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 .
Notice to Applicant
This communication is in response to the amendment filed 12/11/2025. Claims 1, 2, 4-7, and 15-19 have been amended. Claims 8-11 have been canceled. Claims 1-7 and 12-20 remain pending and have been examined.
Response to Arguments
A. Applicant's remarks with respect to the rejection of claims 1-20 under 35 USC 112(a) have been fully considered but they are not persuasive. Applicant asserts on page 11 of the response that “the claims, particularly as-amended, are sufficiently supported in the original disclosure.” Examiner respectfully disagrees.
While the rejection of claims 8-11 is moot due to cancelation of the claims, Examiner notes that Applicant provides no arguments supporting the assertion that the claims are sufficiently supported in the original disclosure. Examiner maintains the corresponding rejections on the grounds set out below in the rejection of claims 1-7 and 12-20 under 35 USC 112(a).
B. Applicant's remarks with respect to the rejection of claims 1-20 under 35 USC 101 have been fully considered but they are not persuasive. Applicant asserts on page 12 of the response that “the claims, particularly as-amended, are directed to patent-eligible subject matter.” Examiner respectfully disagrees.
While the rejection of claims 8-11 is moot due to cancelation of the claims, Examiner notes that Applicant provides no arguments supporting the assertion that the claims are directed to patent-eligible subject matter. Examiner maintains the corresponding rejection on the grounds set out below in the rejection of claims 1-7 and 12-20 under 35 USC 101.
C. Applicant's remarks with respect to the rejection of claims 1-20 under 35 USC 102/103 have been fully considered. The correspond rejection of claim 1 under 35 USC 103 has been withdrawn based on the incorporation of the subject matter previously recited in claims 10 and 11 which were not rejected in view of the prior art of record. The rejection of claims 2-7 and 12-20 is withdrawn based on their dependency from claim 1. With respect to claims 17 and 19, Examiner has construed the limitations reciting “construct the tool according to claim 1” in claim 17 and “using the tool prepared with the method according to claim 1” in claim 19 as requiring performance of all subject matter recited in claim 1.
Claim Objections
The previous objection to claims 1, 4, 6, 11, 17, and 19 is withdrawn based on the amendment filed 12/11/2025.
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 the1reof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-7 and 12-15 and 19 are drawn to methods, while claims 17-18 are drawn to a system, each of which is within the four statutory categories.
However, claims 16 and 20 are rejected under Step 1 because the claims do not fall within at least one of the four categories of patent eligible subject matter.
Claims 16 and 20 are directed to “[a] computer program, a computer database and/or executable instructions saveable as at least one chosen from downloadable applications, downloadable programs, and programs on external units,” the broadest reasonable interpretation of which covers products that do not have a physical or tangible form, such as information (i.e. "data per se") or a computer program per se (i.e. "software per se"). See MPEP 2106.03(I). However, claims 16 and 20 have been included in the analysis below for Steps 2A and 2B in the interest of promoting compact prosecution.
Step 2A(1)
Claim 1 recites, in part, performing the steps of:
analyzing at least three of the following parameters in any combination:
average time spent on, behind or before a target during a trial, expressed as percentage of trial duration,
smoothness of movement index,
amplitude accuracy, calculated as average difference between a target and a body part’s actual position expressed in millimetres or degrees,
global, absolute, or relative repositioning error from movement directions measured in at least 2D space,
variable repositioning error from movement directions for each joint measured in at least 2D space,
performing a principal component analysis for the test or tests and the set of parameters measured in the previous step, of which further analysis is performed on the principal components that have the eigenvalues greater than 1 and individual parameter weights higher than 0.4 for which size of explained variance is calculated,
performing at least one clustering and at least one prediction method on all measured parameters in step a), that have previously been normalized to the variance of a sample for each parameter individually, wherein the at least one clustering method is selected in the group comprising (i) Hierarchical Clustering, (ii) k-Means, (iii) Louvain Clustering, and/or (iv) survival analysis for searching for patient clusters and predicting treatment outcomes, to identify clusters and patients,
preparation of a smart learner operable to identify linear and nonlinear characteristics of data, the smart learner configured for classifying an individual patient into an appropriate cluster identified in previous steps, using at least one of the following methods: Naive Bayes, Random Forest, Support Vector Machines, Deep Neural Network, kNN and Gradient Boosting,
assessment of the importance or contribution of each machine learning model input variable in the classification algorithm,
wherein the assessment is performed for each parameter or test comprising at least one of input variables in individual clusters, SHAP (Shapley additive explanations) values, Gini index, or information gain, and
wherein the components of the joint position sense test are characteristic for the grouping in three components:
repositioning error and velocity of the head and neck movements, which is global,
absolute, constant and variable error all measured in all three planes separately and combined as a three-dimensional vector, from the left and right rotation of the head,
repositioning error of the head and neck, which is absolute and constant error all measured in all three planes separately and combined as a three-dimensional vector, from flexion and extension of the head,
repositioning error of the head and neck, which is variable error measured in all three planes separately and combined as a three-dimensional vector, from the flexion and extension of the head,
or wherein the components of the Butterfly test are characteristic for the grouping in three components:
a first component comprising altered tracking of a low-difficulty trajectory, with significant low time spent on target, higher relative time of overshooting and undershooting, increased difference between the target and actual head position comprising increased amplitude accuracy, and increased smoothness of movement comprising a jerk index,
a second component comprising altered tracking of a medium-difficulty trajectory that is more difficult than the low-difficulty trajectory and a high-difficulty trajectory that is more difficult than the medium-difficulty trajectory, and
a third component comprising altered tracking of the target at medium-difficulty and high-difficulty trajectories with increased undershooting of the target and decreased time on the target.
