DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 07 July 2026 have been fully considered but they are not persuasive.
Applicant’s arguments, see page 11, filed 7 July 2026, with respect to double patenting rejection have been fully considered and are persuasive in view of amendment. The double patenting rejection of 7 July 2026 has been withdrawn.
With respect to 101 rejections, applicant argues that claim require specific technologically rooted operations such as “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,” which a human cannot practically perform these complex artificial neural network operations, which involve processing three-dimensional point clouds to predict specific spatial outputs, in human minds, and the steps are inherently tied to computer technology and point cloud data structures, precluding such features being performed mentally or with pen and paper (pages 8-9). Moreover, applicant further argues that specification describes practical and technological advantages of claimed features, and claim recites a tangible improvement to the functioning of computing system and surgical planning process by “without reconstructing the entirety of the bone” and “the points representing the axis along the bone including points that are not part of the first point cloud,” results in bypassing of computational expensive and complex steps of reconstruction of the bone. Further, applicant argues that in paragraph [0066] supports integration into advanced visualization technology, as it allows a surgeon to see axis along the bone which was not available on the bone (pages 9-10). Moreover, “less than an entirety of a bone” and applying a point cloud neural network to generate points axis along the bone, is novel and unconventional, thus eligible under 1012 (page 10).
However, the examiner respectfully disagrees. First, the claim limitation of applying by the computing system” is not the limitation that the examiner indicated for mental framework. Rather, the examiner has indicated that generating “point cloud” which includes points representing an axis along the bone, the points representing the axis along the bone including points that are not part of the first point cloud.” Thus, except for “applying using a computing device,” a surgeon can observe the image of a portion of the bone, such as distal tip of the tibia in the image, and mentally perform geometric evaluation on determining axis along the bone, which may include points are not all from distal end of the tibia, but can include points outside of the distal end of the tibia. These steps can be performed using evaluation, judgement and drawing a conclusion based on the observation, and a surgeon can use ruler and pen on the image, to draw out an axis comprising many points, that are not all from the bone and have performed task of generating “the second cloud comprising points” as claimed without using any computer. Thus, the limitations still recite “mental process type” abstract idea.
In addition, “applying by the computing system” is recited with high generality, and is a mere collection of data steps that are necessary precursor for performing generation of second point cloud comprising axis of the bone, Thus, using a computing system for applying the abstract idea is presented with high level of generality, that it does not integrate into practical application.
With inventive concept, the examiner submits that specification provides a computing system as personal computers, smartphones ([0029]) which indicates that it is commercially available products (such as iphone), thus describes computing device and applying and obtaining with computing unit as well-known, conventional and commercially available products.
With respect to amended limitation of “generating for display a visualization of at least the axis along the bone for aligning an implant,” the examiner notes that actual alignment of implant is not recited positively, thus, only displaying a result of generating second point cloud, is considered as insignificant post solution activity.
Thus, 101 is proper and maintained (The examiner has modified 101 rejection to include amended limitations).
Applicant’s arguments for 103 rejections with respect to amended claims 11, 28, and 36 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument (The examiner cited new paragraphs of prior arts for disclosing amended limitation). The examiner has modified rejection with Landon’s teaching for amended limitation.
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 11-12, 14-15, 17, 28-29, 31-32, 34, 36,-37, 39-40 and 42-43 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 without reconstructing the entirety of the bone, the second cloud comprising points representing an axis along the bone, the points representing the axis along the bone including points that are not part of the first 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 and mathematical concept of data manipulation (e.g. interpolation). 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). In addition, surgeon can evaluate the image that has portion of the bone (e.g. distal and proximal of the tibia) and estimate axis of the bone by using data manipulation, such as interpolation. 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 that is less than an entirety 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 cloud comprising points representing an axis along the bone.”
“generating, by the computing system for display a visualization of the at least the axis along the bone for aligning an implant,”
Obtaining a point cloud (image) is data gathering and is a form of a pre-solution insignificant activity. “generating by the computing device for display a visualization of the axis along the bone for aligning an implant” is displaying or outputting the result and is a form of a post-solution insignificant activity. Moreover, visualization of the axis along the bone for aligning an implant is recited as intended use, without positively reciting aligning step. 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 claim recites additional claim elements:
“obtaining, by a computing system, a point cloud representing at least a portion of that is less than an entirety 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 cloud comprising points representing an axis along the bone.”
