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 .
Election/Restrictions
Applicant’s election without traverse of Group I (Claims 1-17) in the reply filed on 14th April 2026 is acknowledged. Applicant has canceled claims 18-43.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12, 14 & 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites ‘a patient’, it is unclear whether this is referring to ‘a patient as previously recited or not, rendering claim 1 indefinite. Examiner interprets the indefinite limitation as ‘the patient’ as best understood by the disclosure.
Claim 2 recites ‘patient data’, it is unclear whether this limitation is part of ‘patient data sets’ as recited in claim 1 or not, rendering claim 1 indefinite.
Claim 2, ‘the received patient data’, there is insufficient antecedent basis for this limitation in this claim. Examiner interprets this limitation to read ‘the patient data’.
Claim 3, ‘the corrective plan’, there is insufficient antecedent basis for this limitation in this claim. Examiner interprets this to read ‘the adjustable-implant corrective plan’, as best understood by the disclosure.
Claim 4, 9 & 14, ‘the device’, there is insufficient antecedent basis for this limitation in these claims. Examiner notes this limitation should likely read ‘the intervertebral fusion device implant’ as best understood by the disclosure.
Claims 6 & 16, ‘the one or more implant actuators’, there is insufficient antecedent basis for this limitation in these claims.
Claim 8 recites ‘according to a corrective plan’, it is unclear if this is meant to refer to ‘according to a corrective plan for the patient’ as previously recited or a distinct corrective plan, rendering claim 8 indefinite. Examiner interprets the indefinite limitation to read ‘according to the corrective plan for the patient’ as best understood by the disclosure.
Claims 2-7, 9-12 are rejected for their dependence on a rejected claim.
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) 1-3, 5-6, 8, 10-11, 13, 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210307786 A1 to Ross et al. (hereinafter, Ross) in view of US 20200205900 A1 to Buckland et al. (hereinafter, Buckland).
Regarding Claim 1, Ross discloses a method for treating a patient (Ross: Abstract), comprising:
receiving, by a computer system (Ross: Para. [0004], [0027]; Fig. 2A), implant sensor readings from one or more implant sensors of a spinal implant implanted in a patient and configured in a first physical configuration according to an adjustable-implant corrective plan for the patient (Ross: Para. [0023], [0028-0029], [0036], item 17 ‘sensor’),
the implant sensor readings indicative of a load applied by a spine of the patient on the spinal implant (Ross: Para. [0036], [0046]);
transmitting, by the computer system, the implant electrical signals to the spinal implant to cause the spinal implant to move the spinal implant to a second physical configuration for the target correction (Ross: Para. [0022-0023], [0025]).
While Ross discloses implementation of machine learning to conduct the functions of adjusting the implant for target correction according to the adjustable-implant corrective plan, generating implant electrical signals, modifying, changing, tailoring, or restructuring the operations including incorporating data such as age, height, sitting height, Risser sign or the cobb angle in the scoliotic curve before or after adjustment and to adjust the load to achieve target correction (Ross: Para. [0036], [0046], [0056]). Ross does not explicitly disclose extracting a feature vector from the sensor readings and does not discuss the training of the machine learning module.
However, Buckland teaches extracting, by the computer system, a feature vector from the implant sensor readings using a machine learning module of the computer system (Buckland: Para. [0017], [0025-0027]),
the machine learning module trained based on patient data sets to generate the implant electrical signals (Buckland: Para. [0025-0027]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to specify extraction of features and training the machine learning module based on patient data sets to generate implant electrical signals as taught by Buckland to create high quality implant orientation recommendations to achieve the desired results, allowing the system to make very specific goal based recommendations directed to more power, speed, accuracy, flexibility, etc. (Buckland: Para. [0026]).
Regarding Claim 2, Ross in view of Buckland discloses the method of claim 1, Ross further discloses comprising: receiving, by the computer system, patient data (Ross: Para. [0003-0004], [0029], [0056]);
determining, by the computer system, an anatomical configuration of the patient's spine based on the received patient data (Ross: Para. [0029], [0032], [0056]), and
identifying, by the computer system, the target correction based on the anatomical configuration and available adjustability of the spinal implant (Ross: Para. [0032], [0056]),
Ross does not explicitly disclose a feature vector.
