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 .
Claims 1-19 are pending for examination. Claims 1, and 17-19 are independent.
Claim Objections
Claim 18 objected to because of the following informalities: Claim 18 line 4 does not end with any semicolon. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a first determination unit” in Claim 1.
“a second determination unit” in Claim 1.
“a reinforcement learning unit” in Claim 1.
“a control unit” in Claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 19 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 19:
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is
directed to non-statutory subject matter. The claim(s) does/do not fall within at least one
of the four categories of patent eligible subject matter because directed toward a program. Therefore, it appears that the claim is directed to software per se, which is not patent eligible subject matter under 35 USC 101.
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.
Claim(s) 1-13, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saur et al. (US 20220039883 A1, hereinafter "Saur") in view of Zhi et al. (US 20190317472 A1, hereinafter "Zhi").
Regarding Claim 1
Saur discloses: A medical arm control system ([Para 0050-0053 and Fig 3]) comprising:
a first determination unit that performs ([Para 0011 and Fig 3] describe a processing unit.) supervised learning using first input data and first training data, and generates an autonomous movement control model for autonomously moving a medical arm ([Para 0008-0010, 0042, 0055, and Fig 2] describes virtual surgical robot 32 simulating movements and actions of the moveable robot member 8 and with its kinematic and geometric characteristics by a virtual 3D model of the surgical robot 2. The virtual surgical robot 32 includes at least one of a robot arm, an endoscope, and a surgical tool 14. Examiner interprets the virtual 3D model as a control model, pre-surgical data as input data, and sensor data as training data.)
([Para 0015-0017, 0048-0050, 0055 and Fig 2] discloses calculating a reward model for the robot. Examiner interprets pre-surgical data as second input data used by the machine learning unit.); and
([Para 0048-0050, 0055 and Fig 2] Examiner interprets simulated data as third input data used by the machine learning unit to perform reinforcement learning and control the robot.).
Saur does not explicitly disclose: a second determination unit that performs supervised learning using second input data and second training data, and generates a reward model; a reinforcement learning unit that executes the reward model
However, Zhi discloses in the same field of endeavor: a first determination unit ([Para 0062-0063 and Fig 5] discloses a determination data acquisition unit.), and generates an autonomous movement control model ([Para 0006-0009, 0058-0063, 0102 and Fig 5] describes controlling a machine on the basis of coefficients of the Lugre model (i.e. control model).)
a second determination unit ([Para 0065 and Fig 5] discloses a reward calculation unit.) that performs supervised learning using second input data and second training data, and generates a reward model for calculating a reward ([Para 0054-0057, 0062-0069, 0073 and Fig 5-6] describes a reward model using state data S (i.e. second input data) and determination data D (i.e. second training data).);
a reinforcement learning unit ([Para 0064 and Fig 5] discloses reinforcement learning unit.) that executes the reward model using third input data, and reinforces the autonomous movement control model using the reward calculated by the reward model ([Para 0064-0071 and Fig 5] describes a reinforcement learning model using action input data and that’s reinforcing the Lugre model using the calculated reward.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the processing units disclose by Zhi into the Surgical System disclose by Saur to disclose processing units and training data. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of processing units disclose by Zhi as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to train and adjust a reinforcement learning algorithm.
Regarding Claim 17
Saur discloses: A medical arm device ([Para 0050-0053 and Fig 3]) which stores an autonomous movement control model obtained by reinforcing a control model for autonomously moving a medical arm using a reward obtained by inputting third input data to a reward model for calculating the reward to be given to a movement of the medical arm, ([Para 0048-0050, 0055 and Fig 2] Examiner interprets simulated data as third input data used by the machine learning unit to perform reinforcement learning and control the robot.)
the control model being generated by performing supervised learning using first input data and first training data, ([Para 0008-0010, 0042, 0055, and Fig 2] describes virtual surgical robot 32 simulating movements and actions of the moveable robot member 8 and with its kinematic and geometric characteristics by a virtual 3D model of the surgical robot 2. The virtual surgical robot 32 includes at least one of a robot arm, an endoscope, and a surgical tool 14. Examiner interprets the virtual 3D model as a control model, pre-surgical data as input data, and sensor data as training data.)
the reward model being generated by performing supervised learning using second input data and ([Para 0015-0017, 0048-0050, 0055 and Fig 2] discloses calculating a reward model for the robot. Examiner interprets pre-surgical data as second input data used by the machine learning unit.)
