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 RCE
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/20/2026 has been entered.
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 5, 9, 10, 11, 13, 15, 19, 20, 21, 23 are rejected under 35 U.S.C. 103 as being unpatentable by Eyssautier (US20210154852, from IDS) in view of Hayashida (US20200311928) and Neubach (US20110112549, from IDS) and Sohlden (US20180318009).
Regarding claim 5, Eyssautier teaches a system, comprising:
a robotic manipulator ([0042] discloses a robotic arm with an end effector “robotic manipulator”); and
a processor-implemented controller operably coupled to the robotic manipulator and configured to ([0057], [0064] disclosing the computer processing means that adjusts the sequence of commands based on sensor data):
receive sensor data ([0014] disclosing acquiring a three-dimensional representation of a surface to be treated. [0054] disclosing the acquisition is done through sensors such as infrared sensors);
predict, based on the sensor data, a deformation of a deformable body to be contacted by the robotic manipulator ([0017] disclosing adjusting the three dimensional generic model of the surface to be treated based on the acquired three dimensional representation of the surface to be treated by deformation of the generic model, i.e., predicting based on the sensor data deformation of the deformable body to be contacted with the manipulator);
identify at least one adjustment parameter based on the predicted deformation ([0017] discloses obtaining new sequence of movements “adjusted parameter” based on the deformation of the model);
and cause the robotic manipulator to execute a movement relative to the deformable body, based on the at least one adjustment parameter ([0017] discloses treating the surface based on the new sequence of movement, i.e., causing the manipulator to execute the movement relative to the deformable body based on the adjustment parameter).
While Eyssautier does not teach predict based on sensor data input into a contact model, the deformation, wherein a first finite element analysis (FES) model corresponds to the deformable body and includes dermis layer, a model of a muscle layer, a model of a fat layer, or a model of a bone. Detect a displacement of the deformable body and a force component associated with the deformable body during the execution of the movement by the manipulator; Combine the displacement of the deformable body and a force component associated with the deformable body to obtain a combinable value; Generate a stiffness model for the deformable body based on the combination value; predict based on sensor data input into a contact model, a deformation. wherein a second FEA model corresponds to the robotic manipulator, wherein the contact model corresponds to a combination of the first FEA and the second FEA.
Hayashida teaches predict based on sensor data input into a contact model; the deformation, wherein a first finite element analysis (FES) model corresponds to the deformable body and includes dermis layer, a model of a muscle layer, a model of a fat layer, or a model of a bone ([0033]-[0035] disclosing Finite element analysis to model body fat and organs and to predict deformation states by simulation, [0035] further disclosing in response to the movement of the surgical tool based on sensor data the FEA predicts the deformation).
Update the first FEA based on the stiffness model ([0035] further disclosing in response to the movement of the surgical tool based on sensor data the FEA predicts the deformation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Eyssautier to incorporate the teaching of Hayashida of wherein the controller is configured to predict the deformation using a finite element analysis (FEA) model, the FEA model including at least one of a model of a dermis layer, a model of a muscle layer, a model of a fat layer, or a model of a bone tissue in order to determine simulate a state where an organ is pulled or pressed by forceps as taught by Hayashida [0034].
Eyssautier as modified by Hayashida does not teach Detect a displacement of the deformable body and a force component associated with the deformable body during the execution of the movement by the manipulator; Combine the displacement of the deformable body and a force component associated with the deformable body to obtain a combinable value; Generate a stiffness model for the deformable body based on the combination value;
Neubach teaches Detect a displacement of the deformable body and a force component associated with the deformable body during the execution of the movement by the manipulator ([0089]-[0095] disclosing measuring tissue displacement under the applied force to obtain an elastic property of the tissue as a combinable value which is used to determine stiffness model of the tissue);
Combine the displacement of the deformable body and a force component associated with the deformable body to obtain a combinable value ([0089]-[0095] disclosing measuring tissue displacement under the applied force to obtain an elastic property of the tissue as a combinable value which is used to determine stiffness model of the tissue);
Generate a stiffness model for the deformable body based on the combination value ([0089]-[0095] disclosing measuring tissue displacement under the applied force to obtain an elastic property of the tissue as a combinable value which is used to determine stiffness characterization “stiffness model” of the tissue);
It is obvious to one of ordinary skill in the art to combine the stiffness model determined based on the force and displacement thus determining elasticity of a region, and or the combinable value to the combinable value “elastic property” to the stiffness model of Hayashida yielding predictable results in order to update the model incrementally as the needle is placed thus improving the treatment procedure. thus it is obvious to one or ordinary skill in the art to combine the teaching of Neubach with the teaching of Hayashida thus updating the FEA based on the updated stiffness model yielding predictable results in order to improve the treatment and not over penetrate or harm a person with softer tissue and improve accuracy of FEA simulation.
