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 . 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 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.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 1-14 are directed to a “system,” which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claims 15-20 are directed to a “method,” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 1 and 15 recite (“set forth” or “describe”) the abstract idea of a mental process and a mathematical concept, substantially as follows:
generating a gait feature using the received gait or balance information; and determining a cardiac or pulmonary event risk of the patient based at least in part on the generated gait feature.
The generating and determining steps can be practically performed in the human mind, with the aid of a pen and paper, but for performance on a generic computer, in a computer environment, or merely using the computer as a tool to perform the steps. If a person were to see a printout of e.g. the received gait or balance information, they would be able to generate a gait feature and determine an event risk based on the feature by e.g. analyzing the morphology of the feature with respect to a threshold. There is nothing to suggest an undue level of complexity in the steps. Therefore, a person would be able to perform the generating and determining mentally or with pen and paper.
The steps also involve the mathematical concepts of feature extraction and risk calculation. These steps correspond to “[w]ords used in a claim operating on data to solve a problem [that] can serve the same purpose as a formula.” See MPEP 2106.04(a)(2)(I).
Prong Two: Claims 1 and 15 do not include additional elements that integrate the mental process or mathematical concept into a practical application. Therefore, the claims are “directed to” the mental process and mathematical concept. The additional elements merely:
recite the words “apply it” (or an equivalent) with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g. a gait analyzer circuit and a physiologic event detector circuit), and
add insignificant extra-solution activity (the pre-solution activity of: receiving gait or balance information; and the post-solution activity of: outputting the event risk to a user or process).
As a whole, the additional elements merely serve to gather and feed information to the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the event risk is not outputted in any way such that a diagnostic benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1 and 15 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
Dependent Claims
The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
further describe the abstract idea (e.g. details of the gait feature (claims 4-6 and 16-18), pre-processing and analysis (claims 8-10), determining a histogram or statistical distribution (claims 12 and 19), trend analysis and categorization (claims 13, 14, and 20), etc.), and
further describe the pre-solution activity (or the structure used for such activity) (e.g. the type of data-gathering and sensor (claims 2, 3, 7, and 8), filtering (claim 11), etc.).
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way (e.g. nothing is done with the outputted event risk). They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves another technology or technical field, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-6, and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 2016/0147959 (“Mariottini”) and US Patent Application Publication 2017/0293729 (“Movva”)
Regarding claim 1, Mariottini teaches [a] system for assessing a cardiac or pulmonary event risk of a patient (¶ 0023), the system comprising: a gait analyzer circuit configured to receive gait or balance information of the patient during locomotion, and to generate a gait feature from the received gait or balance information (¶ 0022, extracting gait parameters); and a physiologic event detector circuit configured to determine a cardiac or pulmonary … risk of the patient based at least in part on the generated gait feature, and to output the cardiac or pulmonary … risk to a user or a process executable by the system (¶¶s 0022 and 0023, using algorithms to predict a risk or likelihood of certain medical events or conditions, which include e.g. cardiovascular disease, and then outputting alerts based thereon).
Mariottini does not appear to explicitly teach determination of a cardiac or pulmonary event risk.
Movva teaches the relationship of gait to degradation of e.g. CHF, COPD, etc. (¶ 0060).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine cardiac or pulmonary degradation likelihood/risk based on gait, as in Movva, for the purpose of helping the user to catch degradation early, thereby reducing hospitalizations (Movva: ¶ 0060, Abstract).
Regarding claim 3, Mariottini-Movva teaches all the features with respect to claim 1, as outlined above. Mariottini-Movva further teaches wherein the gait analyzer circuit is communicatively coupled to a wearable sensor or an apparel-mounted sensor associated with the patient, and configured to receive therefrom the gait or balance information (Movva: ¶¶s 0012, 0050, 0059, 0060, etc., wearable sensors including e.g. accelerometers to detect walking, running, falling, etc.).
Regarding claim 4, Mariottini-Movva teaches all the features with respect to claim 1, as outlined above. Mariottini-Movva further teaches wherein the generated gait feature includes a gait speed or a change in gait speed over time (Mariottini: ¶ 0035, gait speed).
Regarding claims 5 and 6, Mariottini-Movva teaches all the features with respect to claim 1, as outlined above. Mariottini-Movva further teaches wherein the generated gait feature includes a gait pattern or a change in gait pattern over time (Mariottini: ¶ 0022, walking pattern – also see ¶ 0035, gait freezing, which is a change in gait pattern), wherein the gait pattern includes a measure of a degree of intermittence or interruptions, or a degree of coordination among extremities, during locomotion (Mariottini: ¶¶s 0022 and 0035, pattern recognition related to gait freezing).
