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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 11, 16, 28, and 33 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, 11-12, 19, and 22 of co-pending Application No. 18/872,185 in view of “Landon et al.,” US 2022/0160430 (hereinafter Landon).
Claims 11, 16, 28, and 33 of instant application and co-pending claims 1, 8, 11-12, 19, and 22 of 18/872,185 have been mapped as below.
Instant Application 18/872,550
Claims – 12/06/2024
Co-pending application 18/872,185
Claims as of 12/05/2024
Claim 11. (Original): A method for surgical planning, the method comprising:
obtaining, by a computing system, a first point cloud representing at least a portion of a bone;
applying, by the computing system, a point cloud neural network to generate a second point
cloud based on the first point cloud, the second point cloud comprising points representing an axis
along the bone; and generating, by the computing system, surgical planning information based on the second point cloud.
Claim 16. (Currently Amended):
Wherein applying the point cloud neural network comprises:
applying an input transform to a first array that comprises the first point cloud to generate
a second array, wherein the input transform is implemented using a first T-Net model;
applying a first multi-layer perceptron (MLP) to the second array to generate a third array;
applying a feature transform to the third array to generate a fourth array, wherein the input
transform is implemented using a second T-Net model;
applying a second MLP to the fourth array to generate a fifth array;
applying a max pooling layer to the fifth array to generate a global feature vector;
sampling N points in a unit square in 2-dimensions;
concatenating the sampled points with the global feature vector to obtain a combined
vector; and applying one or more third MLPs to generate points in the second point cloud.
Claim 28. (Currently Amended):
A system comprising:
a storage system configured to store a first point cloud representing at least a portion of a bone of a patient; and
processing circuitry configured to:
obtain the first point cloud representing at least the portion of the bone;
apply a point cloud neural network to generate a second point cloud based on the first point cloud, the second point cloud comprising points representing an axis along the bone; and generate surgical planning information based on the second point cloud.
Claim 33. (Currently Amended):
apply the point cloud neural network, the processing circuitry is configured to:
apply an input transform to a first array that comprises the first point cloud to generate a
second array, wherein the input transform is implemented using a first T-Net model;
apply a first multi-layer perceptron (MLP) to the second array to generate a third array;
apply a feature transform to the third array to generate a fourth array, wherein the input
transform is implemented using a second T-Net model;
apply a second MLP to the fourth array to generate a fifth array;
apply a max pooling layer to the fifth array to generate a global feature vector;
sample N points in a unit square in 2-dimensions;
concatenate the sampled points with the global feature vector to obtain a combined vector;
and apply one or more third MLPs to generate points in the second point cloud.
Claim 1. (Original): A method for estimating landmarks on a morbid bone, the method
comprising:
obtaining, by a computing system, a first point cloud representing one or more bones of a patient;
processing, by the computing system, the first point cloud using one or more point cloud neural networks to generate an output point cloud, the output point cloud including labels indicating locations of one or more landmarks on the one or more bones of the patient; and
outputting, by the computing system, the output point cloud.
Claim 8. (Original): The method of claim 1, wherein processing, by the computing system, the
first point cloud using one or more point cloud neural networks comprises:
applying an input transform to a first array that comprises the first point cloud to generate
a second array, wherein the input transform is implemented using a first T-Net model;
applying a first multi-layer perceptron (MLP) to the second array to generate a third array;
applying a feature transform to the third array to generate a fourth array, wherein the input
transform is implemented using a second T-Net model;
applying a second MLP to the fourth array to generate a fifth array;
applying a max pooling layer to the fifth array to generate a global feature vector;
sampling N points in a unit square in 2-dimensions;
concatenating the sampled points with the global feature vector to obtain a combined vector; and applying one or more third MLPs to generate points in the output point cloud.
Claim 11. (Original): The method of claim 1, further comprising:
determining one or more of a tibia mechanical axis, a tibia anatomical axis, a tibia axial plane, a tibia medial gutter line, a fibula lateral gutter line, a tibial mortise AP (anteroposterior) axis, or a tibial mortise ML (mediolateral) axis based on the locations of the one or more landmarks.
