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 .
Drawings
The drawings were received on 09/25/2024. These drawings are accepted.
Claim Objections
Claims 1-11 are objected to because of the following informalities: Claim 1, line 22 recites “automatic adjustments.”, the period should be replaced by a “;” semicolon. Per the MPEP, each claim is treated as one long unbroken sentence, and thus should only end with a period. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-11 are rejected 35 U.S.C. 103 as being unpatentable over Rabindran et al. (USPGPUB No. 2024/0252267 A1, hereinafter referred to as Rabindran) in view of Shelton et al. (USPGPUB No. 2023/0146947 A1, hereinafter referred to as Shelton).
Referring to Claim 1, Rabindran discloses a control system for managing and processing signals {control system “surgical systems… operator console 40”, see Figs. 1 and 2 [0056]} from a variety of input devices {“left and right input devices 41, 42”, see Figs. 1 and 2 [0062], 1st sentence} and providing different outputs comprising {“first manipulator system and a second manipulator system of the manipulator systems 120, 130, 140, 150 hold instruments”, see Figs. 1 and 2 [0059], 1st sentence}:
a management computing system {“[management computing system] patient-side system 250”, see Fig. 3 [0070], 1st sentence}, such as a Microcontroller Hub or a computer system {“patient-side system 250 for minimally invasive [computing system] computer-assisted teleoperated surgery or other medical procedures”, see Fig. 3 [0070], 1st sentence}, configured to receive and manage inputs from a plurality of input devices and peripherals {“translate and transfer the mechanical motion of input devices 41, 42”, see Figs. 1 and 2, [0063], 3rd sentence}, wherein the Management Computing System:
comprises one or more memory devices for storing instructions and processing data {“non-transitory machine-readable medium including a plurality of machine-readable instructions”, [0011]};
comprises software components for adjusting interactions of each sensing element {“first and second actuators may then adjust … [interactions] corresponding engagement members 632a and 632b”, see Figs. 15-18, [0121]}, or external device with various control parameters {Examiner’s note: recitation “or external device” portion treats this claim as a Markush claim, thus the reference need only disclose one member in the group to address the claim}, including those automatically adjusted based on external data or algorithmic inputs {“one or more instrument drive system actuators engage the corresponding one or more instrument engagement members and set a [external data] dynamic preload tension”, see Figs. 15-18, [0121], last sentence};
is configured to compile all inputs and produce certain outputs with parameters computed from the inputs {“forces or torques measured by each of the force and/or torque sensors may be [compiled] aggregated to”, see Fig. 21 [0147], last 4 sentences}, including but not limited to cursor speed, resolution, DPI settings, audio, images, visual outputs {“holds an image capturing device such as a monoscopic or stereoscopic endoscope.” That produce visual output, see Figs. 1 and 2 [0059]}, temperature, video, haptic feedback, robotic control {“When used for minimally invasive robotic surgery”, see Figs. 1 and 2 [0058], 1st sentence}, drone motion control, virtual motion for virtual reality (VR) or augmented reality (AR) or other control parameters {the recitation “or” term treats this claim as a Markush claim, thus the reference needs only disclose at least one element in the group to address the claim};
is further enabled to communicate bidirectionally with a variety of peripherals and/or external devices {“[external device] one or more instrument drive system actuators engage the corresponding one or more instrument engagement members and set a [external data] dynamic preload tension”, see Figs. 15-18, [0121], last sentence}”}, including those equipped with integrated microcontrollers for manipulating {“control unit 2010 is shown with only one processor 2020… one or more central processing units, multi-core processors, microprocessors, microcontrollers”, see Figs. 2 and 20 [0137]};
one or more electronic input devices or peripherals coupled to one or more users {“[input/output] A processor 43 is provided in the operator console 40 for control and other purposes”, see Figs. 1 and 2 [0063], 1st sentence} for controlling a function or functions in the management computing system {“processor 43 performs [controlling] various functions in the medical robotic system”, see Figs. 1 and 2 [0063], 2nd sentence};
wherein each of the input devices or peripherals optionally comprises:
one or more sensors capable of providing control data, including resistive, capacitive, optical, or magnetic sensors {“resistive, or capacitive switches, pressure transducers, strain gauges, [magnetic] Hall Effect sensors, RFID devices, and/or the like”, see Fig. 21 [0148], last sentence};
Rabindran does not appear to explicitly disclose
refining and optimizing settings, and output characteristics while also enabling real-time or automatic adjustments;
wherein each of the input devices or peripherals optionally comprises:
one or more memory devices for storing instructions:
one or more network interface cards;
an integrated microcontroller for processing input signals locally, while communicating data to the Management Computing System;
The system further configured to:
receive information from one or more sensors, peripherals, or external devises in analog or digital format.