Regarding claim 12, Mariottini-Movva teaches all the features with respect to claim 1, as outlined above. Mariottini-Movva further teaches wherein the physiologic event detector circuit is configured to determine a histogram or a statistical distribution of the gait feature, and based thereon to determine the cardiac or pulmonary event risk (Mariottini: ¶ 0076, different deviations from normal gait indicate different levels of risk/likelihood).
Regarding claims 13 and 14, Mariottini-Movva teaches all the features with respect to claim 1, as outlined above. Mariottini-Movva further teaches wherein the gait analyzer circuit is further configured to generate a trend of the gait feature over multiple time periods, and to recognize patient gait as one of a plurality of pre-defined gait categories based on the generated trend of the gait feature (Mariottini: ¶ 0035, extracting multiple gait parameters, such as gait speed and gait freezing, and using them to generate an output; ¶ 0042, associating changes in the parameters (i.e., trends) with different outputs based on different particular kinds of gait abnormality that take into account risk/severity), wherein the physiologic event detector circuit is configured to determine the cardiac or pulmonary event risk based at least in part on the recognized gait (Mariottini: ¶ 0042, e.g. a trend in one particular gait parameter being tied to risk/severity of osteoarthritis, etc. It would have been obvious to apply this classification to cardiac and/or pulmonary metrics as well, as noted above with respect to Movva), wherein to recognize the patient gait, the gait analyzer circuit is further configured to generate a second gait feature, different from the first gait feature, from the received gait or balance information, generate a composite trend using a combination of the first and the second gait features over multiple time periods, and to recognize the patient gait based on the generated composite trend (Mariottini: ¶ 0035, using multiple parameters to generate an output; ¶ 0072, using thresholds based on multiple parameters).
Regarding claim 15, Mariottini teaches [a] method of assessing a cardiac or pulmonary event risk of a patient (¶ 0023) using a medical system, the method comprising: receiving gait or balance information of the patient during locomotion; generating, via a gait analyzer circuit, a gait feature using the received gait or balance information (¶ 0022, extracting gait parameters); determining, via a physiologic event detector circuit, a cardiac or pulmonary … risk of the patient based at least in part on the generated gait feature; and outputting the cardiac or pulmonary … risk to a user or a process (¶¶s 0022 and 0023, using algorithms to predict a risk or likelihood of certain medical events or conditions, which include e.g. cardiovascular disease, and then outputting alerts based thereon).
Mariottini does not appear to explicitly teach determination of a cardiac or pulmonary event risk.
Movva teaches the relationship of gait to degradation of e.g. CHF, COPD, etc. (¶ 0060).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine cardiac or pulmonary degradation likelihood/risk based on gait, as in Movva, for the purpose of helping the user to catch degradation early, thereby reducing hospitalizations (Movva: ¶ 0060, Abstract).
Regarding claim 16, Mariottini-Movva teaches all the features with respect to claim 15, as outlined above. Mariottini-Movva further teaches wherein the generated gait feature includes a gait speed or a change in gait speed over time (Mariottini: ¶ 0035, gait speed).
Regarding claims 17 and 18, Mariottini-Movva teaches all the features with respect to claim 15, as outlined above. Mariottini-Movva further teaches wherein the generated gait feature includes a gait pattern or a change in gait pattern over time (Mariottini: ¶ 0022, walking pattern – also see ¶ 0035, gait freezing, which is a change in gait pattern), wherein the gait pattern includes a measure of a degree of intermittence or interruptions, or a degree of coordination among extremities, during locomotion (Mariottini: ¶¶s 0022 and 0035, pattern recognition related to gait freezing).
Regarding claim 19, Mariottini-Movva teaches all the features with respect to claim 15, as outlined above. Mariottini-Movva further teaches via the physiologic event detector circuit, determining a histogram or a statistical distribution of the gait feature, and based thereon determining the cardiac or pulmonary event risk (Mariottini: ¶ 0076, different deviations from normal gait indicate different levels of risk/likelihood).