However, Buckland teaches wherein the identified target correction is used to extract the feature vector (Buckland: Para. [0017], [0025-0027]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to specify extraction of features and training the machine learning module based on patient data sets to generate implant electrical signals as taught by Buckland to create high quality implant orientation recommendations to achieve the desired results, allowing the system to make very specific goal based recommendations directed to more power, speed, accuracy, flexibility, etc. (Buckland: Para. [0026]).
Regarding Claim 3, Ross in view of Buckland discloses the method of claim 1, Ross further discloses wherein the corrective plan comprises criteria for actuating the spinal implant (Ross: Para. [0023]).
Regarding Claim 5, Ross in view of Buckland discloses the method of claim 1, Ross further discloses utilizing data that is further indicative of at least one of lumbar lordosis, Cobb angles, coronal parameters, sagittal parameters, pelvic parameters, disc height, segment flexibility, bone quality, or rotational is placement of the spine of the patient (Ross: Para. [0032], [0056]). Ross does not explicitly disclose a feature vector.
However, Buckland teaches extracting the feature vector from the implant sensor readings using a machine learning module of the computer system (Buckland: Para. [0017], [0025-0027]),
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to specify extraction of features as taught by Buckland to create high quality implant orientation recommendations to achieve the desired results, allowing the system to make very specific goal based recommendations directed to more power, speed, accuracy, flexibility, etc. (Buckland: Para. [0026]).
Regarding Claim 6, Ross in view of Buckland discloses the method of claim 1, Ross further discloses wherein configuring the spinal implant in the second physical configuration comprises: adjusting at least one of a screw, a cage, a plate, a rod, a disk, a spacer, an expandable device, a stent, a bracket, a tie, a scaffold, a fixation device, an anchor, a nut, a bolt, a rivet, a connector, a tether, a fastener, or a joint replacement of the spinal implant using the one or more implant actuators (Ross: Para. [0022]).
Regarding Claim 8, Ross discloses a non-transitory, computer-readable storage medium storing computer instructions, which when executed by one or more computer processors, cause the one or more computer processors (Ross: Abstract; Para. [0004]) to:
receive implant sensor readings from one or more implant sensors of a spinal implant implanted in a patient and configured in a first physical configuration according to a corrective plan for the patient (Ross: Para. [0023], [0028-0029], [0036], item 17 ‘sensor’),
the implant sensor readings indicative of a load applied by a spine of the patient on the spinal implant (Ross: Para. [0036], [0046]);
transmit the implant electrical signals to the spinal implant to cause the spinal implant to move the spinal implant to a second physical configuration for the target correction (Ross: Para. [0022-0023], [0025]).
While Ross discloses implementation of machine learning to conduct the functions of adjusting the implant for target correction according to the adjustable-implant corrective plan, generating implant electrical signals, modifying, changing, tailoring, or restructuring the operations including incorporating data such as age, height, sitting height, Risser sign or the cobb angle in the scoliotic curve before or after adjustment and to adjust the load to achieve target correction (Ross: Para. [0036], [0046], [0056]). Ross does not explicitly disclose extracting a feature vector from the sensor readings and does not discuss the training of the machine learning module.
However, Buckland teaches extracting, by the computer system, a feature vector from the implant sensor readings using a machine learning module of the computer system (Buckland: Para. [0017], [0025-0027]),
the machine learning module trained based on patient data sets to generate the implant electrical signals (Buckland: Para. [0025-0027]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to specify extraction of features and training the machine learning module based on patient data sets to generate implant electrical signals as taught by Buckland to create high quality implant orientation recommendations to achieve the desired results, allowing the system to make very specific goal based recommendations directed to more power, speed, accuracy, flexibility, etc. (Buckland: Para. [0026]).
Regarding Claim 10, Ross in view of Buckland discloses the non-transitory, computer-readable storage medium of claim 8, Ross further discloses utilizing data that is further indicative of at least one of lumbar lordosis, Cobb angles, coronal parameters, sagittal parameters, pelvic parameters, disc height, segment flexibility, bone quality, or rotational is placement of the spine of the patient (Ross: Para. [0032], [0056]). Ross does not explicitly disclose a feature vector.