“generating, by the computing system for display a visualization of the at least the axis along the bone for aligning an implant,”
With inventive concept, the examiner submits that specification provides a computing system as personal computers, smartphones ([0029]) which indicates that it is commercially available products (such as iphone), thus describes computing device and applying and obtaining as well as generating display with computing unit as well-known, conventional and commercially available products.
Using “a point cloud neural network” is considered to be well known, conventional routine as Lang (US2021/0192759) teaches algorithms includes deep learning or other intelligence for processing data, point cloud data are known in the art and have been implemented in publicly and/or commercially available code libraries and programming interfaces ([0268]).
As described in specification and Lang, use of computing device and potin cloud neural network is well-understood, routine and conventional. Well-understood, routine and conventional elements/functions cannot provide “significantly more.”
For these reasons, there is no inventive concept in the claim. In light of the above, claim 11 is ineligible.
Claims 12, 14-15 and 42 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, 14-15 and 42 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”
“wherein the bone comprises a tibia, wherein the first point cloud comprises points representing a distal end of the tibia,”
“wherein the visualization is a Mixed Realty visualization”
“the portion that is less than the entirety of the bone excludes at least one of the tibia plafond landmark and the center of proximal tibia spines”
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. Details of the portion excluding at least one of the tibia plafond landmark and the center of proximal tibia spines is data gathering step. Point cloud comprising points, a tibia, distal end of tibia excluding tibia plafond landmark and center of proximal tibia spines is performed in order to gather data for the recited mental process step and is a necessary precursor for all uses of the recited abstract idea since no determination of axis along the bone can be carried out without first gathering necessary data (such as point clouds comprising points of a distal end of the tibia). Applying neural network is recited with high level of generality such that neural network acts as instructions being applied by generic hardware components, in executing data manipulation.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the claim recites additional claim 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”
“wherein the bone comprises a tibia, wherein the first point cloud comprises points representing a distal end of the tibia,”
“wherein the visualization is a Mixed Realty visualization”
“the portion that is less than the entirety of the bone excludes at least one of the tibia plafond landmark and the center of proximal tibia spines”
With inventive concept, the examiner submits that specification provides a mixed realty as Microsoft HOLOGEN, available from Microsoft Corporation ([0034]), which is commercially available products.
Using “a point cloud neural network” is considered to be well known, conventional routine as Lang (US2021/0192759) teaches algorithms includes deep learning or other intelligence for processing data, point cloud data are known in the art and have been implemented in publicly and/or commercially available code libraries and programming interfaces ([0268]).
As described in specification and Lang, use of mixed realty and point cloud neural network are well-understood, routine and conventional. Well-understood, routine and conventional elements/functions cannot provide “significantly more.”
For these reasons, there is no inventive concept in the claim.
In light of above, claims 12, 14-15 and 42 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: 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:
“Generating training datasets based on bones of historic patients; and training the point cloud neural network using the training datasets”
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 receiving and reviewing patient records such as bones of historic patients, mentally processing the historic patients’ bones and identifying patterns in notes and integrating this with current data to identify axis of the bone. Thus, a surgeon can observe the image contents of bones from historic patients and identify a pattern, such as axis along the bone based on the bone data, and apply logic/algorithm to predict and determine axis of the bone based on the bone data. 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: neural network
“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 recites “point cloud neural network”
Using “a point cloud neural network” is considered to be well known, conventional routine as Lang (US2021/0192759) teaches algorithms includes deep learning or other intelligence for processing data, point cloud data are known in the art and have been implemented in publicly and/or commercially available code libraries and programming interfaces ([0268]).
As described in specification and Lang, use of point cloud neural network are well-understood, routine and conventional. Well-understood, routine and conventional elements/functions cannot provide “significantly more.”
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 without reconstructing the entirety of the bone, the second cloud comprising points representing an axis along the bone, the points representing the axis along the bone including points that are not part of the first 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 and mathematical concept of data manipulation (e.g. interpolation). 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). In addition, surgeon can evaluate the image that has portion of the bone (e.g. distal and proximal of the tibia) and estimate axis of the bone by using data manipulation, such as interpolation. 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.”
“generate for display a visualization of at least the axis along the bone for aligning an implant”
Obtaining a point cloud (image) is data gathering and is a form of a pre-solution insignificant activity. “generating by the computing device for display a visualization of the axis along the bone for aligning an implant” is displaying or outputting the result and is a form of a post-solution insignificant activity. Moreover, visualization of the axis along the bone for aligning an implant is recited as intended use, without positively reciting aligning step. 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 claim recites additional claim elements:
“a storage system configured to store a first point cloud representing at least a portion of that is less than an entirety 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.”