Claim 12. (Original): A computing system configured to estimate landmarks on a morbid bone,
the computing system comprising:
a memory configured to store a first point cloud representing one or more bones of a
patient; and
one or more processors in communication with the memory, the one or more processors configured to:
obtain the first point cloud representing the one or more bones of the patient;
process the first point cloud using one or more point cloud neural networks to generate an output point cloud, the output point cloud including labels indicating locations
of one or more landmarks on the one or more bones of the patient; and output the output point cloud.
Claim 19. (Original): The computing system of claim 12, wherein to process the first point cloud using one or more point cloud neural networks, the one or more processors are configured to:
apply an input transform to a first array that comprises the first point cloud to generate a
second array, wherein the input transform is implemented using a first T-Net model;
apply a first multi-layer perceptron (MLP) to the second array to generate a third array;
apply a feature transform to the third array to generate a fourth array, wherein the input
transform is implemented using a second T-Net model;
apply a second MLP to the fourth array to generate a fifth array;
apply a max pooling layer to the fifth array to generate a global feature vector;
sample N points in a unit square in 2-dimensions;
concatenate the sampled points with the global feature vector to obtain a combined vector;
and apply one or more third MLPs to generate points in the output point cloud.
Claim 22. (Original): The computing system of claim 12, wherein the one or more processors are
further configured to:
determine one or more of a tibia mechanical axis, a tibia anatomical axis, a tibia axial plane,
a tibia medial gutter line, a fibula lateral gutter line, a tibial mortise AP (anteroposterior) axis, or
a tibial mortise ML (mediolateral) axis based on the locations of the one or more landmarks.
Regarding instant claim(s) 11, 16, 28, and 33, co-pending claim(s) 1, 11-12, 19, and 22 collectively set(s) forth the above-mapped underlined claims.
Claim 11 recites “points representing an axis along the bone,” while co-pending claim 1 recites “labels indicating locations of one or more landmarks on the one or more bones of the patient” and co-pending claim 11 recites “a tibia mechanical axis,” thus collectively set(s) forth the claim 11 of instant application.
Claim 11 further teaches the limitation of “generating surgical planning information based on the second point cloud points representing an axis along the bone” while co-pending claim 1 recites “outputting the output point cloud” collectively with co-pending claim 11 recites “a tibia mechanical axis, a tibia anatomical axis, a tibia axial plane, a tibia medial gutter line, a fibula lateral gutter line, a tibial mortise AP (anteroposterior) axis, or a tibial mortise ML (mediolateral) axis.” All specific examples of axis along the bones in co-ending claim 11 are within the scope of “an axis along the bone” of instant application’s claim 11. Thus, is a species of the generic invention of claim 11 of instant application. It has been held that the generic invention is anticipated by the species of the co-pending claim.
Claim 11 of instant application further teaches the limitation of “generating surgical planning information based on the second point cloud points representing an axis along the bone” while co-pending claim 1 recites “outputting the output point cloud.”
However, in the analogous field of endeavor in image registration using mask, Landon teaches surgical planning information based on the second point cloud (surgical plan based on the model and analysis, such as axis of the bone [0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by co-pending claim(s) 1 to be as claimed in the instant application, since such limitations were well known in the art as made obvious by Landon. One of ordinary skill in the art could have combined the elements as claimed by known methods (e.g. generating surgical plan based on the identified landmark, such as axis of the bone) with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide patient specific planned surgical intervention ([0120]), and there was reasonable expectation of success.
Regarding instant claim(s) 16, co-pending claim 8 set(s) forth the above mapped and underlined claims.
Regarding instant claim(s) 28, co-pending claim(s) 12 and 22 collectively set(s) forth the above-mapped underlined claims.
Claim 28 recites “a storage system” while co-pending claim recites “a memory.” A memory falls within the scope of “a storage system” thus, is a species of the generic invention of claim 28 of instant application. It has been held that the generic invention is anticipated by the species of the co-pending claim.