Convert analog inputs into digital signals if needed:
Process the information and transmit it via one or more interfaces to the Management Computing System;
Optionally adjust control parameters automatically based on external conditions or algorithmic inputs, including environmental sensing or machine-learning-derived data.
The system optionally comprising a power supply for energizing the system, said power supply including but not limited to batteries, power adapters, or variations thereof.
However, Shelton discloses refining and optimizing settings {“can inform the situational awareness of a surgical system” (see Fig. 209 [1702] last sentence) that situational awareness including “related to functions or settings of any surgical device used during the surgical procedure” ([1442], last three sentences)}, and output characteristics while also enabling real-time or automatic adjustments {“real-time or near real-time to [adjust] inform the machine learning and/or decision-making process”, see Fig. 209 [1702], last two sentences };
wherein each of the input devices or peripherals optionally comprises {peripherals such as “secondary surgical display”, see Fig. 178B [1442]}:
one or more memory devices for storing instructions {“ such smart medical devices may transmit data related to their operating parameters” (see Fig. 178B [1442], last sentence) such operating parameters including instructions and storing instructions}:
one or more network interface cards {“ communication using a Transmission Control Protocol/IP.” ([2174], 3rd sentence) such communication performed by interface card “via a data port, which may be wired” by an appropriate card (see Fig. 150, [1298], 2nd sentence)};
an integrated microcontroller for processing input signals locally {“ A SoC integrates a microcontroller (or microprocessor) with advanced peripherals”, [0682], last two sentences}, while communicating data {communicating data “contextual information derived from received data”, see Fig. 82a, [1026], 1st sentence} to the Management Computing System { distributed computing system including at least one of the aforementioned cloud computing system 104 and/or a control circuit of a surgical hub 5104 in combination with a control circuit of a modular device, such as the [integrated] microcontroller 461”, see Fig. 82a [1026]};
The system further configured to {the system “surgical hub 5104 can be configured to derive a variety of other inferences”, see Fig. 83a, [1042}:
receive information from one or more sensors {“directly detecting whether the device is powered on, detecting whether there is a [receive information pressure differential between an ambient pressure sensor and a pressure sensor internal to the surgical site”, see Fig. 83b [1039], 3rd sentence}, peripherals, or external devises in analog or digital format {“an external or ambient pressure sensor Pi” measuring an analog format “pressure differential” until turn digital in post processing, see Fig. 83B [1044]}.
Convert analog inputs into digital signals if needed {“ measured force is converted to a digital signal and provided to the processor 462”, see Fig. 12, [0585], last sentence}:
Process the information and transmit it via one or more interfaces {“measured strain is converted to a digital signal and provided to a processor 462 of the microcontroller 461”, see Fig. 12, [0586]} to the Management Computing System {via the management computing system “cloud computing system 104”, see Fig. 82a [1026]};
Optionally adjust control parameters automatically based on external conditions or algorithmic inputs {“the inferences derived from the perioperative data received from different modular devices 5102 can be utilized to confirm and/or increase the confidence of prior inferences.”, see Figs. 83a and 83b, [1039], last two sentences}, including environmental sensing {Markush group } or machine-learning-derived data {“processed by a pattern recognition system or a machine learning system to recognize features”, see Figs. 83a and 83b, [1037], last two sentences}.
The system optionally comprising a power supply for energizing the system {“energy source 712 may be a DC power supply driven”, see Fig. 17 [0613], 2nd sentence}, said power supply including but not limited to batteries, power adapters, or variations thereof {“alternating current power source, a battery, a super capacitor, or any other suitable energy source”, see Fig. 17 [0613], 2nd sentence}.
Rabindran and Shelton are analogous because they are from the same field of endeavor, managing peripheral device communication(s).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rabindran and Shelton before him or her, to modify Rabindran’s “surgical systems” (see Figs. 1 and 2) incorporating Shelton’s “situational awareness of a surgical system” (see Fig. 209, [1702]) and appropriate machine learning functionality “real-time or near real-time to [adjust] inform the machine learning and/or decision-making process” (also [1702]).
The suggestion/motivation for doing so would have been to implement robotic surgical systems (Shelton [0004]) To improve patient practices, it would be desirable to find ways to help interconnect medical systems and facilities better (Shelton [0003], last sentence).
Therefore, it would have been obvious to combine Shelton with Rabindran to obtain the invention as specified in the instant claim(s).