“generate for display a visualization of the at least the axis along the bone for aligning an implant,”
With inventive concept, the examiner submits that specification provides a processing circuitry include microprocessors, and various software, hardware included in computing system such as personal computers, smartphones ([0029]) which indicates that it is commercially available products (such as iphone), thus describes computing device and applying and obtaining as well as generating display with computing unit as well-understood, conventional and commercially available products.
With a storage system, specification provides storage system may be formed by a varity of memory devices, RAM or any other memory devices ([0032]), which shows that storage system in form of RAM is well-understood, routine and conventional.
Using “a point cloud neural network” is considered to be well known, conventional routine as Lang (US2021/0192759) teaches algorithms includes deep learning or other intelligence for processing data, point cloud data are known in the art and have been implemented in publicly and/or commercially available code libraries and programming interfaces ([0268]).
As described in specification and Lang, use of computing device and potin cloud neural network is well-understood, routine and conventional. Well-understood, routine and conventional elements/functions cannot provide “significantly more.”
In light of the above, claim 28 is ineligible.
Claim 29, 31-32 and 43 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.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements:
Claims 29, 31-32 and 43 further recite the following elements:
“apply 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 that forms a line passing through a tibia plafond landmark and a center of proximal tibia spines”
“wherein the bone comprises a tibia, wherein the first point cloud comprises points representing a distal end of the tibia,”
“the visualization is a Mixed Realty visualization.”
“the portion that is less than the entirety of the bone excludes at least one of the tibia plafond landmark and the center of proximal tibia spines”
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. Details of the portion excluding at least one of the tibia plafond landmark and the center of proximal tibia spines is data gathering step. Point cloud comprising points, a tibia, distal end of tibia excluding tibia plafond landmark and center of proximal tibia spines is performed in order to gather data for the recited mental process step and is a necessary precursor for all uses of the recited abstract idea since no determination of axis along the bone can be carried out without first gathering necessary data (such as point clouds comprising points of a distal end of the tibia). Applying neural network is recited with high level of generality such that neural network acts as instructions being applied by generic hardware components, in executing data manipulation.
This pre and post-solution insignificant activity does not integrate the judicial exception into a practical application.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the claim recites additional claim elements:
“apply 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 that forms a line passing through a tibia plafond landmark and a center of proximal tibia spines”
“wherein the bone comprises a tibia, wherein the first point cloud comprises points representing a distal end of the tibia,”
“the visualization is a Mixed Realty visualization.”
“the portion that is less than the entirety of the bone excludes at least one of the tibia plafond landmark and the center of proximal tibia spines”
With inventive concept, the examiner submits that specification provides a mixed realty as Microsoft HOLOGEN, available from Microsoft Corporation ([0034]), which is commercially available products.
Using “a point cloud neural network” is considered to be well known, conventional routine as Lang (US2021/0192759) teaches algorithms includes deep learning or other intelligence for processing data, point cloud data are known in the art and have been implemented in publicly and/or commercially available code libraries and programming interfaces ([0268]).
As described in specification and Lang, use of mixed realty and point cloud neural network are well-understood, routine and conventional. Well-understood, routine and conventional elements/functions cannot provide “significantly more.”
For these reasons, there is no inventive concept in the claim.
In light of above, claims 29, 31-32 and 43 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: 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:
“Generating training datasets based on bones of historic patients; and training the point cloud neural network using the training datasets”
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 receiving and reviewing patient records such as bones of historic patients, mentally processing the historic patients’ bones and identifying patterns in notes and integrating this with current data to identify axis of the bone. Thus, a surgeon can observe the image contents of bones from historic patients and identify a pattern, such as axis along the bone based on the bone data, and apply logic/algorithm to predict and determine axis of the bone based on the bone data. 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: neural network
“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 recites “point cloud neural network”
Using “a point cloud neural network” is considered to be well known, conventional routine as Lang (US2021/0192759) teaches algorithms includes deep learning or other intelligence for processing data, point cloud data are known in the art and have been implemented in publicly and/or commercially available code libraries and programming interfaces ([0268]).
As described in specification and Lang, use of point cloud neural network are well-understood, routine and conventional. Well-understood, routine and conventional elements/functions cannot provide “significantly more.”