Sohlden teaches predict based on sensor data input into a contact model, a deformation of the robotic manipulator ([0058]-[0077] disclosing the prediction of the deformation of the surgical tool based on an FEA combining both the tool and the tissue of the patient wherein at least the tissue of the model is obtained via sensors by imaging and updating).
wherein a second FEA model corresponds to the robotic manipulator, wherein the contact model corresponds to a combination of the first FEA and the second FEA ([0058]-[0077] disclosing the prediction of the deformation of the surgical tool based on an FEA combining both the tool and the tissue of the patient wherein at least the tissue of the model is obtained via sensors by imaging and updating).
It would have been obvious to one of ordinary skill in the art to combine the teaching of Sohlden to the instrument of Eyssautier as modified by Hayashida and Neubach yielding predictable results in order to determine a path that avoids unnecessary forces and or cause unwanted deforming of the tool or reaches blocked path thus improving safety and avoids damaging the tool.
Regarding claim 9, Eyssautier as modified by Hayashida and Neubach and Sohlden teaches the system of claim 5, wherein the processor-implemented controller is configured to cause the robotic manipulator to execute the movement according to a predefined interaction goal (Eyssautier [0017] discloses treating the surface based on the new sequence of movement “goal”).
Regarding claim 10, Eyssautier as modified by Hayashida and Neubach and Sohlden teaches the system of claim 9, wherein the predefined interaction goal specifies at least one of a desired mechanical shearing or a desired percussive manipulation (Eyssautier [0098] discloses the robot can implement a path with desired vibrations “desired percussive manipulation”).
Regarding claim 11, Eyssautier as modified by Hayashida and Neubach and Sohlden further teaches the system of claim 5, wherein the robotic manipulator includes an ultrasonic sensor, the sensor data includes data from the ultrasonic sensor, and the processor-implemented controller is further configured to generate at least one of a tissue density estimate or elasticity information for the deformable body during the execution of the movement by the manipulator.
Neubach teaches wherein the robotic manipulator includes an ultrasonic sensor, the sensor data includes data from the ultrasonic sensor, and the processor-implemented controller is further configured to generate at least one of a tissue density estimate or elasticity information for the deformable body during the execution of the movement by the manipulator ([0015] disclosing using ultrasonic sensors to determine a tissue density in response to the movement of a needle by a robot on the tissue).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Eyssautier as modified by Hayashida and Neubach and Sohlden to incorporate the teaching of Neubach wherein the robotic manipulator includes an ultrasonic sensor, the sensor data includes data from the ultrasonic sensor, and the processor-implemented controller is further configured to generate at least one of a tissue density estimate or elasticity information for the deformable body during the execution of the movement by the manipulator in order to adjust the robotic path based on the tissue density determined by ultrasonic sensors as taught by Neubach ([0015]).
Regarding claim 13, Eyssautier as modified by Hayashida and Neubach and Sohlden teaches the system of claim 11. wherein the processor-implemented controller is further configured to: detect a deformation of the deformable body during the execution of the movement by the manipulator; and determine at least one of a composition or a dimension of an anatomical layer of the deformable body based on the detected deformation of the deformable body.
Specifically, Neubach teaches detect a deformation of the deformable body during the execution of the movement by the manipulator ([0030] disclosing a robot moving a needle into a tissue. [0095] disclosing determining the displacement of the tissue by the needle, I.e., during the movement by the manipulator); and
determine at least one of a composition or a dimension of an anatomical layer of the deformable body based on the detected deformation of the deformable body ([0095] disclosing determining a type of the tissue “composition of an anatomical layer” based on the deformation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Eyssautier to incorporate the teaching of Neubach and Sohlden of detect a deformation of the deformable body during the execution of the movement by the manipulator; and determine at least one of a composition or a dimension of an anatomical layer of the deformable body based on the detected deformation of the deformable body in order to adjust the robotic path based on the tissue density by Neubach ([0015]).
Claims 15, 19, 20 are rejected for similar reasons as claims 5, 9, 10, respectively, see above rejection. Eyssautier teaches in [0057], [0064] disclosing the computer processing means that adjusts the sequence of commands based on sensor data.
Claims 21 are rejected for similar reasons as claims 11, see above rejection.
Claim 23 is rejected for similar reasons as claim 13, see above rejection.
Claim 7, 17 is rejected under 35 U.S.C. 103 as being unpatentable by Eyssautier (US20210154852, from IDS) in view of Hayashida (US20200311928) and Neubach (US20110112549, from IDS) and Sohlden (US20180318009) and Qui (US20200281805, from IDS).
Regarding claim 7, Eyssautier as modified by Hayashida and Neubach and Sohlden teaches the system of claim 5. Eyssautier as modified by Hayashida and Neubach and Sohlden does not teach wherein the processor-implemented controller is further configured to predict a desired palpation force, and the processor-implemented controller is configured to cause the robotic manipulator to execute the movement further based on the desired palpation force.