Claim 28 further teaches the limitation of “generating surgical planning information based on the second point cloud points representing an axis along the bone” while co-pending claim 12 recites “outputting the output point cloud” collectively with co-pending claim 22 recites “a tibia mechanical axis, a tibia anatomical axis, a tibia axial plane, a tibia medial gutter line, a fibula lateral gutter line, a tibial mortise AP (anteroposterior) axis, or a tibial mortise ML (mediolateral) axis.” All specific examples of axis along the bones in co-ending claim 22 are within the scope of “an axis along the bone” of instant application’s claim 28. Thus, is a species of the generic invention of claim 28 of instant application. It has been held that the generic invention is anticipated by the species of the co-pending claim.
Claim 28 further teaches the limitation of “generating surgical planning information based on the second point cloud points representing an axis along the bone” while co-pending claim 1 recites “outputting the output point cloud.”
However, in the analogous field of endeavor in image registration using mask, Landon teaches surgical planning information based on the second point cloud (surgical plan based on the model and analysis, such as axis of the bone [0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by co-pending claim(s) 1 and 11 to be as claimed in the instant application, since such limitations were well known in the art as made obvious by Landon. One of ordinary skill in the art could have combined the elements as claimed by known methods (e.g. generating surgical plan based on the identified landmark, such as axis of the bone) with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide patient specific planned surgical intervention ([0120]), and there was reasonable expectation of success.
Regarding instant claim(s) 33, co-pending claim 19 set(s) forth the above mapped and underlined claims.
This is a provisional nonstatutory double patenting rejection.
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.
Claim(s) 11-15, 17, 28-32, 34, and 36-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes - The claims recite a method for surgical planning and therefore, is a method.
Step 2A, Prong 1, Judicial Exception: Yes - The claim recites the limitations:
“generate a second point cloud based on the first point cloud, the second cloud comprising points representing an axis along the bone.”
“generating, surgical planning information based on the second point cloud.
This limitation, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it is regarding a concept relating to the planning surgical information based on observation of the data. The examiner submits that in light of specification, the point cloud is interpreted as an image content (paragraph [0023] of instant application discloses that image content as first and second point cloud). Thus, a surgeon can observe the image content, and determine and generate the areas of interest of bones, and make judgement which portion of the bone is to be the axis along the bone, using mental framework to determine axis of the bone by applying geometrical relationship and symmetry (mathematical concepts). Moreover, using mental process of determining axis of the bone using judgement, and can mentally determine how to plan and proceed with surgical procedures. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements:
“obtaining, by a computing system, a point cloud representing at least a portion of bone,”
“applying, by the computing system, a point cloud neural network to generate a second point cloud based on the first point cloud, the second cloud comprising points representing an axis along the bone.”
“generating, by the computing system,”
Obtaining a point cloud (image) is data gathering and is a form of a pre-solution insignificant activity. “generating surgical planning information using computing device” is displaying or outputting the result and is a form of a -post-solution insignificant activity. The use of computing device for applying a neural network and using a neural network are recited with high generality, and does not integrate the judicial exception into a practical application as it is merely used to perform the judicial exception.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claim 11 is ineligible.
Claim 12-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 and Step 2A, Prong 1, Judicial Exception are discussed above in the claim 11 rejection.
Claims 12-15 recites the following elements:
“point cloud comprising points representing a tibia mechanical axis that forms a line passing through a tibia plafond landmark and a center of proximal tibia spines”
“first point cloud represents less than an entirety of the bone,”
“wherein the bone comprises a tibia, wherein the first point cloud comprises points representing a distal end of the tibia,”
“generating information for a Mixed Realty visualization of at least the axis along the bone.”
These claim elements are mere data collection and data of the bone, distal end of the tibia and output data of tibia mechanical axis which amounts to a pre-solution insignificant activity. In addition, a tibia mechanical axis forming line is a data output, and displaying the output on mixed realty visualization are displaying steps which amounts to a post-solution insignificant activity. This pre and post-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step. In light of above, claims 12 -15 are ineligible.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 and Step 2A, Prong 1, Judicial Exception are discussed above in the claim 11 rejection.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements: “generating training datasets based on bones of historic patients; and training the point cloud neural network using the training datasets.”