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 without reconstructing the entirety of the bone, the second cloud comprising points representing an axis along the bone, the points representing the axis along the bone including points that are not part of the first 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 and mathematical concept of data manipulation (e.g. interpolation). 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). In addition, surgeon can evaluate the image that has portion of the bone (e.g. distal and proximal of the tibia) and estimate axis of the bone by using data manipulation, such as interpolation. 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-transtiory computer-readable storage medium storing instructions thereon that when executed cause on or more processors to:”
“obtain a first point cloud representin a portion that is less than an entirety of a bone”
“ apply a point cloud neural network”
“generate for display a visualization of at least the axis along the bone for aligning an implant”
Obtaining a point cloud (image) is data gathering and is a form of a pre-solution insignificant activity. “generating by the computing device for display a visualization of the axis along the bone for aligning an implant” is displaying or outputting the result and is a form of a post-solution insignificant activity. Moreover, visualization of the axis along the bone for aligning an implant is recited as intended use, without positively reciting aligning step. 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 claim recites additional claim elements:
“a non-transtiory computer-readable storage medium storing instructions thereon that when executed cause on or more processors to:”
“obtain a first point cloud representin a portion that is less than an entirety of a bone”
“ apply a point cloud neural network”
“generate for display a visualization of at least the axis along the bone for aligning an implant”
With inventive concept, the examiner submits that specification provides a processing circuitry to operate defined by stored instructions include microprocessors, and various software, hardware included in computing system such as personal computers, smartphones ([0029]) which indicates that it is commercially available products (such as iphone), thus describes computing device and applying and obtaining as well as generating display with computing unit as well-understood, conventional and commercially available products.
With a storage medium, specification provides storage system may be formed by a variety of memory devices, RAM or any other memory devices ([0032]) and CD-ROM ([0095]), commercially available products, which shows that storage system in form of RAM is well-understood, routine and conventional.
Using “a point cloud neural network” is considered to be well known, conventional routine as Lang (US2021/0192759) teaches algorithms includes deep learning or other intelligence for processing data, point cloud data are known in the art and have been implemented in publicly and/or commercially available code libraries and programming interfaces ([0268]).
As described in specification and Lang, use of computing device, storage and point cloud neural network is well-understood, routine and conventional. Well-understood, routine and conventional elements/functions cannot provide “significantly more.”
In light of above, claim 36 is ineligible.
Claims 37 and 39-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements:
“apply 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 that forms a line passing through a tibia plafond landmark and a center of proximal tibia spines”
“wherein the bone comprises a tibia, wherein the first point cloud comprises points representing a distal end of the tibia”
“wherein the visualization is a Mixed reality visualization”
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. Point cloud comprising points, a tibia, distal end of tibia is performed in order to gather data for the recited mental process step and is a necessary precursor for all uses of the recited abstract idea since no determination of axis along the bone can be carried out without first gathering necessary data (such as point clouds comprising points of a distal end of the tibia). Applying neural network is recited with high level of generality such that neural network acts as instructions being applied by generic hardware components, in executing data manipulation.
This pre and post-solution insignificant activity does not integrate the judicial exception into a practical application.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the claim recites additional claim elements:
“apply 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 that forms a line passing through a tibia plafond landmark and a center of proximal tibia spines”
“wherein the bone comprises a tibia, wherein the first point cloud comprises points representing a distal end of the tibia,”
“the visualization is a Mixed Realty visualization.”
With inventive concept, the examiner submits that specification provides a mixed realty as Microsoft HOLOGEN, available from Microsoft Corporation ([0034]), which is commercially available products.
Using “a point cloud neural network” is considered to be well known, conventional routine as Lang (US2021/0192759) teaches algorithms includes deep learning or other intelligence for processing data, point cloud data are known in the art and have been implemented in publicly and/or commercially available code libraries and programming interfaces ([0268]).
As described in specification and Lang, use of mixed realty and point cloud neural network are well-understood, routine and conventional. Well-understood, routine and conventional elements/functions cannot provide “significantly more.”
For these reasons, there is no inventive concept in the claim.
In light of above, claims 37 and 39-40 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.
The following rejection has been modified in view of applicant's arguments and/or amendments.