As per claim 2, the rejection of claim 1 is incorporated and Shelton discloses wherein each input device is connected to the Management computing system via one or more dedicated one directional or bi directional data transmission means such as cables {“all peripherals using a standardized four-wire cable that provides both communication and power distribution”, see Fig. 11 [0569], 2nd sentence}, physical wired connection {“may interact through any suitable wired or wireless data communication network such as Bluetooth and WiFi. As used here, a ‘data communication network’ represents any number of physical, virtual, or logical components”, see Figs. 56-61 [0899], 2nd sentence}, optical and/or wireless communication, including but not limited to USB {“Wireless USB”, see Fig. 150, [1298], 3rd sentence}, Wi-Fi {“wireless data communication network such as Bluetooth and WiFi”, see Figs. 56-61, [0899], 3rd sentence}, Bluetooth, or infrared among others {Markush group }.
As per claim 3, the rejection of claim 2 is incorporated and Shelton discloses wherein the Management Computing System dynamically adjusts settings and control parameters for each connected input device {“real-time or near real-time to [adjust] inform the machine learning and/or decision-making process” (see Fig. 209 [1702], last two sentences) per input device within “surgical hubs 9000” as claimed (see Figs. 193 and 194, [1571], 2nd sentence}, peripheral or external device based on user inputs or automatic sensing, environmental data, or algorithmic processing {external device “hub 12224… the cloud 104 or the cloud 204… inform the [automatic sensing] machine-learning and decision-making processes related to the [settings] advanced energy parameters and/or mechanical control parameters”, see Figs. 212 and 213 [1722]}.
As per claim 4, the rejection of claim 3 is incorporated and Shelton discloses further comprising a user interface {“control system graphic user interface (GUI) and device control processor communicate and parameters are changed using the system.”, see Fig. 136, [1234] configured to dynamically modify settings and control parameters for each input device {“real-time or near real-time to [adjust] inform the machine learning and/or decision-making process” (see Fig. 209 [1702], last two sentences) per input device within “surgical hubs 9000” as claimed (see Figs. 193 and 194, [1571], 2nd sentence}, peripheral or external device including but not limited to DPI, cursor speed, resolution, haptic feedback {Markush group }, robotic control {“robotic surgical system can include a robotic control tower”, see Fig. 209 [1700], 1st sentence}, or virtual/augmented reality parameters {“augmented reality vision system”, see Fig. 129 [1193], 2nd sentence}.
As per Claim 5, the rejection of claim 4 is incorporated and Shelton discloses wherein the Management Computing System optimizes interactions across the connected input devices, peripherals and external devices {“coordinate interactions between the components of the surgical system 102” such components including input devices, peripherals and so on as claimed (see Figs. 34, 35, and 36, [0764] 1st sentence}, using real-time feedback {“real-time or near real-time to [adjust] inform the machine learning and/or decision-making process” (see Fig. 209 [1702], last two sentences) or algorithmic data {Markush group } to adjust control parameters for various usage scenarios {“alert the [user] clinician in certain scenarios”, see Fig. 252 [1899], 1st sentence}, including but not limited to the recognition of distinct patterns or sequences {“ variety of gestures such as, for example, [distinct pattern] drag and drop, scroll, zoom, rotate, tap, double tap, flick, drag, swipe, pinch open, pinch close, touch and hold, two-finger scroll”, see Figs. 143a and 143b [1271]} from input devices as specific commands or gestures {“through simple or multi-touch gestures by touching the touchscreen 6701,”, see Figs. 136 and 137, [1250]}.
As per claim 6, the rejection of claim 5 is incorporated and Shelton discloses wherein the plurality of electronic input devices coupled to the user include but are not limited to one or more of the following: an optical mouse, a keyboard, a joystick, a touch screen, a load cell button {“sensors 738… a load cell”, see Figs. 17 and 18 [0621]} }, a haptic feedback device, a voice recognition system, a neural interface, an augmented reality (AR) {“augmented reality vision system”, see Fig. 129 [1193], 2nd sentence} or virtual reality (VR) peripheral {Markush group }, a wearable device, a gesture recognition device {“through simple or multi-touch gestures [recognized] by touching the touchscreen 6701,”, see Figs. 136 and 137, [1250]}, an audio interface for merging or transitioning between different music genres {Markush group } and variations or equivalents thereof.
As per claim 7, the rejection of claim 6 is incorporated and Shelton discloses wherein some of the input devices include resistive sensors {“sensors 738… a load cell”, see Figs. 17 and 18 [0621]}, and the system further comprises software for adjusting how each resistive sensor {“a resistive sensor”, see Fig. 17 [0621]} interacts with control parameters and settings {“combination of tissue visualization modalities”, see Fig. 170, [1402], 1st sentence}.