The above steps recite a mathematical relationships, equations, and calculations, and therefore fall within the scope of an abstract idea in the form of mathematical concepts. Fundamentally the process is that of analyzing movement parameters from patient tests, selecting variables using principal component analysis and clustering the data, preparing a smart learner for classifying patients into clusters, and assessing the importance of input variables. Each of these steps constitutes either performing mathematical calculations on the patient data or determinations of mathematical relationships.
Examiner notes that the limitation reciting “with the help of at least one of the following methods: Naive Bayes, Random Forest, Support Vector Machines, Deep Neural Network, kNN and Gradient Boosting” does not specify any function actually being performed by the listed model types given that “with the help of” encompasses any use of such models outside the scope of the claim for any purpose during the “preparation,” and does not require that any of these models be trained for classifying patients into clusters. Likewise, the recitation of “assessment of the importance/contribution of each machine learning model input variable” does not require execution of a machine learning model, and only requires an evaluation of the input variable itself.
Claim 17 recites, in part, performing the steps of:
receiving information from recording and/or measurement units,
tracking, capturing and recording the measured components,
presenting the above-described tests that comprises tasks, where the patient tries to perform free movements, follow the verbal instructions, follow the movement or maintain a certain specific posture, or follow a target trajectory with above-described body parts, muscle contraction or eye movements,
capturing and recording the displayed patterns, positions, stimuli, which includes trajectory or position of the cursor movement,
calculating cursor movements and/or changes between the trajectory of the cursor and path/position of the target,
construct the tool according to claim 1 based on the above-described types of calculations for classification of patients into subgroups based on the results of above listed mobility, movement control and other tests with the method described above.
The above steps recite a process of managing personal behavior or relationships or interactions between people and therefore fall within the scope of an abstract idea in the form of a certain method of organizing human activity. Fundamentally the process is that of performing movement tests on a patient, tracking and collecting movement data from the tests, analyzing movement parameters, and determining how to classify patients based on the results of the tests. These steps could be performed by a healthcare provider or other individual as part of administering tests to a patient and determining how the patient should be classified based on the tests.
Claim 19 recites, in part, performing the steps of:
performing at least one of the tests selected in a group comprising (i) a Butterfly test, (ii) a joint position sense test, (iii) a mobility test of the spine and hips,
wherein at least three of the following parameters are analyzed in any combination:
average time spent on, behind or before the target during each trial,
expressed as percentage of each trial duration,
smoothness of movement index,
amplitude accuracy, calculated as average difference between the target and the body part’s actual position expressed in millimetres or degrees,
global, absolute, or relative repositioning error from movement directions for each joint measured in at least 2D space,
variable repositioning error from each joint movement direction measured in at least 2D space,
using the tool prepared with the method according to claim 1, which based on obtained results classifies the patient into a suitable cluster, and
defining the most suitable rehabilitation based on the determined cluster to which the patient belongs.
The above steps recite a process of managing personal behavior or relationships or interactions between people and therefore fall within the scope of an abstract idea in the form of a certain method of organizing human activity. Fundamentally the process is that of performing any of a series of movement or motion tests on a patient, analyzing parameters collected during the tests, classifying the patient into a cluster, and defining a most suitable rehabilitation based on the cluster. A human clinician could perform these functions as part of testing and diagnosing movement and neck disorders in a patient.
Step 2A(2)
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
Claim 17 recites the additional elements of a) a display connected to a processor recited as presenting the tests, b) a wearable mobile recording system arranged to measure head/eye, limb or body movements, which can be positioned on the head, neck other limbs, trunk or pelvis of the patient, wherein the recording system comprises at least one sensor and/or recording system, c) a processor comprising software operable to perform the subsequent functions such as receiving information from the recording or measurement units and capturing the test data, and d) the target trajectory and path/position of the target being computer generated.
Page 10 provides the only disclosure of a display, listing “a display (e.g. virtual reality system, stationary or mobile display)” as part of the system. No further disclosure of structure associated with the display is provided. The display is therefore construed as encompassing generic computer display devices.
