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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “gaming device for playing computer games” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For examination purposes, the “gaming device for playing computer games” will be interpreted as any personal computer (PC), desktop computer, laptop computer, tablet computer, smart phone, or smart watch.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed inventions are directed towards an abstract idea without significantly more than an abstract idea.
Step 1 – Is the claim to a statutory category of intention?
Claims 1-6 recite a method. Claims 7 and 9 recite a machine (i.e., a device and a system). Claim 8 recites, “[a] computer program comprising instructions…”. A computer program is an abstract idea and is not directed to a statutory category of invention. Therefore, claim 8 fails step 1 and is rejected under 35 USC 101. Claims 1-7 and 9 are directed to a statutory category of invention.
Step 2A, prong 1 – Does the claim recite a judicial exception?
Independent claim 1 recites, “analyzing the sensor data with an algorithm…”. An algorithm is an abstract idea mathematical concept in that the sensor data is processed (analyzed) using an algorithm, which can be interpreted as a mathematical calculation. Reciting MPEP 2106.04(a)(2)(I): The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. An algorithm, as generally recited, would be considered to be a mathematical concept.
Further, claim 1 recites that the algorithm is adapted “to detect whether or not a person suffers from peripheral neuropathy based on the sensor data.” As recited, “detect[ing] whether or not the person suffers from peripheral neuropathy” is an abstract idea mental process in that one of ordinary skill in the art, such as a physician, could observe sensor data to make a determination as to whether an individual has neuropathy or not. As generally recite, the limitation of detecting, based on sensor data, could be performed by the judgements and observations of a physician. Reciting MPEP 2106.04(a)(2)(III): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” As recited, a physician could make the judgement and opinion of whether an individual is suffering from peripheral neuropathy in their mind. Therefore, the claimed subject matter is an abstract idea mental process.
Step 2A, prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
In addition to the abstract ideas previously detailed in Step 2A, prong 1, claim 1 recites the additional limitations: “…loading sensor data from foot bottom pressure sensors that were pressed between the feet of the person and the underground while the person was playing a predetermined computer game while controlling the computer game by exerting pressure on the pressure sensors via the person's feet…”. Loading sensor data from pressure sensors is merely insignificant, extra-solution activity data gathering (see MPEP 2106.06(g)). Further, the claim further limits how the data is obtained, but only relates to the field of use. Therefore, the additional limitations of claim 1 do not integrate the abstract idea into a practical application.
Step 2B – Do the additional elements add significantly more to the judicial exception?
In addition to the abstract ideas previously detailed in Step 2A, prong 1, claim 1 recites the additional limitations: “…loading sensor data from foot bottom pressure sensors that were pressed between the feet of the person and the underground while the person was playing a predetermined computer game while controlling the computer game by exerting pressure on the pressure sensors via the person's feet…”. Loading sensor data from pressure sensors is merely insignificant, extra-solution activity data gathering (see MPEP 2106.06(g)). Further, the claim further limits how the data is obtained, but only relates to the field of use. Therefore, the additional limitations of claim 1 do add significantly more to the judicial exception.
Dependent Claims
Claim 2 further defines insignificant, extra-solution activity data gathering, and an abstract idea mathematical concept (“analyzed by the algorithm”).
Claim 3 and 4 further defines insignificant, extra-solution activity data gathering.
Claim 5 further defines the calculations made by the mathematical concept abstract idea.
Claim 6 further defines the abstract idea as an AI model.
Claim 7 recites generic computer structure.
Claim 8 recites subject matter not directed towards a statutory category of invention, as well as generic computer structure.
Claim 9 recites generic computer structure, as well as further defining the types of sensors for insignificant, extra-solution activity data gathering.
In summary, dependent claims 2-9 do not include any additional limitations that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
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 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation, "the underground" in line 5. There is insufficient antecedent basis for this limitation in the claim. Additionally, “the underground” is not clear. The Examiner is interpreting “the underground” to mean the ground beneath the foot and the sensor.
Claims 2-9 are rejected due to their dependency from claim 1.
Claim 2 recites, “game data” in line 2. It is unclear if the game data is the same as the sensor data or if it is data obtained from some other sensor. For examination purposes, it is interpreted that the game data can come from the sensor data, and is related to the performance during the game.
Claim 4 is rejected due to its dependency from claim 2.
Claim 3 recites the limitation "the amount" in 2, “the timing” in lines 2 and 3, and “the pressure” in line 3. There is insufficient antecedent basis for the limitations in the claim.
Claim 6 recites the limitation "the pressure data" in line 3. There is insufficient antecedent basis for the limitations in the claim.
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 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Slobounov et al. (US 20120108909 A1, "Slobounov"), in view of Corpin et al. (“Prediction of Diabetic Peripheral Neuropathy (DPN) using Plantar Pressure Analysis and Learning Models”; Published Nov. 11, 2019 in 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, “Corpin”).