“generating datasets” is data collection steps which amounts to a pre-solution insignificant activity and training neural network using the training datasets are merely data collection steps of training the neural network with datasets and neural networks are recited with high generality, and does not integrate the judicial exception into a practical application as it is merely used to perform the judicial exception.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claim 17 is ineligible.
Claim 28 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes - The claims recite a system and therefore, is an apparatus.
Step 2A, Prong 1, Judicial Exception: Yes - The claim recites the limitations:
“generate a second point cloud based on the first point cloud, the second cloud comprising points representing an axis along the bone.”
“generating, surgical planning information based on the second point cloud.
This limitation, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it is regarding a concept relating to the planning surgical information based on observation of the data. The examiner submits that in light of specification, the point cloud is interpreted as an image content (paragraph [0023] of instant application discloses that image content as first and second point cloud). Thus, a surgeon can observe the image content, and determine and generate the areas of interest of bones, and make judgement which portion of the bone is to be the axis along the bone, using mental framework to determine axis of the bone by applying geometrical relationship and symmetry (mathematical concepts). Moreover, using mental process of determining axis of the bone using judgement, and can mentally determine how to plan and proceed with surgical procedures. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements:
“a storage system configured to store a first point cloud representing at least a portion of a bone of a patient,”
“Processing circuitry configured to apply a point cloud neural network to generate a second point cloud based on the first point cloud, the second cloud comprising points representing an axis along the bone.”
Obtaining a point cloud (image) is data gathering and is a form of a pre-solution insignificant activity. “generating surgical planning information using computing device” is displaying or outputting the result and is a form of a -post-solution insignificant activity. The use of storage system to store and processing circuitry for applying a neural network and using a neural network are recited with high generality, and does not integrate the judicial exception into a practical application as it is merely used to perform the judicial exception.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claim 28 is ineligible.
Claim 29-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 and Step 2A, Prong 1, Judicial Exception are discussed above in the claim 28 rejection.
Claims 29-32 further recite the following elements:
“point cloud comprising points representing a tibia mechanical axis that forms a line passing through a tibia plafond landmark and a center of proximal tibia spines”
“first point cloud represents less than an entirety of the bone,”
“wherein the bone comprises a tibia, wherein the first point cloud comprises points representing a distal end of the tibia,”
“generating information for a Mixed Realty visualization of at least the axis along the bone.”
These claim elements are mere data collection and data of the bone, distal end of the tibia and output data of tibia mechanical axis which amounts to a pre-solution insignificant activity. In addition, a tibia mechanical axis forming line is a data output, and displaying the output on mixed realty visualization are displaying steps which amounts to a post-solution insignificant activity. Moreover, use of processing circuitry is recited with high generality and is implemented for mere applying the neural network. This pre and post-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step. In light of above, claims 29-32 are ineligible.
Claim 34 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 and Step 2A, Prong 1, Judicial Exception are discussed above in the claim 28 rejection.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements: “processing circuitry configured to train the point cloud neural network, wherein to train the point cloud neural network, the processing circuitry is configured to generate training datasets based on bones of historic patients; and training the point cloud neural network using the training datasets.”
“generating datasets” is data collection steps which amounts to a pre-solution insignificant activity and training neural network using the training datasets are merely data collection steps of training the neural network with datasets and neural networks are performed by processing circuitry and is recited with high generality, and does not integrate the judicial exception into a practical application as it is merely used to perform the judicial exception.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claim 34 is ineligible.
Claim 36 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes - The claims recite a non-transitory computer-readable storage medium storing instructions thereon that when executed cause one or more processors and therefore, is an apparatus.
Step 2A, Prong 1, Judicial Exception: Yes - The claim recites the limitations:
“generate a second point cloud based on the first point cloud, the second cloud comprising points representing an axis along the bone.”
“generating, surgical planning information based on the second point cloud.