However, Buckland teaches extracting the feature vector from the implant sensor readings using a machine learning module of the computer system (Buckland: Para. [0017], [0025-0027]),
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to specify extraction of features as taught by Buckland to create high quality implant orientation recommendations to achieve the desired results, allowing the system to make very specific goal based recommendations directed to more power, speed, accuracy, flexibility, etc. (Buckland: Para. [0026]).
Regarding Claim 11, Ross discloses the non-transitory, computer-readable storage medium of claim 8, Ross further discloses wherein configuring the spinal implant in the second physical configuration comprises: adjusting at least one of a screw, a cage, a plate, a rod, a disk, a spacer, an expandable device, a stent, a bracket, a tie, a scaffold, a fixation device, an anchor, a nut, a bolt, a rivet, a connector, a tether, a fastener, or a joint replacement of the spinal implant using one or more implant actuators (Ross: Para. [0022]).
Regarding Claim 13, Ross discloses a system (Ross: Abstract), comprising: one or more computer processors (Ross: Para. [0004]); and a non-transitory, computer-readable storage medium storing computer instructions, which when executed by the one or more computer processors, cause the one or more computer processors (Ross: Para. [0004]) to:
receive implant sensor readings from one or more implant sensors of a spinal implant implanted in a patient and configured in a first physical configuration (Ross: Para. [0023], [0028-0029], [0036], item 17 ‘sensor’),
the implant sensor readings indicative of a load applied by a spine of the patient on the spinal implant (Ross: Para. [0036], [0046]);
transmit the implant electrical signals to the spinal implant to cause the spinal implant to move the spinal implant to a second physical configuration for the target correction (Ross: Para. [0022-0023], [0025]).
While Ross discloses implementation of machine learning to conduct the functions of adjusting the implant for target correction according to the adjustable-implant corrective plan, generating implant electrical signals, modifying, changing, tailoring, or restructuring the operations including incorporating data such as age, height, sitting height, Risser sign or the cobb angle in the scoliotic curve before or after adjustment and to adjust the load to achieve target correction (Ross: Para. [0036], [0046], [0056]). Ross does not explicitly disclose extracting a feature vector from the sensor readings and does not discuss the training of the machine learning module.
However, Buckland teaches extracting, by the computer system, a feature vector from the implant sensor readings using a machine learning module of the computer system (Buckland: Para. [0017], [0025-0027]),
the machine learning module trained based on patient data sets to generate the implant electrical signals (Buckland: Para. [0025-0027]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to specify extraction of features and training the machine learning module based on patient data sets to generate implant electrical signals as taught by Buckland to create high quality implant orientation recommendations to achieve the desired results, allowing the system to make very specific goal based recommendations directed to more power, speed, accuracy, flexibility, etc. (Buckland: Para. [0026]).
Regarding Claim 15, Ross discloses the system of claim 13, Ross further discloses utilizing data that is further indicative of at least one of lumbar lordosis, Cobb angles, coronal parameters, sagittal parameters, pelvic parameters, disc height, segment flexibility, bone quality, or rotational is placement of the spine of the patient (Ross: Para. [0032], [0056]). Ross does not explicitly disclose a feature vector.
However, Buckland teaches extracting the feature vector from the implant sensor readings using a machine learning module of the computer system (Buckland: Para. [0017], [0025-0027]),
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to specify extraction of features as taught by Buckland to create high quality implant orientation recommendations to achieve the desired results, allowing the system to make very specific goal based recommendations directed to more power, speed, accuracy, flexibility, etc. (Buckland: Para. [0026]).
Regarding Claim 16, Ross discloses the system of claim 13, Ross further discloses wherein configuring the spinal implant in the second physical configuration comprises: adjusting at least one of a screw, a cage, a plate, a rod, a disk, a spacer, an expandable device, a stent, a bracket, a tie, a scaffold, a fixation device, an anchor, a nut, a bolt, a rivet, a connector, a tether, a fastener, or a joint replacement of the spinal implant using the one or more implant actuators (Ross: Para. [0022]).