Regarding claim 1, Slobounov teaches a computer implemented method for indicating, whether or not a person suffers from a traumatic brain injury (abstract; "A user-friendly reliable process is provided to help diagnose (assess) and treat (rehabilitate) impairment or deficiencies in a person (subject or patient) caused by a traumatic brain injury (TBI) or other neurocognitive disorders.") characterized in that the method comprises: loading sensor data (para. [0117]: " Thereafter, interactive communications comprising the person's responses and performance of the task can be electronically inputted to the CPU with the electronic interactive communications device."; The input for responses and performance into the CPU is sensor data that comes from a force plate (see para. [0120]); para. [0025]: "The CPU and software can capture subject response data and measure the ability of the subject (patient) to perform various tasks. The CPU and software can then compile a quantitative assessment of the subject's experience within the virtual environment.") from foot bottom pressure sensors (Fig. 6; para. [0120]: "The interactive communications device can comprise, but is not limited to:…" motion tracking device, force sensing device, force platform, force plate."; para. [0043]: "FIG. 6 is a back view of a person (subject) with a safety harness, standing on a force platform that is built into the floor and navigating a virtual hospital corridor for use in the TBI diagnostic process (assessment)…") that were pressed between the feet of the person and the underground (Fig. 6 shows an individual standing on a force plate; para. [0128]: "In the diagnostic and rehabilitative process, in conjunction with the balance module or attention module, the person with the TBI performing the task can stand on an interactive communications device which can comprise a moveable and tiltable force sensing device, such as a force platform or force plate,") while the person was playing a predetermined computer game (para. [0126]: "In the diagnostic (assessment) and rehabilitative process, the task can include, but is not limited to: virtual navigation, virtual walking, spatial navigation, virtual object selection, virtual object manipulation or combinations thereof for use in conjunction with a memory module, spatial memory module, recognition module or object recognition module…The task can be electronically performed by virtually arriving at the virtual destination."; The task is implemented virtually, which means it is a computer game task) while controlling the computer game (the virtual task) by exerting pressure on the pressure sensors via the person's feet (para. [0123]: "The task can comprise one or more of the following tasks, but is not limited to:…virtual walking."; Walking is a way to exert pressure on the force plate. Standing on the force plate is also a form of exerting pressure), and analyzing the sensor data (para. [0202]: "The CPU and software modules generate and capture data based on the subject's performance of the specific given tasks. This data is placed in data storage directories for access by the Reporting Module. The CPU in conjunction with the Reporting Module can analyze and score the subject's testing results.") with an algorithm that is adapted to detect the severity of motor and cognitive deficiencies from the traumatic brain injury based on the sensor data. (para. [0202]: "The Reporting Module can display and generate a report comprising a severity index for the TBI or other cognitive and motor function deficiency. This is a relative rating scale that can be used to compare a subject's performance over time. The subject's performance can also be compared to the subject's prior performances or to other subjects' results."; para. [0023]: "The performance data can be electronically inputted and recorded in the CPU and electronically reported (e.g. electronically outputted, e-mailed, transmitted or printed) from the CPU. The performance data can be electronically compared with the person's prior performance data or normal performance data from a data base. The performance data and comparison data can be electronically scored. The score and comparison data can be electronically reported from the CPU and used to help rehabilitate the person (subject or patient) having and suffering from a TBI or other neurological deficiency."; The comparison and score calculated by the CPU can be considered to be analysis by an algorithm). However, Slobounov does not expressly disclose that the method and system detect whether or not a person suffers from peripheral neuropathy (although Slobounov does teach that the system and method can be used for “other neurocognitive disorders” (abstract)).
Corpin, in the same field of endeavor of detecting peripheral neuropathy, discloses analyzing plantar pressure in the feet to determine peripheral neuropathy. Coprin discloses where pressure data can be gathered to predict peripheral neuropathy (abstract: “A common complication of this disease is diabetic peripheral neuropathy (DPN), characterized by the loss of sensation in different parts of the foot. Several studies reported that DPN can be patterned from the plantar pressure data of a person). Corpin also discloses using pressure sensors (Fig. 2: “Segmentations of the foot regions present in the raw data and the corresponding pressure values in kilopascals (kPa).”) and inputting pressure data into a machine learning classifier to predict neuropathy (Abstract: “In this study, the plantar pressure data gathered using the Tekscan F-Scan Research Software were statistically analyzed and used to train different machine learning classifiers.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Slobounov to include detection of peripheral neuropathy. One of ordinary skill would have recognized that peripheral neuropathy is a type of neurocognitive disorder, and that Slobounov’s method and system could use force sensors, such as those disclosed in Corpin, to detect peripheral neuropathy. Further, since Corpin discloses similar methods for detecting neuropathy using pressure sensors, it would have been obvious that Slobounov’s method, which also uses foot pressure sensors, to detect peripheral neuropathy. One of ordinary skill in the art would have recognized that Corpin’s method is effective for detecting clinical neuropathy, and would have been an improvement when combined with the method of Slobounov.