Regarding claim 20, Mariottini-Movva teaches all the features with respect to claim 15, as outlined above. Mariottini-Movva further teaches, via the gait analyzer circuit, generating a trend of the gait feature over multiple time periods, and recognizing patient gait as one of a plurality of pre-defined gait categories based on the generated trend of the gait feature (Mariottini: ¶ 0035, extracting multiple gait parameters, such as gait speed and gait freezing, and using them to generate an output; ¶ 0042, associating changes in the parameters (i.e., trends) with different outputs based on different particular kinds of gait abnormality that take into account risk/severity), wherein determining the cardiac or pulmonary event risk is based at least in part on the recognized gait (Mariottini: ¶ 0042, e.g. a trend in one particular gait parameter being tied to risk/severity of osteoarthritis, etc. It would have been obvious to apply this classification to cardiac and/or pulmonary metrics as well, as noted above with respect to Movva).
Claims 2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Mariottini-Movva in view of US Patent Application Publication 2007/0250134 (“Miesel”).
Regarding claim 2, Mariottini-Movva teaches all the features with respect to claim 1, as outlined above. Mariottini-Movva does not appear to explicitly teach wherein the gait analyzer circuit is communicatively coupled to an implantable sensor associated with the patient, and configured to receive therefrom the gait or balance information.
Miesel teaches collecting gait information from an implanted sensor (¶¶s 0019, 0054, 0056, 0068, etc.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use an implantable sensor to provide the gait information of the combination, as in Miesel, for the purpose of being able to provide concurrent therapy based on the information (Miesel: Abstract).
Regarding claim 7, Mariottini-Movva teaches all the features with respect to claim 5, as outlined above. Mariottini-Movva further teaches wherein the received gait or balance information includes multi-dimensional … data …, wherein the gait analyzer circuit is configured to generate a three-dimensional motion contour using the collected multi-dimensional … data, and to determine the gait pattern or the change in gait pattern using a feature derived from the three-dimensional motion contour (Mariottini: ¶¶s 0029 and 0065, Fig. 6C, describing a 3D motion contour).
Mariottini-Movva does not appear to explicitly teach the data being multi-dimensional acceleration data collected via one or more accelerometers associated with the patient (although Movva teaches use of gyros, accelerometers, magnetometers, and inclinometers, they are not explicitly multi-dimensional).
Miesel teaches using e.g. 3-axis accelerometers to detect a patient’s gait and other movements (¶ 0140).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a multi-dimensional accelerometer in the combination, as in Miesel, for the purpose of gathering more comprehensive gait data (Miesel: ¶ 0140), and as the simple substitution of one know element for another with predictable results (gait monitoring).
Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Mariottini-Movva in view of US Patent Application Publication 2013/0090881 (“Janardhanan”).
Regarding claims 8-11, Mariottini-Movva teaches all the features with respect to claim 1, as outlined above. Mariottini-Movva further teaches wherein the received gait or balance information includes a body acceleration signal during locomotion (Movva: ¶¶s 0012, 0050, 0059, 0060, etc., wearable sensors including e.g. accelerometers to detect walking, running, falling, etc.), but does not appear to explicitly teach wherein the gait analyzer circuit is configured to pre-process the body acceleration signal, and to generate the gait feature from the pre-processed acceleration signal, wherein the gait analyzer circuit is configured to generate the gait feature based on a correlation analysis of the pre-processed acceleration signal, wherein the gait analyzer circuit is configured to generate the gait feature based on a frequency analysis of the pre-processed acceleration signal, wherein the gait analyzer circuit is configured to pre-process the body acceleration signal using a filter circuit, and to adaptively adjust a filter parameter of the filter circuit based on the generated gait feature.
Janardhanan teaches using an accelerometer for gait monitoring (Title, ¶ 0051, etc.). The accelerometer signal is pre-processed using an adaptive filter circuit (¶ 0070, low pass filtering based on a frequency range adapted to e.g. walking rate). Gait features are generated based on correlation and frequency analysis (e.g. Figs. 18A and 18B show correlation and frequency analysis that leads to a step length determination).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to pre-process (e.g. adaptively filter) the accelerometer data of the combination, and combine that with correlation and frequency analysis, as in Janardhanan, for the purpose of extracting gait features in an accurate manner (Janardhanan: ¶¶s 0010, 0011, 0014, 0051, 0066, etc.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREY SHOSTAK whose telephone number is (408) 918-7617. The examiner can normally be reached Monday-Friday, 7am-3pm PT.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson, can be reached at telephone number (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREY SHOSTAK/Primary Examiner, Art Unit 3791
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