Qui teaches wherein the processor-implemented controller is further configured to predict a desired palpation force, and the processor-implemented controller is configured to cause the robotic manipulator to execute the movement further based on the desired palpation force ([0018] disclosing determining a force to be applied to the body within a predetermined range wherein the predetermined force is adjusted automatically by the massaging apparatus, i.e., the processor predicts a desired range of palpation force and the robotic manipulator executes movement based on the predetermined desired palpation force).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Eyssautier as modified by Hayashida and Neubach and Sohlden to incorporate the teaching of Qui of wherein the processor-implemented controller is further configured to predict a desired palpation force, and the processor-implemented controller is configured to cause the robotic manipulator to execute the movement further based on the desired palpation force in order to avoid discomfort or hurting of the patient as taught by Qui ([0018].
Claim 17 is rejected for similar reasons as claim 7, see above rejection.
Claims 8, 18 are rejected under 35 U.S.C. 103 as being unpatentable by Eyssautier (US20210154852, from IDS) in view of Hayashida (US20200311928) and Neubach (US20110112549, from IDS) and Sohlden (US20180318009) and Gu (US20190160684, from IDS).
Regarding claim 8, Eyssautier as modified by Hayashida and Neubach and Sohlden teaches the system of claim 5. Eyssautier as modified by Hayashida and Neubach and Sohlden does not teach wherein the processor-implemented controller is further configured to detect at least one of a thermal state of the deformable body, a tissue stiffness of the deformable body, or a tissue anomaly of the deformable body, and the processor- implemented controller is configured to cause the robotic manipulator to execute the movement further based on the at least one of the thermal state of the deformable body, the tissue stiffness of the deformable body, or the tissue anomaly of the deformable body.
Gu teaches wherein the processor-implemented controller is further configured to detect at least one of a thermal state of the deformable body, a tissue stiffness of the deformable body, or a tissue anomaly of the deformable body, and the processor- implemented controller is configured to cause the robotic manipulator to execute the movement further based on the at least one of the thermal state of the deformable body, the tissue stiffness of the deformable body, or the tissue anomaly of the deformable body ([0032]-[0033] disclosing detecting at least a knot “anomaly” in the tissue of the person “deformable body” and causing the manipulator to apply a pressure on the knot, i.e., execute movement based on the detected anomaly).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Eyssautier as modified by Hayashida and Neubach and Sohlden to incorporate the teaching of Gu of wherein the processor-implemented controller is further configured to detect at least one of a thermal state of the deformable body, a tissue stiffness of the deformable body, or a tissue anomaly of the deformable body, and the processor- implemented controller is configured to cause the robotic manipulator to execute the movement further based on the at least one of the thermal state of the deformable body, the tissue stiffness of the deformable body, or the tissue anomaly of the deformable body in order to apply a concentrated pressure on a knot “anomaly” in the tissue to cure the patient as taught by Gu [0032]-[0033].
Claim 18 is rejected for similar reasons as claim 8, see above rejection.
Claims 14 are rejected under 35 U.S.C. 103 as being unpatentable by Eyssautier (US20210154852) in view of Hayashida (US20200311928) and Neubach (US20110112549, from IDS) and Sohlden (US20180318009) and Tian (US20200126297, from IDS).
Regarding claim 14, Eyssautier as modified by Hayashida and Neubach and Sohlden teaches the system of claim 5. Eyssautier as modified by Hayashida and Neubach and Sohlden does not teach wherein the sensor data is in the form of a fused sensing stream.
Tian teaches wherein the sensor data is in the form of a fused sensing stream ([0043] disclosing fusion off sensors).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Eyssautier as modified by Hayashida and Neubach and Sohlden to incorporate the teaching of Tian of of sensor data is in the form of a fused stream in order to cause the analysis model to generate a recommendation regarding changes in the currently administered treatment procedure or plan based on the fused sensor data.
Response to Arguments
Applicant’s arguments filed on 05/20/2026 have been fully considered but they are not persuasive.
Applicant’s arguments with respect to the independent claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure. The prior art cited in PTO-892 and not mentioned above disclose related devices and methods.
US20100168918 discloses FEA for bending of tools based on sensor data.
US20170304008 disclosing determining elasticity based on force and displacement of tissue.
US20140094702 disclosing measuring displacement and applying incremental force to determine elasticity.
US20200357508 disclosing a finite element analysis of a foot for treatment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMAD O EL SAYAH whose telephone number is (571)270-7734. The examiner can normally be reached on M-Th 6:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramon Mercado can be reached on (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMAD O EL SAYAH/Examiner, Art Unit 3664B
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