Saur does not explicitly disclose: second training data.
However, Zhi discloses in the same field of endeavor: stores an autonomous movement control model obtained by reinforcing a control model for autonomously moving ([Para 0064-0071 and Fig 5] describes a reinforcement learning model using action input data and that’s reinforcing the Lugre model (i.e. control model) using the calculated reward.); the reward model being generated by performing supervised learning using second input data and second training data. ([Para 0054-0057, 0062-0069, 0073 and Fig 5-6] describes a reward model using state data S (i.e. second input data) and determination data D (i.e. second training data).)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the processing units disclose by Zhi into the Surgical System disclose by Saur to disclose processing units and training data. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of processing units disclose by Zhi as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to train and adjust a reinforcement learning algorithm.
Regarding Claim 18
Saur discloses: A medical arm control method, by a medical arm control system ([Para 0050-0053 and Fig 3]), comprising:
reinforcing an autonomous movement control model for autonomously moving the medical arm ([Para 0048-0050, 0055 and Fig 2] describes a reinforcement learning model for utilizing a virtual model to control the robot.)
using a reward obtained by inputting third input data to a reward model for calculating the reward to be given to a movement of the medical arm, the autonomous movement control model being generated by performing supervised learning using first input data and first training data, the reward model being generated by performing supervised learning using second input data and ([Para 0015-0017, 0048-0050, 0055 and Fig 2] discloses calculating a reward model for the robot. Examiner interprets pre-surgical data as second input data used by the machine learning unit.; and
controlling the medical arm using the reinforced autonomous movement control model. ([Para 0016-0018, 0048-0057, and Fig 1-3] describes controlling surgical robot based on machine learning an (i.e. reinforcement learning).)
Saur does not explicitly disclose: second training data;
However, Zhi discloses in the same field of endeavor: reinforcing an autonomous movement control model for autonomously moving ([Para 0064-0071 and Fig 5] describes a reinforcement learning model using action input data and that’s reinforcing the Lugre model (i.e. control model) using the calculated reward.); using a reward obtained by inputting third input data to a reward model for calculating the reward to be given to a movement of the ([Para 0054-0057, 0062-0069, 0073 and Fig 5-6] describes a reward model using state data S (i.e. second input data) and determination data D (i.e. second training data).);
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the processing units disclose by Zhi into the Surgical System disclose by Saur to disclose processing units and training data. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of processing units disclose by Zhi as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to train and adjust a reinforcement learning algorithm.
Regarding Claim 19
Saur in view of Zhi discloses: A program causing a computer to function ([Para 0026-0027], Saur) as: (Claim 19 is a program claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 2
Saur in view of Zhi discloses: The medical arm control system according to claim 1, wherein the medical arm supports a medical observation device ([Para 0008, 0042 and Fig 1] Saur discloses a robot arm with a surgical tool.).
Regarding Claim 3
Saur in view of Zhi discloses: The medical arm control system according to claim 2, wherein the medical observation device is an endoscope ([Para 0008, 0042 and Fig 1] Saur discloses a robot arm with endoscope.).
Regarding Claim 4
Saur in view of Zhi discloses: The medical arm control system according to claim 1, wherein the medical arm supports a medical instrument ([Para 0008, 0042 and Fig 1] Saur discloses a robot arm with a surgical tool.).
Regarding Claim 5
Saur in view of Zhi discloses: The medical arm control system according to claim 1, wherein the first input data includes information regarding at least one of a position and an attitude of the medical arm, a position and an attitude of a medical instrument, surgical site information, patient information, and an image. ([Para 0010, 0041, and Fig 2] Saur descries pre-surgical data of a patient.)
Regarding Claim 6
Saur in view of Zhi discloses: The medical arm control system according to claim 5, wherein the first input data and the first training data are clinical data, simulated clinical data, or virtual clinical data. ([Para 0008-0010, 0042, 0055, and Fig 2] Saur describes a virtual 3D model of the surgical robot 2. Examiner interprets the virtual 3D model as with pre-surgical data as input data, and sensor data as training data
Regarding Claim 7
Saur in view of Zhi discloses: The medical arm control system according to claim 5, wherein the first training data includes information regarding at least one of the position and the attitude of the medical arm, and image information. ([Para 0009 and 0042] Saur describes kinematic and geometric characteristics by a virtual 3D model of the surgical robot 2.)