Page 10 likewise lists examples of a mobile recording system, describing “a mobile recording system that measures head/eye, limb or body movements, which can be positioned on the head, neck other limbs, trunk or pelvis of the patient, wherein the recording system comprises at least one sensor and/or recording system (e.g. inertial units, virtual reality systems, video oculography/eye trackers, electrooculography, kinematic systems, electromyography, and others).” Given the breadth of devices disclosed, the mobile recording system comprising a sensor or recording system is construed as encompassing generic sensor devices.
Pages 10 and 11 describe the processor as “a processor provided with software” as well as “[t]he processor connected to a recording or acquisition unit, which comprises a database with information on patient classification.” No further disclosure of processor structure is provided. The processor comprising software is therefore construed as encompassing a generic computer processor.
With respect to the target trajectory and path/position of the target being computer generated, pages 10 and 11 provide the only disclosure of this term, and do so in the language used in the claim. The use of a computer to generate the target trajectory and path/position of the target is construed as use of a generic computer and/or software.
Each of the above elements only amounts to mere instructions to implement the abstract idea using computing elements as tools. For example, the display is only recited at a high level of generality as used to present the tests, while the wearable mobile recording system is also recited at a high level of generality as comprising at least one sensor and/or recording system and arranged to measure head/eye, limb or body movements. These elements are therefore not sufficient to integrate the abstract idea into a practical application.
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claim 1 recites the additional elements of performing a joint position sense test and at least one test selected in a group comprising a Butterfly test and a mobility test of the spine and hips.
However, these functions amount to mere data gathering activity for use in the abstract idea. MPEP 2106.05(g) lists “Performing clinical tests on individuals to obtain input for an equation” among the examples of mere data gathering activities found to amount to insignificant extra-solution activity.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claim 17 only recites the display, wearable mobile recording system comprising at least one sensor and/or recording system, processor comprising software, and computer generation of the target trajectory and path/position of the target as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f)
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claim 1 recites the additional elements of performing a joint position sense test and at least one test selected in a group comprising a Butterfly test and a mobility test of the spine and hips.
However, as set out above these functions amount to mere data gathering activity for use in the abstract idea. MPEP 2106.05(g) lists “Performing clinical tests on individuals to obtain input for an equation” among the examples of mere data gathering activities found to amount to insignificant extra-solution activity.
C. Well-Understood, Routine and Conventional Activities. MPEP 2106.05(d)
In addition to amounting to insignificant extra-solution activity, the above additional elements of claim 1 amount to well-understood routine and conventional activity given that they are recited at a high level of generality and as insignificant extra-solution activity, and are analogous to testing and measuring activity previously recognized as well-understood routine and conventional activity by the courts. See MPEP 2106.05(d)(II)(listing various methods of patient testing and data collection previously recognized as well-understood routine and conventional activity).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Depending Claims
Claim 2 recites the additional elements of wherein in step a) both the Butterfly test and tests of joint position sense are performed.
However, these functions amount to mere data gathering activity for use in the abstract idea. MPEP 2106.05(g) lists “Performing clinical tests on individuals to obtain input for an equation” among the examples of mere data gathering activities found to amount to insignificant extra-solution activity.
In addition to amounting to insignificant extra-solution activity, these additional elements amount to well-understood routine and conventional activity given that they are recited at a high level of generality and as insignificant extra-solution activity, and are analogous to testing and measuring activity previously recognized as well-understood routine and conventional activity by the courts. See MPEP 2106.05(d)(II)(listing various methods of patient testing and data collection previously recognized as well-understood routine and conventional activity).
These elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 3 recites wherein after step c) assessing the quality of the clusters with the Silhouette score is performed. These limitations fall within the scope of the abstract idea as set out above.
Claim 4 recites wherein for clustering the method is selected in the group consisting of: (i) Hierarchical Clustering, (ii) k-Means, (iii) Louvain Clustering, and/or (iv) survival analysis for searching for clusters and predicting treatment outcomes. These limitations fall within the scope of the abstract idea as set out above.
Claim 5 recites wherein in step d) preparation of a smart learner for classifying individual patients into appropriate clusters identified in previous steps is achieved with multiple methods selected in the group consisting of: Naive Bayes, Random Forest, Support Vector Machines, Deep Neural Network, kNN and Gradient Boosting. These limitations fall within the scope of the abstract idea as set out above.
Claim 6 recites wherein assessment of the importance/contribution of each machine learning model input variable in the classification algorithm is performed with SHAP values, wherein a SHAP value for each value for each patient and parameter is presented in order to assess how individual patients are classified into each previously defined cluster and how each value contributes to the classification accuracy. These limitations fall within the scope of the abstract idea as set out above.
Claim 7 recites wherein before step c) a correlation check is performed to check the correlation among the components. These limitations fall within the scope of the abstract idea as set out above.
Claim 12 recites wherein in step c) more than one clustering method is used. These limitations fall within the scope of the abstract idea as set out above.