Claims 11, 14-15, 17, 28, 31-32, 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 a portion of a bone that is less than an entirety 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]; a portion of bone [0211]; “bony landmarks can include distal tibia” [0075]; plurality of images include capturing an upper portion of a femur, lower portion of the femur and upper portion of the tibia [0232]; Figure 24A and B show image (data) that is less than an entirety of a bone as claimed);
applying, by the computing system, a point cloud neural network to generate a second point cloud based on the first point cloud without reconstructing the entirety of the bone ( key points such as axis determined from the images [0234]; Figures 24A and 24B show image, a portion of the image of the candidate bone to identify key points, axes, thus using image of part of the bone (Figure 24B), without reconstructing the entirety of the bone as claimed), the second point cloud comprising points representing an axis along the bone, the points representing the axis along the bone including points that are not part of the first point cloud (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, “create an axis line by associating one or more points with one or more other points [0234]; a key point can identify expected resection location or an expected position for implant with respect to anatomical features or landmarks, and key points may be located at pre-determined offset position from one or more features of landmarks, which reads on the points representing the axis along the bone including points that are not part of the first point cloud [0226]); and
generating, by the computing system, for display a visualization of at least the axis along the bone for aligning an implant ( display with any visualization for providing instructions in the image, display can depict mechanical and anatomical axes of the femur and tibia, ad dynamically updating and displaying how changes in surgical plan would impact the procedure and the final position and orientation of implants installed on the bone [0118]; [0226]).
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 claim 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 bone that is less than an entirety of a bone of a patient (points in the point cloud in image is stored in the library [0256]; a portion of bone [0211]; “bony landmarks can include distal tibia” [0075]; plurality of images include capturing an upper portion of a femur, lower portion of the femur and upper portion of the tibia [0232]; Figure 24A and B show image (data) that is less than an entirety of a bone as claimed); and
processing circuitry ([0019], [0078], [0124]) configured to:
apply a point cloud neural network to generate a second point cloud based on the first point cloud without reconstructing the entirety of the bone(key points such as axis determined from the images [0234]; Figures 24A and 24B show image, a portion of the image of the candidate bone to identify key points, axes, thus using image of part of the bone (Figure 24B), without reconstructing the entirety of the bone as claimed), 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), the points representing the axis along the bone including points that are not part of the first point cloud (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, “create an axis line by associating one or more points with one or more other points [0234]; a key point can identify expected resection location or an expected position for implant with respect to anatomical features or landmarks, and key points may be located at pre-determined offset position from one or more features of landmarks, which reads on the points representing the axis along the bone including points that are not part of the first point cloud [0226]);
generate for display a visualization of at least the axis along the bone for aligning an implant (display with any visualization for providing instructions in the image, display can depict mechanical and anatomical axes of the femur and tibia, ad dynamically updating and displaying how changes in surgical plan would impact the procedure and the final position and orientation of implants installed on the bone [0118]; [0226]).
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 claim 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], [0078] and [0124]) to:
obtain the first point cloud representing at least the portion of the bone that is less than an entirety of a bone of a patient (points in the point cloud in image is stored in the library [0256]; a portion of bone [0211]; “bony landmarks can include distal tibia” [0075]; plurality of images include capturing an upper portion of a femur, lower portion of the femur and upper portion of the tibia [0232]; Figure 24A and B show image (data) that is less than an entirety of a bone as claimed);
apply a point cloud neural network to generate a second point cloud based on the first point cloud without reconstructing the entirety of the bone(key points such as axis determined from the images [0234]; Figures 24A and 24B show image, a portion of the image of the candidate bone to identify key points, axes, thus using image of part of the bone (Figure 24B), without reconstructing the entirety of the bone as claimed), 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), the points representing the axis along the bone including points that are not part of the first point cloud (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, “create an axis line by associating one or more points with one or more other points [0234]; a key point can identify expected resection location or an expected position for implant with respect to anatomical features or landmarks, and key points may be located at pre-determined offset position from one or more features of landmarks, which reads on the points representing the axis along the bone including points that are not part of the first point cloud [0226]);
generate for display a visualization of at least the axis along the bone for aligning an implant (display with any visualization for providing instructions in the image, display can depict mechanical and anatomical axes of the femur and tibia, ad dynamically updating and displaying how changes in surgical plan would impact the procedure and the final position and orientation of implants installed on the bone [0118]; [0226]).
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, 37 and 42-43 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.
Regarding to claims 42-43, Landon, Lang and Mashita and in view of Nguyen together teach all limitations of claims 12 and 29 as set forth above.
Landon further teaches the portion less than the entirety of the bone excludes at least one of the tibia plafond landmark and the center of proximal tibia spines, since image 2035 B show image capturing upper portion of the tibia, thus, would not have tibia plafond landmark ([0232]).
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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zimmermann (US2022/0125517) teaches neural network for bone image data for classification of point cloud based on geometric analysis, including splitting axes ([0153]).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PATRICIA J PARK whose telephone number is (571)270-1788. The examiner can normally be reached Monday-Thursday 8 am - 3 pm.
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/PATRICIA J PARK/Primary Examiner, Art Unit 3798
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