Claim(s) 4, 9, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ross in view of Buckland in further view of US 20090234456 A1 to Nycz.
Regarding Claim 4, Ross in view of Buckland discloses the method of claim 1, While Ross discloses instructions based on receiving sensor readings from sensor(s) in various implants (Ross: Para. [0023], [0025], [0028-0029]) and providing instructions for adjusting configurations of implants (Ross: Para. [0022-0023], [0025], [0036], [0046], [0056]) with machine learning implementation (Ross: Para. [0056]), Ross is silent on specifically an intervertebral fusion device implant, receiving device sensor readings from one or more device sensors embedded in the intervertebral fusion device implant, the device sensor readings received before the implant sensor readings are received from the spinal implant, generating electrical signals that include instructions for adjusting a configuration of the intervertebral fusion device implant.
However, Nycz teaches receiving, by a computer system, device sensor readings from one or more device sensors embedded in an intervertebral fusion device implant implanted in the patient (Nycz: Para. [0006-0010], Fig. 4 item 100 implant, items 106, 108, 110, 112 ‘support’, w/ embedded sensors item 158, 160; Fig. 11); and
wherein the device electrical signals include instructions for adjusting a configuration of the device (Nycz: Para. [0006], [0008], [0010]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross by substituting one or more of the implants for an intervertebral fusion device implant with embedded sensors as taught by Nycz to achieve predictable results, in this case to provide data to the computing device for configuration determinations (Nycz: Para. [0010].)
Ross in view of Nycz is silent on receiving device sensor readings before the implant sensor readings are received from the spinal implant.
However, Buckland teaches the device sensor readings received before the implant sensor readings are received from the spinal implant (Buckland: Para. [0013]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross in view of Nycz to specify receiving device sensor readings before implant sensor readings as taught by Buckland to determine an orientation configuration for the surgical implant that is personalized to the patient (Buckland: Para. [0011]).
Regarding Claim 9, Ross in view of Buckland discloses the non-transitory, computer-readable storage medium of claim 8. While Ross discloses instructions based on receiving sensor readings from sensor(s) in various implants (Ross: Para. [0023], [0025], [0028-0029]) and providing instructions for adjusting configurations of implants (Ross: Para. [0022-0023], [0025], [0036], [0046], [0056]) with machine learning implementation (Ross: Para. [0056]), Ross is silent on specifically an intervertebral fusion device implant, receiving device sensor readings from one or more device sensors embedded in the intervertebral fusion device implant, the device sensor readings received before the implant sensor readings are received from the spinal implant, generating electrical signals that include instructions for adjusting a configuration of the intervertebral fusion device implant.
However, Nycz teaches receiving, by a computer system, device sensor readings from one or more device sensors embedded in an intervertebral fusion device implant implanted in the patient (Nycz: Para. [0006-0010], Fig. 4 item 100 ‘cage’, items 106, 108, 110, 112 ‘support’, w/ embedded sensors item 158, 160; Fig. 11),
wherein the device electrical signals include instructions for adjusting a configuration of the device (Nycz: Para. [0006], [0008], [0010]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross by substituting one or more of the implants for an intervertebral fusion device implant with embedded sensors as taught by Nycz to achieve predictable results, in this case to provide data to the computing device for configuration determinations (Nycz: Para. [0010].)
Ross in view of Nycz is silent on receiving device sensor readings before the implant sensor readings are received from the spinal implant.
However, Buckland teaches the device sensor readings received before the implant sensor readings are received from the spinal implant (Buckland: Para. [0013]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross in view of Nycz to specify receiving device sensor readings before implant sensor readings as taught by Buckland to determine an orientation configuration for the surgical implant that is personalized to the patient (Buckland: Para. [0011]).
