an artificial neural network, that has been trained on training data to correlate various inputs from data sources 2426 to corresponding contextual information regarding a surgical procedure); and
filtering the data from the first data set to determine the second data set for updating the performance of the surgical task is based on the determined key areas within the first data set (see [0215]; contextual information inferred from the received data can include, for example, the type of surgical procedure being performed, the particular step of the surgical procedure that the surgeon is performing, the type of tissue being operated on, or the body cavity that is the subject of the procedure, [0214]; the surgical hub is configured to infer information about the surgical procedure from received data and then control the modular devices paired to the surgical hub based upon the inferred context of the surgical procedure).
Regarding claims 3 and 10, Shelton teaches the device of claim 1 and the method of claim 8, wherein:
the neural network is trained to determine personalized or individualized data within the first data set (see [0216]; the situational awareness system includes a pattern recognition system, or machine learning system, e.g. an artificial neural network, that has been trained on training data to correlate various inputs from data sources 2426, including patient monitoring devices 2424 and modular devices 2402, to corresponding contextual information regarding a surgical procedure); and
filtering the data from the first data set to determine the second data set for updating the performance of the surgical task is based on the determined personalized or individualized data within the first data set (see [0215]; contextual information inferred from the received data can include, for example, the type of surgical procedure being performed, the particular step of the surgical procedure that the surgeon is performing, the type of tissue being operated on, or the body cavity that is the subject of the procedure, [0214]; the surgical hub is configured to infer information about the surgical procedure from received data and then control the modular devices paired to the surgical hub based upon the inferred context of the surgical procedure).
Regarding claims 4 and 11, Shelton teaches the device of claim 1 and the method of claim 8, wherein the neural network is trained to pre-identify the filtered data from the first data set (see [0216]; the situational awareness system can include a lookup table storing pre-characterized contextual information regarding a surgical procedure in association with one or more inputs or ranges of inputs corresponding to the contextual information).
Regarding claims 5 and 12, Shelton teaches the device of claim 4 and the method of claim 11, wherein the filtered data that is pre-identified from the first data set is a minimum amount of surgical data needed to perform the surgical task (see [0216]; in response to a query with one or more inputs, the lookup table can return the corresponding contextual information for the situational awareness system for controlling the modular devices 2402).
Regarding claims 6 and 13, Shelton teaches the device of claim 1 and the method of claim 8, wherein:
the neural network is trained to identify a less invasive combination of surgical data within the first data set (see [0216]; the situational awareness system can include a lookup table storing pre-characterized contextual information regarding a surgical procedure in association with one or more inputs or ranges of inputs corresponding to the contextual information); and
filtering the data from the first data set to the less invasive combination of surgical data, wherein the less invasion combination of surgical data is the second data set (see [0216]; in response to a query with one or more inputs, the lookup table can return the corresponding contextual information for the situational awareness system for controlling the modular devices 2402; it can be appreciated that the pre-characterized contextual information which can be quickly associated with one or more inputs is identified as the less invasive combination of surgical data, which is the second data set and provides contextual information which the surgical hub uses to control the paired modular devices).
Regarding claims 7 and 14, Shelton teaches the device of claim 6 and the method of claim 13, wherein the less invasive combination of surgical data comprises surgical data that:
has a lower processing capacity, or has a lower memory capacity (see [0215]; contextual information inferred from the received data can include, for example, the type of surgical procedure being performed, the particular step of the surgical procedure that the surgeon is performing, the type of tissue being operated on, or the body cavity that is the subject of the procedure, it can be appreciated that the inferred contextual information requires a lower processing capacity and/or a lower memory capacity than the received input data from multiple data sources 2426).
Regarding claim 15, Shelton teaches a method associated with a surgical computing device (see [0212]; situational awareness system which may be executed by any of surgical hubs 2106, 2236, 2404 in surgical systems 2100, 2200, 2400), comprising:
generating an initial control signal for performing a surgical task; sending, to a surgical instrument, the initial control signal for performing the surgical task (see [0220]; situationally aware surgical hub 2404 could determine what step of the surgical procedure is being performed or will subsequently be performed and then update the control algorithms for the generator and/or ultrasonic surgical instrument or RF electrosurgical instrument to set the energy level at a value appropriate for the expected tissue type according to the surgical procedure step);
receiving, from the surgical instrument, a first data set for performing the surgical task (see [0214]; surgical hub receives data about the surgical procedure being performed based on data received from various data sources 2426);
training a neural network with data associated with the first data set (see [0216]; an artificial neural network has been trained on training data from data sourced 2426 including databases 2422, patient monitoring devices 2424, and/or modular devices 2402);
based on an evaluation of the first data set performing the surgical task (see [0215]; the surgical hub derives or infers information related to the surgical procedure from received data in a processes referred to as situational awareness, [0216]; the situational awareness system includes a pattern recognition system, or machine learning system, e.g. an artificial neural network, that has been trained on training data to correlate various inputs from data sources 2426 to corresponding contextual information regarding a surgical procedure),
inputting the data associated with the first data set to the neural network to filter the data from the first data set to determine a second data set for updating the performance of the surgical task wherein the second data set has a lower amount of data than the first data set (see [0215]; contextual information inferred from the received data can include, for example, the type of surgical procedure being performed, the particular step of the surgical procedure that the surgeon is performing, the type of tissue being operated on, or the body cavity that is the subject of the procedure);
outputting the second data set for updating the performance of the surgical task (see [0215]; inferred contextual information);
generating an updated control signal for updating the performance of the surgical task using the second data set (see [0216]; the contextual information received by the situational awareness system of the surgical hub 2404 is associated with a particular control adjustment or set of control adjustments for one or more modular devices 2402); and
sending, to the surgical instrument, the updated control signal for updating the performance of the surgical task using the second data set (see [0214]; the surgical hub is configured to infer information about the surgical procedure from received data and then control the modular devices paired to the surgical hub based upon the inferred context of the surgical procedure).