Claim 13 recites wherein in step c) three different clustering methods are used, namely Hierarchical Clustering, k-Means and Louvain Clustering. These limitations fall within the scope of the abstract idea as set out above.
Claim 14 recites wherein the hierarchical clustering is performed by calculating Euclidean distances between individual values of parameters of analysed patients, followed by analysis of calculated distances using Ward linkages with maximal pruning depth of 10, wherein the k-Means method is performed with 10 re-runs and 300 maximum iterations, while Louvain clustering comprises data pre-processing using principal components. These limitations fall within the scope of the abstract idea as set out above.
Claim 15 recites wherein in step d) trained systems are combined using a stacking method, which generates a smart learner arranged to identify linear and nonlinear characteristics of data and their relations based on new patient assessments methods and classifies them into previously recognized clusters. These limitations fall within the scope of the abstract idea as set out above.
Claim 16 recites the additional elements of a computer program, a computer database and/or executable instructions saveable as at least one chosen from downloadable applications, downloadable programs, and programs on external units, and operable to execute the method according to claim 1 comprising steps b) through e).
Pages 9, 10, and 12 describe “a computer program, database and/or algorithm that can be stored in any appropriate manner such as downloadable applications, installed programs, programs or application installed on external units such as external disks, USB keys and the like” as used to store the described method. The above elements are construed as encompassing computer software as well as generic computer storage elements.
The above elements only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, each of the elements is only recited at a high level of generality as “operable to execute the method according to claim 1.” These elements are therefore not sufficient to integrate the abstract idea into a practical application.
Claim 18 recites the additional elements of the device for tracking movement comprising two sensors, wherein a first of the two sensors is configured for placement on the head, neck or limb of a patient, and a second of the two sensors is configured for placement on the patient's torso.
Page 10 lists examples of a mobile recording system, describing “a mobile recording system that measures head/eye, limb or body movements, which can be positioned on the head, neck other limbs, trunk or pelvis of the patient, wherein the recording system comprises at least one sensor and/or recording system (e.g. inertial units, virtual reality systems, video oculography/eye trackers, electrooculography, kinematic systems, electromyography, and others).” Given the breadth of devices disclosed, the mobile recording system comprising a sensor or recording system is construed as encompassing generic sensor devices.
The above elements only amounts to mere instructions to implement the abstract idea using computing elements as tools. Specifically, sensors are only recited at a high level of generality based on their location, with the first “configured for placement on the head, neck or limb of a patient” and the second “configured for placement on the patient's torso.” These elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea or to amount to significantly more than the abstract idea.
Claim 20 recites a computer program, a computer database and/or executable instructions saveable as at least one chosen from downloadable applications, downloadable programs, and programs on external units, and operable to execute the method according to claim 19.
Pages 9, 10, and 12 describe “a computer program, database and/or algorithm that can be stored in any appropriate manner such as downloadable applications, installed programs, programs or application installed on external units such as external disks, USB keys and the like” as used to store the described method. The above elements are construed as encompassing computer software as well as generic computer storage elements.
The above elements only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, each of the elements is only recited at a high level of generality as “operable to execute the method according to claim 19.” These elements are therefore not sufficient to integrate the abstract idea into a practical application.
Claims 1-7 and 12-20 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 and 12-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In order to satisfy the written description requirement, the specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See MPEP 2161.01(I). However, generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed, and even original claims may fail to satisfy the written description requirement when the invention is claimed and described in functional language but the specification does not sufficiently identify how the invention achieves the claimed function. See MPEP 2161.01(I)
Specifically with regard to computer-implemented functional claims, the specification must provide a disclosure of the computer and the algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention, including how to program the disclosed computer to perform the claimed function. MPEP 2161.01(I).
With regard to claim 1, the specification does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system capable of “predicting treatment outcomes,” as recited in line 39.
Page 11 of Applicant’s specification as originally filed states that “[o]ptionally, the computer program of the processor is arranged to identify different clusters and subgroups of patients, that have a specific functional meaning, with the help of described database as well as preparing prevention, treatment and rehabilitation interventions (e.g. advice, training and exercises, physiotherapy modalities, manual therapy, cognitive-behavioural therapy and others) and prediction of their outcomes.” However, no disclosure is provided of how any such prevention, treatment and rehabilitation interventions are prepared or how outcomes are determined.
Similarly, page 14 states that “[a] system of the linear and nonlinear sample characteristics and relations may serve to classify patients into clusters, consequently enabling preparations of adjusted and goal-oriented treatment plans based on the characteristics of each cluster.” However, no disclosure is provided of how classification into clusters accomplishes the function of predicting treatment outcomes.
These portions of the disclosure are not sufficient to provide written description support for the above limitation.