This limitation, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it is regarding a concept relating to the planning surgical information based on observation of the data. The examiner submits that in light of specification, the point cloud is interpreted as an image content (paragraph [0023] of instant application discloses that image content as first and second point cloud). Thus, a surgeon can observe the image content, and determine and generate the areas of interest of bones, and make judgement which portion of the bone is to be the axis along the bone, using mental framework to determine axis of the bone by applying geometrical relationship and symmetry (mathematical concepts). Moreover, using mental process of determining axis of the bone using judgement, and can mentally determine how to plan and proceed with surgical procedures. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements:
“A non-transitory computer-readable storage medium storing instructions thereon that when executed cause one or more processors to”
“obtain a first point cloud representing at least a portion of a bone of a patient,”
“apply a point cloud neural network to generate a second point cloud based on the first point cloud, the second cloud comprising points representing an axis along the bone.”
Obtaining a point cloud (image) is data gathering and is a form of a pre-solution insignificant activity. The use of storage medium to store instructions and processor for applying a neural network and using a neural network are recited with high generality, and does not integrate the judicial exception into a practical application as it is merely used to perform the judicial exception.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claim 36 is ineligible.
Claims 37-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 and Step 2A, Prong 1, Judicial Exception are discussed above in the claim 28 rejection.
Claims 37-40 further recite the following elements:
“point cloud comprising points representing a tibia mechanical axis that forms a line passing through a tibia plafond landmark and a center of proximal tibia spines”
“first point cloud represents less than an entirety of the bone,”
“wherein the bone comprises a tibia, wherein the first point cloud comprises points representing a distal end of the tibia,”
“generating information for a Mixed Realty visualization of at least the axis along the bone.”
These claim elements are mere data collection and data of the bone, distal end of the tibia and output data of tibia mechanical axis which amounts to a pre-solution insignificant activity. In addition, a tibia mechanical axis forming line is a data output, and displaying the output on mixed realty visualization are displaying steps which amounts to a post-solution insignificant activity. Moreover, use of non-transitory computer readable medium with processors is used for applying the limitation and is recited with high generality and is implemented for mere applying the neural network. This pre and post-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step. In light of above, claims 37-40 are ineligible.
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.
Claims 11, 13-15, 17, 28, 30-32, 34, 36, and 38-40 are rejected under 35 U.S.C. 103 as being unpatentable over “Landon et al.,” US 2022/0160430 (hereinafter Landon) and “Lang et al.,” US 2022/0133484 (hereinafter Lang), and “Mashita et al.,” US 2022/0343553 (hereinafter Mashita).
Regarding to claim 11, Landon teaches a method for surgical planning, the method comprising:
obtaining, by a computing system, a first point cloud representing at least a portion of a bone (set of key points for calculating a pre-determined set of properties of the bones [0234]; point cloud of potential positions for the corresponding points across a plurality of 3D bone models in the library [0256]);
applying, by the computing system, a point cloud neural network to generate a second point cloud based on the first point cloud, the second point cloud comprising points representing an axis along the bone (calculate one or more properties of the bones of the patient, such as anatomical axis and mechanical axis [0234]; one of more key points are identified using machine learning, artificial networks); and
generating, by the computing system, surgical planning information based on the second point cloud ([0086]).
Landon does not explicitly disclose using a neural network to generate points representing an axis along the bone as claimed.
However, in the analogous field of endeavor in planning surgical procedures for bone, Lang discloses using artificial neural network to train set of objects, properties such as a mechanical axis of a femur and used for aligning virtual implant component in relationship to one or more mechanical axis, such as a mechanical axis of a tibia ([0035]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the point clouds as taught by Landon to incorporate teaching of Lang, since using ANN to train data to identify a mechanical axis of a tibia was well known in the art as taught by Lang. One of ordinary skill in the art could have combined the elements as claimed by Landon with no change in their respective functions, configuring ANN to implement identification of axis of the bone based on Landon’s data clouds of bone, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide surgeon an alignment of implant ([0035]), and there was reasonable expectation of success.
Landon and Lang do not explicitly disclose generate a second point cloud based on the first point cloud using a point cloud neural network.