As per claim 8, the rejection of claim 6 is incorporated and Shelton discloses wherein the Management Computing System is further configured to manage inputs from one or multiple input devices {peripherals such as “secondary surgical display”, see Fig. 178B [1442]}, including but not limited to potentiometer foot pedals {“point the laser at the up arrow and click the foot pedal repeatedly until the desired setting is attained”, see Fig. 137, [1236] last sentence} and adjust settings and output parameters {“real-time or near real-time to [adjust] inform the machine learning and/or decision-making process” (see Fig. 209 [1702], last two sentences) per input device within “surgical hubs 9000” as claimed (see Figs. 193 and 194, [1571], 2nd sentence} based on user’s force input {“analog rotary sensor like a potentiometer” that would translate a user’s foot pressure to analog signal, see Fig. 12 [0580], last sentence}, including but not limited to cursor speed, DPI adjustment, audio, or haptic feedback {“foot pedal which has a plurality of buttons 6562, 6564, 6565”, see Fig. 135 [1219], 1st sentence}, AR/VR peripherals, wearable devices {Markush group}.
As per claim 9, the rejection of claim 6 is incorporated and Shelton discloses wherein some of the input devices include a load cell {“sensors 738… a load cell”, see Figs. 17 and 18 [0621]} capable of detecting varying pressures applied by the user {“the one or more sensors 738 may be sampled in real time [and varying pressures] during a clamping operation by the processor of the control circuit 710”, see Fig. 17, [0624], last two sentences}, allowing for dynamic interaction modalities that adjust control parameters, such as cursor speed and DPI settings {“visualized by the real-time imaging system… provide [cursor] triangulation and instrument mapping of the surgical tools within the visualization field” ([02020], 3rd sentence) to “direct a displacement of a surgical tool along the axis of the elongate shaft of the surgical tool” ([2020]) that displacement include speed, in real time {“real-time or near real-time to [adjust] inform the machine learning and/or decision-making process”, see Fig. 209 [1702], last two sentences}.
As per claim 10, the rejection of claim 9 is incorporated and Shelton discloses: wherein some of the input devices include a load cell capable {“sensors 738… a load cell”, see Figs. 17 and 18 [0621]} of detecting varying pressures applied by the user {“a strain gauge, a load cell, a pressure sensor”, see Figs. 17 and 18, [0621], 2nd sentence}, allowing for dynamic interaction modalities {“combination of tissue visualization modalities”, see Fig. 170, [1402], 1st sentence} that adjust control parameters such as but not limited to cursor speed {“visualized by the real-time imaging system… provide [cursor] triangulation and instrument mapping of the surgical tools within the visualization field” ([02020], 3rd sentence) to “direct a displacement of a surgical tool along the axis of the elongate shaft of the surgical tool” ([2020]) that displacement include speed and DPI settings in real time {“image phase data related to tissue layer composition, [dpi settings] image intensity (amplitude) data related to tissue surface features, and image wavelength data related to tissue mobility (such as blood cell transport) as well as tissue depth”, see Fig. 170 [1402]}.
As per claim 11, the rejection of claim 10 is incorporated and Shelton discloses wherein the load cell's sensitivity and response to applied pressure can be adjusted through software or manual controls {“the one or more sensors 738 may be sampled in real time [and varying pressures] during a clamping operation by the processor [software] of the control circuit 710”, see Fig. 17, [0624], last two sentences}, providing real-time feedback {“image phase data related to tissue layer composition, [dpi settings] image intensity (amplitude) data related to tissue surface features, and image wavelength data related to tissue mobility (such as blood cell transport) as well as tissue depth”, see Fig. 170 [1402]} and enabling the recognition of distinct pressure patterns or gestures as specific commands {“through simple or multi-touch gestures by touching the touchscreen 6701,”, see Figs. 136 and 137, [1250]}.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following references are indicative the current state of the art regarding claim 1’s “management computing system”, “microcontroller”, or “one or more sensors”: US 20240407963 A1, US 20220104822 A1, US 20110004124 A1, US 7413565 B2, US 6911916 B1, and US 20030135203 A1.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER A. BARTELS whose telephone number is (571)270-3182. The examiner can normally be reached on Monday-Friday 9:00a-5:30pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dr. Henry Tsai can be reached on 571-272-4176. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C. B./ Examiner, Art Unit 2184
/HENRY TSAI/ Supervisory Patent Examiner, Art Unit 2184