With further regard to claim 1, the specification does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system capable of “preparation of a smart learner operable to identify linear and nonlinear characteristics of data, the smart learner configured for classifying an individual patient into an appropriate cluster identified in previous steps” as recited in lines 40-42. Pages 17 and 18 provide a high-level description of searching for clusters of patients using three distinct clustering algorithms, and that the clustering algorithms were used to produce two, three, or four clusters. Page 18 then further states that “the classification accuracy was analysed in order to provide information on how individual patients with neck pain can be classified into previously identified clusters” and that “[p]erformance of six datamining approaches to classify individual patients into specific clusters were compared.” However, this description does not actually disclose how a “smart learner” for classifying patients into clusters was actually created beyond a general statement that six different machine learning algorithms “were compared.” While page 18 additionally states that “stacking of all applied machine learning approaches was performed in order to study how the classification accuracy could improve if individual datamining approaches are combined,” this does not provide support for how any such “stacking” was performed. Merely describing a type of data to be classified and then listing multiple disparate potential algorithms applicable to performing the classification is not sufficient to provide written description support.
Claims 2-7 and 12-20 inherit the deficiencies of claim 1 through dependency and are likewise rejected.
With regard to claim 4, the specification does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system capable of predicting treatment outcomes.
As set out with respect to claim 1, page 11 of Applicant’s specification as originally filed states that “[o]ptionally, the computer program of the processor is arranged to identify different clusters and subgroups of patients, that have a specific functional meaning, with the help of described database as well as preparing prevention, treatment and rehabilitation interventions (e.g. advice, training and exercises, physiotherapy modalities, manual therapy, cognitive-behavioural therapy and others) and prediction of their outcomes.” However, no disclosure is provided of how any such prevention, treatment and rehabilitation interventions are prepared or how outcomes are determined.
Similarly, page 14 states that “[a] system of the linear and nonlinear sample characteristics and relations may serve to classify patients into clusters, consequently enabling preparations of adjusted and goal-oriented treatment plans based on the characteristics of each cluster.” However, no disclosure is provided of how classification into clusters accomplishes the function of predicting treatment outcomes.
These portions of the disclosure are not sufficient to provide written description support for the above limitation.
With regard to claim 5, the specification does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system capable of preparing a smart learner for classifying individual patients into appropriate clusters identified in previous steps with multiple methods selected in the group consisting of: Naive Bayes, Random Forest, Support Vector Machines, Deep Neural Network, kNN and Gradient Boosting.
Pages 7 and 13 each reflect the language of the claim, stating that the smart learner can be prepared “with the help of at least one, optionally multiple methods: Naive Bayes, Random Forest, Support Vector Machines, Deep Neural Network, kNN and Gradient Boosting…”. These portions do not provide a description of how the smart learner is prepared using multiple of these algorithms however.
Page 18 further states that “[p]erformance of six datamining approaches to classify individual patients into specific clusters were compared.” However, this description does not actually disclose how a “smart learner” for classifying patients into clusters was actually created beyond a general statement that six different machine learning algorithms “were compared.” While page 18 additionally states that “stacking of all applied machine learning approaches was performed in order to study how the classification accuracy could improve if individual datamining approaches are combined,” this does not provide support for how any such “stacking” was performed. Merely describing a type of data to be classified and then listing multiple disparate potential algorithms applicable to performing the classification is not sufficient to provide written description support.
With regard to claim 15, the specification does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system capable of “wherein in step d) trained systems are combined using a stacking method, which generates a smart learner arranged to identify linear and nonlinear characteristics of data and their relations based on new patient assessments methods and classifies them into previously recognized clusters.”
As set out above with respect to claims 1 and 8, pages 17 and 18 provide a high-level description of searching for clusters of patients having idiopathic neck pain using three distinct clustering algorithms, and that the clustering algorithms were used to produce two, three, or four clusters.
Page 18 then additionally states that “stacking of all applied machine learning approaches was performed in order to study how the classification accuracy could improve if individual datamining approaches are combined.” However, no disclosure is provided for how any such “stacking” was actually performed or how the smart learner capable of identifying linear and nonlinear characteristics of data and their relations based on new patient assessments methods and classifying them into previously recognized clusters is actually generated. Merely describing types of data to be classified and then listing multiple disparate potential algorithms, or providing a broad statement that algorithms are combined, is not sufficient to provide written description support.
With regard to claim 17, the specification does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system capable of “construct[ing] the tool according to claim 1 based on the above-described types of calculations for classification of patients into subgroups based on the results of above listed mobility, movement control and other tests with the method described above.”
Pages 17 and 18 provide a high-level description of searching for clusters of patients using three distinct clustering algorithms, and that the clustering algorithms were used to produce two, three, or four clusters. Page 18 then further states that “the classification accuracy was analysed in order to provide information on how individual patients with neck pain can be classified into previously identified clusters” and that “[p]erformance of six datamining approaches to classify individual patients into specific clusters were compared.” However, this description does not actually disclose how a “smart learner” for classifying patients into clusters was actually created beyond a general statement that six different machine learning algorithms “were compared.” While page 18 additionally states that “stacking of all applied machine learning approaches was performed in order to study how the classification accuracy could improve if individual datamining approaches are combined,” this does not provide support for how any such “stacking” was performed. Merely describing a type of data to be classified and then listing multiple disparate potential algorithms applicable to performing the classification is not sufficient to provide written description support.