Regarding Claim 8
Saur in view of Zhi discloses: The medical arm control system according to claim 7, wherein the autonomous movement control model outputs information regarding at least one of the position, the attitude, a speed, and an acceleration of the medical arm and an imaging condition of the image. ([Para 0007, 00044-0050, Fig 13-15], Zhi describes a Lugre model (i.e. control model) outputs a compensation torque. [Para 0042] Saur describes actuator 6 moves the moveable robot member 8 to 6D poses in the surgical field, which means that there are three directions of translational movement and three directions of rotational movement. The actuator 6 changes the position as well as the orientation of the robot member 8. )
Regarding Claim 9
Saur in view of Zhi discloses: The medical arm control system according to claim 5, wherein the second input data includes at least one of the patient information and the image ([Para 0010, 0041, and Fig 2] Saur descries pre-surgical data of a patient.).
Regarding Claim 10
Saur in view of Zhi discloses: The medical arm control system according to claim 9, wherein the second input data is clinical data, simulated clinical data, or virtual clinical data. ([Para 0010, 0041, and Fig 2] Saur descries pre-surgical data of a patient that’s simulated.)
Regarding Claim 11
Saur in view of Zhi discloses: The medical arm control system according to claim 5, wherein the patient information includes information regarding at least one of a heart rate, a pulse, a blood pressure, a blood flow oxygen concentration, brain waves, respiration, sweating, myoelectric potential, a skin temperature, and a skin electrical resistance of a patient. ([Para 00012-0013, 0044-0045], Saur describes data like blood pressure.)
Regarding Claim 12
Saur in view of Zhi discloses: The medical arm control system according to claim 5, wherein the surgical site information includes information regarding at least one of a type, a position, and an attitude of an organ, and a positional relationship between the medical instrument and the organ. ([Para 0006, 0009-0010, 0038-0045], Saur describes surgical field information for changing the robot member's position and/or orientation.)
Regarding Claim 13
Saur in view of Zhi discloses: The medical arm control system according to claim 1, further comprising a control unit that controls the medical arm according to the reinforced autonomous movement control model. ([Para 0016-0018, 0048-0057, and Fig 1-3] describes controlling surgical robot based on machine learning an (i.e. reinforcement learning).)
Regarding Claim 16
Saur in view of Zhi discloses: The medical arm control system according to claim 1, wherein the third input data is virtual clinical data. ([Para 0048-0050, 0055 and Fig 2], Saur describes simulated data as third input data used by the machine learning unit.)
Claim(s) 14 -15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saur in view of Zhi and Lendvay et al. (US 20170116873 A1, hereinafter "Lendvay").
Regarding Claim 14
Saur in view of Zhi discloses: The medical arm control system according to claim 1, wherein the second training data includes
Saur in view of Zhi does not explicitly disclose: an evaluation score;
However, Lendvay discloses in the same field of endeavor: wherein the second training data includes an evaluation score of a state of the medical ([Para 0047-0052, 0133, 0156, 0165, Fig 4, and Fig 10-12] describes a rating for robotic surgery.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of rating robotic surgeries disclosed by Lendvay into the Control method disclosed by Zhi and the surgical system disclosed by Saur to provide an evaluation score. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of rating robotic surgeries disclosed by Lendvay as all the references are in the field of robotics. A person of ordinary skill of the art would have been motivated to perform the combination for being able to assess the performance of robotic operations to further improve future operations.
Regarding Claim 15
Saur in view of Zhi and Lendvay discloses: The medical arm control system according to claim 14, wherein the evaluation score is a subjective evaluation score by a doctor. ([Para 0047-0052, 0133, 0156, 0165, Fig 4, and Fig 10-12] Lendvay describes a rating for robotic surgery can be from an expert reviewer (i.e. surgeon).)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dahdouh et al (US 20200383734 A1) describes supervised surgical robotics. Ando (US 20190346836 A1) describes a reinforcement learning model for robotics.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TEWODROS E MENGISTU whose telephone number is (571)270-7714. The examiner can normally be reached Mon-Fri 9:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABDULLAH KAWSAR can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TEWODROS E MENGISTU/ Examiner, Art Unit 2127