However, in the analogous field of endeavor in point cloud data, Mashita teaches “PointNet” which is a deep neural network for 3-dimensional point cloud data being input and outputs the point cloud data features ([0105]-[0106]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the point clouds as taught by Landon and Lang to incorporate teaching of Mashita, since using ANN to train data to identify a mechanical axis of a tibia was disclosed by Lang, and point cloud inputs and outputs were well known in the art as taught by Mashita. One of ordinary skill in the art could have combined the elements as claimed by Landon and Lang with no change in their respective functions, configuring its neural network to be PointNet to use point cloud of a bone as an input and output point cloud feature of being mechanical axis of a tibia, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide output point cloud data features ([0105]), and there was reasonable expectation of success.
Regarding to claims 13-14, Landon, Lang, and Mashita together teach all limitations of claim 11 as discussed above.
Landon further teaches the bony landmarks to be distal tibia for determining mechanical axis of the tibia ([0075]) and point cloud is a point selected on the 3D bone model ([0256] Figure 27 shows visually point clouds that are less than the entirety of the bone and distal portion of the tibia as claimed).
Regarding to claim 15, Landon, Lang, and Mashita together teach all limitations of claim 11 as discussed above.
Landon teaches generating the surgical planning information comprises generating information for a Mixed Reality visualization of at least the axis along the bone (axis of the bone, Figure 27,[0075] and [0256]) and displaying in augmented realty head mounted device ([0077]).
Regarding to claim 17, Landon, Lang, and Mashita together teach all limitations of claim 11 as discussed above.
Lang further teaches training artificial neural network, training data set comprising preoperative data of the patient, including patient history and medical images as well as clinical assessments, patient outcome measurements ([0053]).
Regarding to claim 28, Landon teaches a system comprising:
a storage system configured to store a first point cloud representing at least a portion of a bone of a patient (points in the point cloud in image is stored in the library [0256]); and
processing circuitry ([0019], [0078], [0124]) configured to:
obtain the first point cloud representing at least the portion of the bone (set of key points for calculating a pre-determined set of properties of the bones [0234]; point cloud of potential positions for the corresponding points across a plurality of 3D bone models in the library [0256]);
apply a point cloud neural network to generate points representing an axis along the bone (calculate one or more properties of the bones of the patient, such as anatomical axis and mechanical axis [0234]; one of more key points are identified using machine learning, artificial networks); and
generate surgical planning information based on the second point cloud ([0086]).
Landon does not explicitly disclose using a neural network to generate points representing an axis along the bone as claimed.
However, in the analogous field of endeavor in planning surgical procedures for bone, Lang discloses using artificial neural network to train set of objects, properties such as a mechanical axis of a femur and used for aligning virtual implant component in relationship to one or more mechanical axis, such as a mechanical axis of a tibia ([0035]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the point clouds as taught by Landon to incorporate teaching of Lang, since using ANN to train data to identify a mechanical axis of a tibia was well known in the art as taught by Lang. One of ordinary skill in the art could have combined the elements as claimed by Landon with no change in their respective functions, configuring ANN to implement identification of axis of the bone based on Landon’s data clouds of bone, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide surgeon an alignment of implant ([0035]), and there was reasonable expectation of success.
Landon and Lang do not explicitly disclose generate a second point cloud based on the first point cloud using a point cloud neural network.
However, in the analogous field of endeavor in point cloud data, Mashita teaches “PointNet” which is a deep neural network for 3-dimensional point cloud data being input and outputs the point cloud data features ([0105]-[0106]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the point clouds as taught by Landon and Lang to incorporate teaching of Mashita, since using ANN to train data to identify a mechanical axis of a tibia was disclosed by Lang, and point cloud inputs and outputs were well known in the art as taught by Mashita. One of ordinary skill in the art could have combined the elements as claimed by Landon and Lang with no change in their respective functions, configuring its neural network to be PointNet to use point cloud of a bone as an input and output point cloud feature of being mechanical axis of a tibia, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide output point cloud data features ([0105]), and there was reasonable expectation of success.