Regarding Claim 14, Ross in view of Buckland discloses the system of claim 13. While Ross discloses instructions based on receiving sensor readings from sensor(s) in various implants (Ross: Para. [0023], [0025], [0028-0029]) and providing instructions for adjusting configurations of implants (Ross: Para. [0022-0023], [0025], [0036], [0046], [0056]) with machine learning implementation (Ross: Para. [0056]), Ross is silent on specifically an intervertebral fusion device implant, receiving device sensor readings from one or more device sensors embedded in the intervertebral fusion device implant, the device sensor readings received before the implant sensor readings are received from the spinal implant, generating electrical signals that include instructions for adjusting a configuration of the intervertebral fusion device implant.
However, Nycz teaches receiving, by a computer system, device sensor readings from one or more device sensors embedded in an intervertebral fusion device implant implanted in the patient (Nycz: Para. [0006-0010], Fig. 4 item 100 ‘cage’, items 106, 108, 110, 112 ‘support’, w/ embedded sensors item 158, 160; Fig. 11),
wherein the device electrical signals include instructions for adjusting a configuration of the device (Nycz: Para. [0006], [0008], [0010]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross by substituting one or more of the implants for an intervertebral fusion device implant with embedded sensors as taught by Nycz to achieve predictable results, in this case to provide data to the computing device for configuration determinations (Nycz: Para. [0010].)
Ross in view of Nycz is silent on receiving device sensor readings before the implant sensor readings are received from the spinal implant.
However, Buckland teaches the device sensor readings received before the implant sensor readings are received from the spinal implant (Buckland: Para. [0013]).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross in view of Nycz to specify receiving device sensor readings before implant sensor readings as taught by Buckland to determine an orientation configuration for the surgical implant that is personalized to the patient (Buckland: Para. [0011]).
Claim(s) 7, 12, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ross in view of Buckland in further view of US 20040171924 A1 to Mire et al. (hereinafter, Mire).
Regarding Claim 7, Ross in view of Buckland discloses the method of claim 1, Ross is silent on adjusting a reservoir to modify an amount of at least one of a pharmacological, a biological, a biochemical, a narcotic, or a steroid delivered to the patient.
However, Mire teaches wherein configuring the spinal implant in the second physical configuration comprises: adjusting a reservoir coupled to the spinal implant to modify an amount of at least one of a pharmacological, a biological, a biochemical, a narcotic, or a steroid delivered to the patient (Mire: Para. [0138], [0144]; Fig. 16).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to include a reservoir to modify an amount of a pharmacological, a biological, a biochemical, a narcotic, or a steroid delivered to the patient as taught by Mire to deliver drugs that may aid in increasing bone density and fusion of broken bones (Mire: Para. [0144]).
Regarding Claim 12, Ross in view of Buckland discloses the non-transitory, computer-readable storage medium of claim 8. Ross is silent on adjusting a reservoir to modify an amount of at least one of a pharmacological, a biological, a biochemical, a narcotic, or a steroid delivered to the patient.
However, Mire teaches wherein configuring the spinal implant in the second physical configuration comprises: adjusting a reservoir coupled to the spinal implant to modify an amount of at least one of a pharmacological, a biological, a biochemical, a narcotic, or a steroid delivered to the patient (Mire: Para. [0138], [0144]; Fig. 16).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to include a reservoir to modify an amount of a pharmacological, a biological, a biochemical, a narcotic, or a steroid delivered to the patient as taught by Mire to deliver drugs that may aid in increasing bone density and fusion of broken bones (Mire: Para. [0144]).
Regarding Claim 17, Ross in view of Buckland discloses the system of claim 13. Ross is silent on adjusting a reservoir to modify an amount of at least one of a pharmacological, a biological, a biochemical, a narcotic, or a steroid delivered to the patient.
However, Mire teaches wherein configuring the spinal implant in the second physical configuration comprises: adjusting a reservoir coupled to the spinal implant to modify an amount of at least one of a pharmacological, a biological, a biochemical, a narcotic, or a steroid delivered to the patient (Mire: Para. [0138], [0144]; Fig. 16).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the system of Ross to include a reservoir to modify an amount of a pharmacological, a biological, a biochemical, a narcotic, or a steroid delivered to the patient as taught by Mire to deliver drugs that may aid in increasing bone density and fusion of broken bones (Mire: Para. [0144]).
Conclusion
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/SHAWN CURTIS BROUGHTON/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
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