Regarding claim 2, Slobounov, in combination with Corpin, discloses the method of claim 1 (see above). Slobounov further discloses where the method is characterized in that the sensor data and game data concerning the person's gaming performance (para. [0117]: "Thereafter, interactive communications comprising the person's responses and performance of the task can be electronically inputted to the CPU with the electronic interactive communications device."; The responses from the force plate would be sensor data, and the performance data would be the game data) are combined and analyzed by the algorithm (para. [0023]: "The performance data can be electronically inputted and recorded in the CPU and electronically reported (e.g. electronically outputted, e-mailed, transmitted or printed) from the CPU. The performance data can be electronically compared with the person's prior performance data or normal performance data from a data base. The performance data and comparison data can be electronically scored. The score and comparison data can be electronically reported from the CPU and used to help rehabilitate the person (subject or patient) having and suffering from a TBI or other neurological deficiency."; The comparison and score calculated by the CPU can be considered to be analysis by an algorithm).
Regarding claim 3, Slobounov, in combination with Corpin, discloses the method of claim 1 (see above). Slobonov further discloses where the method is further characterized in that the sensor data include data representing the amount of pressure exerted by the person (para. [0128]: “In the diagnostic and rehabilitative process, in conjunction with the balance module or attention module, the person with the TBI performing the task can stand on an interactive communications device which can comprise a moveable and tiltable force sensing device, such as a force platform or force plate, that is electronically hardwired to the CPU and/or connected by wireless communications with the CPU. The person can also communicate with the software via an interactive device.” The force platform or plate interacting with the CPU implies that pressure is exerted and measured by the force sensing device) and/or the timing of the pressure exerted by the person.
Regarding claim 4, Slobounov, in combination with Corpin, discloses the method of claim 2 (see above). Slobounov further discloses where the method is further characterized in that the game data include timing and/or accuracy of control of positioning an item of the game with respect to another item of the game. (para. [0195 - 0201] describe the method of the game; para. [0200]: " The subject should accomplish these tasks as fast as possible without error in positioning the images in the correct sequence." The subject is moving, virtually, images into a correct position. "Without error" and "correct sequence" imply accuracy, and "as fast as possible" implies timing. ; para. [0202]: “The CPU and software modules generate and capture data based on the subject's performance of the specific given tasks. This data is placed in data storage directories for access by the Reporting Module. The CPU in conjunction with the Reporting Module can analyze and score the subject's testing results.” The performance and response data report on the accuracy of given tasks.).
Regarding claim 5, Slobounov, in combination with Corpin, discloses the method of claim 1 (see above). Slobounov further discloses where the method is further characterized in that the algorithm is adapted to indicate the person's reaction time, anticipation time, sensation, skillfulness (para. [0202]: "This is a relative rating scale that can be used to compare a subject's performance over time."; Performance is considered skillfulness; The rating scale applied is an algorithm applied to the data), endurance, plantar pressure deviation, and/or muscle strength of lower limbs from the sensor data or the combination of sensor data and game data.
Regarding claim 6, Slobounov, in combination with Corpin, discloses the method of claim 1 (see above). However, Slobounov does not expressly disclose using an artificial intelligence model to detect peripheral neuropathy.
Corpin discloses where an algorithm is an artificial intelligence model trained to indicate whether a person suffered from peripheral neuropathy based at least on the pressure data (Conclusion and future work: “Different classifiers were trained to classify the presence of DPN among diabetic patients. Of the five algorithms, SVM and MLP exhibited the highest performance of the left and right foot datasets, respectively. Moreover, after applying PCA to the original feature set, it was found out that several plantar pressure parameters on the metatarsal region highly contribute to the information in terms of variance contained by plantar pressure datasets.”; Abstract: “In this study, the plantar pressure data gathered using the Tekscan F-Scan Research Software were statistically analyzed and used to train different machine learning classifiers.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Slobounov to artificial intelligence, as disclosed by Corpin. One of ordinary skill would have recognized that artificial intelligence would improve the accuracy in detecting peripheral neuropathy. Further, one would also recognize that AI could be used to effectively classify foot pressure data. It would have been an obvious improvement to use AI to improve the detection methods of Slobounov since doing so would have improved the detection methods of Slobounov to include peripheral neuropathy detection, as demonstrated by Corpin.