Claim 18 inherits the deficiencies of claim 17 through dependency and is likewise rejected.
With regard to claim 19, the specification does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system capable of “defining the most suitable rehabilitation based on the determined cluster to which the patient belongs.”
Pages 7 and 8 describe clustering of spinal pain patients and that “[i]n addition, these tests and parameters are further used in the predictions of the rehabilitation outcomes for each group individually with methods such as survival analysis or others appropriate methods known in the field.” Similarly, page 11 of Applicant’s specification as originally filed states that “[o]ptionally, the computer program of the processor is arranged to identify different clusters and subgroups of patients, that have a specific functional meaning, with the help of described database as well as preparing prevention, treatment and rehabilitation interventions (e.g. advice, training and exercises, physiotherapy modalities, manual therapy, cognitive-behavioural therapy and others) and prediction of their outcomes.” However, no disclosure is provided of how any such prevention, treatment and rehabilitation interventions are prepared or how outcomes are determined. Simply stating that interventions are prepared and outcomes are predicted based on determined clusters is not sufficient to provide written description support for the function of “defining the most suitable rehabilitation based on the determined cluster to which the patient belongs.”
With further regard to claim 19, the specification does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system capable of classifying the patient into a suitable cluster as recited in lines 30-31. The claim states that the classification is performed “using the tool prepared with the method according to claim 1” and “based on obtained results.” However, the disclosure does not provide written description support for preparation of such a tool.
Pages 17 and 18 provide a high-level description of searching for clusters of patients using three distinct clustering algorithms, and that the clustering algorithms were used to produce two, three, or four clusters. Page 18 then further states that “the classification accuracy was analysed in order to provide information on how individual patients with neck pain can be classified into previously identified clusters” and that “[p]erformance of six datamining approaches to classify individual patients into specific clusters were compared.” However, this description does not actually disclose how a “smart learner” for classifying patients into clusters was actually created beyond a general statement that six different machine learning algorithms “were compared.” While page 18 additionally states that “stacking of all applied machine learning approaches was performed in order to study how the classification accuracy could improve if individual datamining approaches are combined,” this does not provide support for how any such “stacking” was performed. Merely describing a type of data to be classified and then listing multiple disparate potential algorithms applicable to performing the classification is not sufficient to provide written description support.
Claim 20 inherits the deficiencies of claim 19 through dependency and is likewise rejected.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 and 12-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is indefinite because Examiner is unable to determine the metes and bounds of the claim based on the recitation of “for which size of explained variance is calculated” in lines 35-36. Specifically, it is not clear whether this limitation is positively reciting a step of calculating a size of explained variance for individual parameter weights higher than 0.4, whether it recites a further subset of individual parameter weights higher than 0.4, or whether this limitation is merely descriptive.
Claim 1 is indefinite because Examiner is unable to determine the metes and bounds of the claim based on the recitation of “preparation of a smart learner operable to identify linear and nonlinear characteristics of data, the smart learner configured for classifying individual patients” in line 43. It is not clear what the scope of “preparation of” a smart learner is in the context of the claim. Given that the broadest reasonable interpretation of “preparation” encompasses acts prior to actually creating or acquiring a thing, and therefore could in theory encompass any act in furtherance of a “smart learner,” is not clear what the metes and bounds of such preparation would be. For example, would preparation include installing software required for subsequent work on a smart learner? Does the scope require all acts leading up to a smart learner? Would the scope require some form of active steps to train or create a smart learner or could it exclude such steps? Given the importance of the above limitation to the subject matter of the claim, Examiner requests that Applicant clarify the intended scope of the claimed subject matter.
Claim 1 is indefinite based on the recitation of the limitation "an appropriate cluster identified in previous steps" in lines 43-44. Initially, there is insufficient antecedent basis for this limitation in the claim because the claim does not previously recite identifying a cluster or appropriate cluster of patients, or the identification of any particular clusters. While the claim broadly recites “performing at least one clustering and at least one prediction method on all measured parameters in step a),” this does not recite or characterize any clusters in a manner which would provide antecedent basis. Furthermore, the term “appropriate” within the above limitation is indefinite because it is not clear what constitutes an “appropriate” cluster, especially given the antecedent basis ambiguities explained above.
Claim 1 recites the limitation "the classification algorithm" in line 48. There is insufficient antecedent basis for this limitation in the claim because there is no prior recitation of a classification algorithm.
Claim 1 is indefinite because Examiner is unable to determine the metes and bounds of the claim based on the recitation of “wherein the components of the joint position sense test are characteristic for the grouping in three components.” Initially, the term “the components of the joint position sense test” lacks antecedent basis within the claim. There is no prior recitation of components of the joint position sense test and it is not clear from the context of the claim whether this term is intended to refer to the previously performed principal component analysis.