Regarding to claims 30-31, Landon, Lang, and Mashita together teach all limitations of claim 28 as discussed above.
Landon further teaches the bony landmarks to be distal tibia for determining mechanical axis of the tibia ([0075]) and point cloud is a point selected on the 3D bone model ([0256] Figure 27 shows visually point clouds that are less than the entirety of the bone and distal portion of the tibia as claimed).
Regarding to claim 32, Landon, Lang, and Mashita together teach all limitations of claim 11 as discussed above.
Landon teaches generating the surgical planning information comprises generating information for a Mixed Reality visualization of at least the axis along the bone (axis of the bone, Figure 27,[0075] and [0256]) and displaying in augmented realty head mounted device ([0077]).
Regarding to claim 34, Landon, Lang, and Mashita together teach all limitations of claim 28 as discussed above.
Lang further teaches training artificial neural network, training data set comprising preoperative data of the patient, including patient history and medical images as well as clinical assessments, patient outcome measurements ([0053]).
Regarding to claim 36, Landon teaches a non-transitory computer-readable storage medium storing instructions thereon that when executed cause one or more processors ([0019]) to:
a storage system configured to store a first point cloud representing at least a portion of a bone of a patient (points in the point cloud in image is stored in the library [0256]); and
processing circuitry ([0019], [0078], [0124]) configured to:
obtain the first point cloud representing at least the portion of the bone (set of key points for calculating a pre-determined set of properties of the bones [0234]; point cloud of potential positions for the corresponding points across a plurality of 3D bone models in the library [0256]);
apply a point cloud neural network to generate points representing an axis along the bone (calculate one or more properties of the bones of the patient, such as anatomical axis and mechanical axis [0234]; one of more key points are identified using machine learning, artificial networks); and
generate surgical planning information based on the second point cloud ([0086]).
Landon does not explicitly disclose using a neural network to generate points representing an axis along the bone as claimed.
However, in the analogous field of endeavor in planning surgical procedures for bone, Lang discloses using artificial neural network to train set of objects, properties such as a mechanical axis of a femur and used for aligning virtual implant component in relationship to one or more mechanical axis, such as a mechanical axis of a tibia ([0035]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the point clouds as taught by Landon to incorporate teaching of Lang, since using ANN to train data to identify a mechanical axis of a tibia was well known in the art as taught by Lang. One of ordinary skill in the art could have combined the elements as claimed by Landon with no change in their respective functions, configuring ANN to implement identification of axis of the bone based on Landon’s data clouds of bone, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide surgeon an alignment of implant ([0035]), and there was reasonable expectation of success.
Landon and Lang do not explicitly disclose generate a second point cloud based on the first point cloud using a point cloud neural network.
However, in the analogous field of endeavor in point cloud data, Mashita teaches “PointNet” which is a deep neural network for 3-dimensional point cloud data being input and outputs the point cloud data features ([0105]-[0106]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the point clouds as taught by Landon and Lang to incorporate teaching of Mashita, since using ANN to train data to identify a mechanical axis of a tibia was disclosed by Lang, and point cloud inputs and outputs were well known in the art as taught by Mashita. One of ordinary skill in the art could have combined the elements as claimed by Landon and Lang with no change in their respective functions, configuring its neural network to be PointNet to use point cloud of a bone as an input and output point cloud feature of being mechanical axis of a tibia, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide output point cloud data features ([0105]), and there was reasonable expectation of success.
Regarding to claims 38-39, Landon, Lang, and Mashita together teach all limitations of claim 36 as discussed above.
Landon further teaches the bony landmarks to be distal tibia for determining mechanical axis of the tibia ([0075]) and point cloud is a point selected on the 3D bone model ([0256] Figure 27 shows visually point clouds that are less than the entirety of the bone and distal portion of the tibia as claimed).
Regarding to claim 40, Landon, Lang, and Mashita together teach all limitations of claim 36 as discussed above.