Regarding claim 16, Shelton teaches the method of claim 15, wherein:
the neural network is trained to identify patterns and trends within the first data set (see [0216]; the situational awareness system includes a pattern recognition system, or machine learning system, e.g. an artificial neural network, that has been trained on training data to correlate various inputs from data sources 2426 to corresponding contextual information regarding a surgical procedure); and
the inputted data from the first data set includes the identified patterns and trends, wherein the patterns and trends are used by the neural network to filter the data from the first data set to determine the second data set for updating the performance of the surgical task. (see [0215]; contextual information inferred from the received data can include, for example, the type of surgical procedure being performed, the particular step of the surgical procedure that the surgeon is performing, the type of tissue being operated on, or the body cavity that is the subject of the procedure, [0214]; the surgical hub is configured to infer information about the surgical procedure from received data and then control the modular devices paired to the surgical hub based upon the inferred context of the surgical procedure).
Regarding claim 17, Shelton teaches the method of claim 15, wherein:
the neural network is trained to identify personalized or individualized data within the first data set (see [0216]; the situational awareness system includes a pattern recognition system, or machine learning system, e.g. an artificial neural network, that has been trained on training data to correlate various inputs from data sources 2426, including patient monitoring devices 2424 and modular devices 2402, to corresponding contextual information regarding a surgical procedure); and
the inputted data from the first data set includes the personalized or individualized data, wherein the personalized or individualized data is used by the neural network to filter the data from the first data set to determine the second data set for updating the performance of the surgical task (see [0215]; contextual information inferred from the received data can include, for example, the type of surgical procedure being performed, the particular step of the surgical procedure that the surgeon is performing, the type of tissue being operated on, or the body cavity that is the subject of the procedure, [0214]; the surgical hub is configured to infer information about the surgical procedure from received data and then control the modular devices paired to the surgical hub based upon the inferred context of the surgical procedure).
Regarding claim 18, Shelton teaches the method of claim 15, wherein the neural network is trained to pre-identify the filtered data from the first data set (see [0216]; the situational awareness system can include a lookup table storing pre-characterized contextual information regarding a surgical procedure in association with one or more inputs or ranges of inputs corresponding to the contextual information).
Regarding claim 19, Shelton teaches the method of claim 18, wherein the filtered data that is pre-identified from the first data set is a minimum amount of surgical data needed to perform the surgical task (see [0216]; in response to a query with one or more inputs, the lookup table can return the corresponding contextual information for the situational awareness system for controlling the modular devices 2402).
Regarding claim 20, Shelton teaches the method of claim 15, wherein:
the neural network is trained to identify a less invasive combination of surgical data within the first data set (see [0216]; the situational awareness system can include a lookup table storing pre-characterized contextual information regarding a surgical procedure in association with one or more inputs or ranges of inputs corresponding to the contextual information); and
the inputted data from the first data set includes the less invasive combination of surgical data, wherein the less invasive combination of surgical data is used by the neural network to filter the data from the first data set to determine the second data set for updating the performance of the surgical task (see [0216]; in response to a query with one or more inputs, the lookup table can return the corresponding contextual information for the situational awareness system for controlling the modular devices 2402; it can be appreciated that the pre-characterized contextual information which can be quickly associated with one or more inputs is identified as the less invasive combination of surgical data, which is the second data set and provides contextual information which the surgical hub uses to control the paired modular devices).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Farley et al (US 20200243199 A1) which teaches methods and systems for providing an episode of care.
Donhowe et al (US 20220129822 A1) which teaches a system and method for detecting events during a surgery.
Nawana et al (US 20140081659 A1) which teaches systems and methods for surgical and interventional planning, support, post-operative follow-up, and functional recovery tracking.
McKinnon et al (US 20200000400 A1) which teaches technologies for intra-operative ligament balancing using machine learning.
Saur et al (US 20210286996 A1) which teaches a machine learning system for identifying a state of a surgery and assistance function.
Shelton, IV et al (US 20190201081 A1) which teaches a powered surgical tool with predefined adjustable control algorithm for controlling end effector parameters.
Daley et al (US 11158415 B2) which teaches a surgical procedure planning system with multiple feedback loops.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALISHA J SIRCAR whose telephone number is (571)272-0450. The examiner can normally be reached Monday - Thursday 9-6:30, Friday 9-5:30 CT.
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/A.J.S./Examiner, Art Unit 3792
/ALLEN PORTER/Primary Examiner, Art Unit 3796
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