Furthermore, Examiner is unable to determine the intended meaning or scope of the phrase “characteristic for the grouping in three components.” The portion reciting “the grouping” lacks antecedent basis because there is no prior recitation of any grouping, the meaning of “characteristic for” is ambiguous in scope and how it modifies the subsequent limitations, and it is not clear whether “in three components” is intended to refer back to “the primary components” or stands on its own. This limitation is entirely ambiguous in scope and how it is intended to characterize the subsequently recited list of elements.
Claim 1 is indefinite because Examiner is unable to determine the metes and bounds of the claim based on the recitation of “repositioning error and velocity of the head and neck movements, which is global, absolute, constant and variable error all measured in all three planes separately and combined as a three-dimensional vector.” Specifically, this limitation initially recites “repositioning error and velocity” and then defines both error and velocity as “global, absolute, constant and variable error.” It is not clear how this is intended to further limit the velocity element of the claim.
Claim 1 is indefinite because Examiner is unable to determine the metes and bounds of the claim based on the recitation of “wherein the primary components of the Butterfly test are characteristic for the grouping in three components.” Initially, the term “the components of the Butterfly test” lacks antecedent basis within the claim. There is no prior recitation of components of the joint position sense test and it is not clear from the context of the claim whether this term is intended to refer to the previously performed principal component analysis.
Furthermore, Examiner is unable to determine the intended meaning or scope of the phrase “characteristic for the grouping in three components.” The portion reciting “the grouping” lacks antecedent basis because there is no prior recitation of any grouping, the meaning of “characteristic for” is ambiguous in scope and how it modifies the subsequent limitations, and it is not clear whether “in three components” is intended to refer back to “the primary components” or stands on its own. This limitation is entirely ambiguous in scope and how it is intended to characterize the subsequently recited list of elements.
Claim 1 is indefinite because Examiner is unable to determine the metes and bounds of the claim based on the recitation of “a first component comprising altered tracking of a low-difficulty trajectory, with significant low time spent on target, higher relative time of overshooting and undershooting, increased difference between the target and actual head position comprising increased amplitude accuracy, and increased smoothness of movement comprising a jerk index, a second component comprising altered tracking of a medium-difficulty trajectory that is more difficult than the low-difficulty trajectory and a high-difficulty trajectory that is more difficult than the medium-difficulty trajectory, and a third component comprising altered tracking of the target at medium-difficulty and high-difficulty trajectories with increased undershooting of the target and decreased time on the target.”
Initially, the recitation of “significant low time spent on target” is ambiguous because the scope and meaning of “significant” is unclear. It is also unclear what is being described by “with significant low time spent on target, higher relative time of overshooting and undershooting, increased difference between the target and actual head position comprising increased amplitude accuracy, and increased smoothness of movement comprising a jerk index” and how these elements modify the first component comprising altered tracking of a low-difficulty trajectory.
Each of “low-difficulty trajectory,” “medium-difficulty trajectory,” and “high-difficulty trajectory” is also ambiguous because neither the claims nor the disclosure provide sufficient information to determine what “difficulty” and “more difficult” mean in this context or how one would characterize one trajectory as more difficult than another.
The recitation of “higher relative time of overshooting and undershooting” is ambiguous because it is not clear from the claims or disclosure what is meant by “higher” or “relative time” in this context. No baseline is provided, so it is not clear what would be “higher” relative time, or what “relative time” is relative to.
The recitation of “increased difference between the target and actual head position comprising increased amplitude accuracy” is ambiguous based on the term “increased.” Given that no baseline or point of comparison is provided, it is not clear what the increased difference between the target and actual head position comprising increased amplitude accuracy is actually “increased” from. The recitation of “increased smoothness of movement comprising a jerk index” is ambiguous on the same basis. The scope and meaning of “increased undershooting of the target” and “decreased time on the target at medium and difficult level” is ambiguous because it is not clear how one would gauge “increased difference” or “decreased time” or to otherwise determine the scope of these terms.
Furthermore, the specific scope and meaning of “altered tracking of a low-difficulty trajectory” is ambiguous because it is not clear what constitutes “altered tracking” in this context.
Additionally, the scope and meaning of each of “increased difference between the target and actual head position” and “increased smoothness of movement” is ambiguous because there is no prior recitation of a difference or smoothness from which to gauge “increased difference” or “increased smoothness” or to otherwise determine the scope of these terms.
Claims 2-7 and 12-20 inherit the deficiencies of claim 1 through dependency and are likewise rejected.
Claim 3 recites the limitation "the Silhouette score" in line 2. There is insufficient antecedent basis for this limitation in the claim because there is no prior recitation of a Silhouette score. While a Silhouette score references a particular statistical calculation, the wording of the claim makes it unclear whether it is intended to recite calculation of a Silhouette score as part of the limitation or if it was intended to reference a previous calculation of the score, and would therefore lack antecedent basis.