Landon teaches generating the surgical planning information comprises generating information for a Mixed Reality visualization of at least the axis along the bone (axis of the bone, Figure 27,[0075] and [0256]) and displaying in augmented realty head mounted device ([0077]).
Claims 12, 29, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Landon, Lang and Mashita as applied to claims 11, 28, and 36 above, and further in view of “Nguyen et al.,” US 2015/0342516 (hereinafter Nguyen).
Landon, Lang and Mashita together teach all limitations of claim 11, 28, and 36 as discussed above.
Landon, Lang and Mashita together disclose applying the point cloud neural network to generate the second point cloud based on the first point cloud, the second point cloud comprising points representing a tibia mechanical axis (Lang [0035]), but do not further teach axis is axis that forms a line passing through a tibia plafond landmark and a center of proximal tibia spines.
However, the examiner submits that the limitation is a definition of a mechanical axis of tibia.
The examiner submits “Nguyen” which specifically discloses that mechanical axis of the tibia is defined by a line extending between a proximal point at the center of the tibial plateau (interspinous intercruciate midpoint) and a distal point located at the center of the tibial plafond ([0025], [0041] Figures 1A and 6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the point clouds as taught by Landon, Lang, and Mashita to incorporate teaching of Nguyen, since definition of mechanical axis of tibia was well known in the art as taught by Nguyen. One of ordinary skill in the art could have combined the elements as claimed by Landon and Lang with no change in their respective functions, configuring its point cloud to be tibia and Lang’s neural network to identify points relating to axis of the tibia, and the axis to be line passing between a proximal point at the center of the tibial plateau (interspinous intercruciate midpoint) and a distal point located at the center of the tibial plafond as disclosed by Nguyen, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide accurate mechanical axis of the tibia for surgery ([0025] and [0041]), and there was reasonable expectation of success.
Claim(s) 16, 33, and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Landon, Lang and Mashita as applied to claims 11, 28, and, 36 above, and further in view of “Gonzales et al.,” “An In-Depth Look at PointNet,” (hereinafter Gonzales, IDS).
Regarding to claim 16, 33, and 41, Landon, Lang and Mashita together teach all limitations of claims 11, 28, and 36 as discussed above.
Mashita teaches PointNet, but does not further disclose details of the applying the point cloud neural network.
However, in the analogous field of endeavor in deep learning method, Gonzales discloses PointNet, which is a point cloud neural network as claimed, and further comprising following limitations of applying the point cloud neural network comprises PointNet architecture in figure 2 disclosing following limitations:
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applying an input transform to a first array that comprises the first point cloud to generate a second array, wherein the input transform is implemented using a first T-Net model (first array [Wingdings font/0xE0]input transform[Wingdings font/0xE0] 2nd array; T-Net model page 4 Figure 2);
applying a first multi-layer perceptron (MLP) to the second array to generate a third array (mlp to generate third array);
applying a feature transform to the third array to generate a fourth array, wherein the input transform is implemented using a second T-Net model (feature transform using T-Net model to generate a fourth array);
applying a second MLP to the fourth array to generate a fifth array (mlp to generate 5th array);
applying a max pooling layer to the fifth array to generate a global feature vector (max pooling to generate global feature);
sampling N points in a unit square in 2-dimensions (dimensionality is reduced with FC layers page 11; max pooling output compresses n points to a subset of points page, 13);
concatenating the sampled points with the global feature vector to obtain a combined vector (final FC layer are them combined with globally trainable weights resulting in 3 by 3 transformation matrix, page 11); and
applying one or more third MLPs to generate points in the second point cloud (applying mlp to output score)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the point clouds as taught by Landon, Lang, and Mashita to incorporate teaching of Gonzales, since details of PointNet algorithm was well known in the art as taught by Gonzales. One of ordinary skill in the art could have combined the elements as claimed by Landon and Lang in view of Mashita, with no change in their respective functions, configuring its PointNet to include algorithms of Gonzales, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide highly efficient and effective PointNet for classification and local points features for segmentation (page 7) and there was reasonable expectation of success.
Conclusion
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/PATRICIA J PARK/Primary Examiner, Art Unit 3798
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