Claim 4 is indefinite based on the recitation of “selected in the group consisting of” in line 2. This terminology does not set out a proper Markush group and it is not clear what is intended to be recited by “selected in the group.”
Claim 5 is indefinite based on the recitation of the limitation "appropriate clusters identified in previous steps" in line 3. Initially, there is insufficient antecedent basis for this limitation in the claim because the claim does not previously recite identifying any clusters of patients, or the identification of any particular clusters. While the claim broadly recites “performing at least one clustering and at least one prediction method on all measured parameters in step a),” this does not recite or characterize any clusters in a manner which would provide antecedent basis. Furthermore, the term “appropriate” within the above limitation is indefinite because it is not clear what constitutes an “appropriate” cluster, especially given the antecedent basis ambiguities explained above.
Claim 17 recites “the above listed recording and/or measurement units” in lines 11-12. There is insufficient antecedent basis for this limitation in the claim because there is no prior recitation of recording and/or measurement units. While the claim previously recites a wearable mobile recording system comprising at least one sensor or recording system, it is not clear which of these elements is being referenced by the recited “units.”
Claim 17 recites “the measured components” in line 13. There is insufficient antecedent basis for this limitation in the claim because there is no prior recitation of measured components.
Claim 17 recites “the above-described tests” in line 14. There is insufficient antecedent basis for this limitation in the claim because there are no previously recited tests. While the claim previously recites recording body movements, these do not constitute tests.
Claim 17 recites “the verbal instructions” in lines 17-18. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites “the movement” in line 18. There is insufficient antecedent basis for this limitation in the claim because the claim previously recites the recording system as arranged to measure head/eye, limb or body movements it is not clear what specific movement is being referenced.
Claim 17 recites “the displayed patterns, positions, stimuli” in line 21. There is insufficient antecedent basis for this limitation in the claim because there are no previously recited displayed patterns, positions, or stimuli. Additionally, the above grouping does not specify “and” or “or,” and it is unclear whether all of the elements are required or are only required in the alternative.
Claim 17 recites “the above-described types of calculations” in lines 25-26. There is insufficient antecedent basis for this limitation in the claim because there are no previously recited types of calculations.
Claim 17 recites “the results of above listed mobility, movement control and other tests” in lines 26-27. There is insufficient antecedent basis for this limitation in the claim because there are no previously recited results, or of listed mobility or movement control tests. Additionally, the term “and other tests” renders the scope of the claim indefinite because it is not clear what other tests are being referenced.
Claim 17 is indefinite because Examiner is unable to determine the metes and bounds of the claim based on the recitation of “construct the tool according to claim 1 based on the above-described types of calculations for classification of patients into subgroups based on the results of above listed mobility, movement control and other tests with the method described above.” Examiner notes that the preamble of claim 17 recites “A system for classification of patients with spine-related disorders, patients with traumatic brain injury, and patients with vestibular or neurological disorders” while claim 1 is recited as “a method for preparing a tool for classifying patients with idiopathic neck pain…” (emphasis added). Given that the tool of claim 1 is described as for classifying patients with idiopathic neck pain, it is unclear how it is then used for classification of patients with spine-related disorders, patients with traumatic brain injury, and patients with vestibular or neurological disorders.”
Claim 18 inherits the deficiencies of claim 17 through dependency and is likewise rejected.
Claim 19 recites the limitation "the target" in line 22. There is insufficient antecedent basis for this limitation in the claim because there is no prior recitation of a “target”.
Claim 19 recites the limitation "each trial" in line 22. There is insufficient antecedent basis for this limitation in the claim because there is no prior recitation of any trials, and it is not clear what trials are being referenced.
Claim 19 recites the limitation "the tool prepared with the method according to claim 1" in line 31. There is insufficient antecedent basis for this limitation in the claim because there is no prior recitation of a prepared tool in either claim 1 or claim 19. While the preamble of claim 1 contains “A method for preparing a tool…,” the claim does subsequently recite any preparation of a tool. It is therefore not clear what the “tool” is that is being referenced in claim 19.
Claim 19 is indefinite based on the recitation of the limitation "a suitable cluster" in line 32. The term “suitable” is indefinite because it is not clear what constitutes a “suitable” cluster given the context of the term within the claim.
Claim 19 recites the limitation "the patient" in line 32. There is insufficient antecedent basis for this limitation in the claim. The only previous recitation of patients is in the preamble of the claim, which states that the claim is directed to “a method for classification of patients…”. However, it is not clear what specific patient is being referenced in line 32.
Claim 20 inherits the deficiencies of claim 19 through dependency and is likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
McNair et al (US10,716,517);
Sarig-Bahat (US Patent Application Publication 2011/0230792).
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 WILLIAM G LULTSCHIK whose telephone number is (571)272-3780. The examiner can normally be reached 9am - 5pm.
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/Gregory Lultschik/Examiner, Art Unit 3682