Regarding claim 7, Slobounov discloses a data processing device (CPU 102 and hard drive 103) with a storage device and a processor (para. [0078]: "As shown in FIG. 1 of the drawings, a traumatic brain injury (TBI) diagnostic (assessment) and rehabilitative process and system 100 can have a central processing unit (CPU) 102 including a hard drive 103 which provides data storage."), wherein the storage device comprises instruction (para. [0078-0080]]; The hard drive 103 stores data and communicates with the CPU; The CPU generates the VRE and tasks). Slobounov also discloses a processor (CPU 102) which can execute instructions (para. [0025] discloses the CPU’s analysis of response and performance data). Further, as detailed in the rejection of claim 1, Slobounov, in combination with Corpin, disclose, the method of claim 1 (see 103 rejection above).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Slobounov to include detection of peripheral neuropathy. One of ordinary skill would have recognized that peripheral neuropathy is a type of neurocognitive disorder, and that Slobounov’s device and system could use force sensors, such as those disclosed in Corpin, to detect peripheral neuropathy. Further, since Corpin discloses similar methods for detecting neuropathy using pressure sensors, it would have been obvious that Slobounov’s method, which also uses foot pressure sensors, to detect peripheral neuropathy. One of ordinary skill in the art would have recognized that Corpin’s method is effective for detecting clinical neuropathy, and would have been an improvement when combined with the method of Slobounov.
Regarding claim 8, , Slobounov discloses a computer program comprising instructions (para. [0025]: "The CPU and software can capture subject response data and measure the ability of the subject (patient) to perform various tasks. The CPU and software can then compile a quantitative assessment of the subject's experience within the virtual environment."), which, when the program is executed by a computer (CPU 102). Further, as detailed in the rejection of claim 1, Slobounov, in combination with Corpin, disclose, the method of claim 1 (see 103 rejection above).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the program of Slobounov to include detection of peripheral neuropathy. One of ordinary skill would have recognized that peripheral neuropathy is a type of neurocognitive disorder, and that Slobounov’s device and system could use force sensors, such as those disclosed in Corpin, to detect peripheral neuropathy. Further, since Corpin discloses similar methods for detecting neuropathy using pressure sensors, it would have been obvious that Slobounov’s method, which also uses foot pressure sensors, to detect peripheral neuropathy. One of ordinary skill in the art would have recognized that Corpin’s method is effective for detecting clinical neuropathy, and would have been an improvement when combined with the program of Slobounov.
Claims 3 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Slobounov et al. (US 20120108909 A1, "Slobounov"), Corpin et al. (“Prediction of Diabetic Peripheral Neuropathy (DPN) using Plantar Pressure Analysis and Learning Models”; Published Nov. 11, 2019 in 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, “Corpin”), and Bunn et al. (US 20200129109 A1, “Bunn”).
Regarding claim 3, Slobounov, in combination with Corpin, discloses the method of claim 1 (see above). Slobounov further discloses where the method is further characterized in that the sensor data include data representing the amount of pressure exerted by the person (para. [0128]: “In the diagnostic and rehabilitative process, in conjunction with the balance module or attention module, the person with the TBI performing the task can stand on an interactive communications device which can comprise a moveable and tiltable force sensing device, such as a force platform or force plate, that is electronically hardwired to the CPU and/or connected by wireless communications with the CPU. The person can also communicate with the software via an interactive device.” The force platform or plate interacting with the CPU implies that pressure is exerted and measured by the force sensing device). However, neither reference expressly discloses that the method is characterized by the timing of the pressure exerted by the person.
Bunn, in the same field of endeavor of detecting neurological conditions, discloses a mobility assessment tracking tool (MATT) that uses machine learning to detect biomechanical abnormalities. Bunn discloses where the sensor data include data representing the amount of pressure exerted by the person (FIG. 13 are three 2-D plots over 25 seconds of time as calculated from Wii board data."; para. [0076] mentions the balance between left and right, which is determined from the Wii balance board.; This demonstrates data is taken involving the pressure difference between sensors) and/or the timing of the pressure exerted by the person (Fig. 13; shows a plot over time, which shows when pressure is exerted).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Slobounov to further include data comprising measuring pressure exerted by an individual and the time of exertion, as disclosed by Bunn. One of ordinary skill in the art would have recognized that including this data in the method of Slobounov would have been effective in measuring abnormalities in biomechanical and neurological conditions in order to diagnose and treat conditions, which could be used to restore or improve a subject’s health (see para. [0026] of Bunn). Therefore, it would have been obvious to modify the method and systems of Slobounov to include this data.
Regarding claim 9, Slobounov discloses A system for indicating, whether or not a person suffers from peripheral neuropathy (abstract; "A user-friendly reliable process is provided to help diagnose (assess) and treat (rehabilitate) impairment or deficiencies in a person (subject or patient) caused by a traumatic brain injury (TBI) or other neurocognitive disorders."), characterized in that the system comprises a gaming device (para. [0120]: "The interactive communications device can comprise, but is not limited to: an electronic joystick, electronic mouse, three-dimensional (3D) electronic mouse, electronic controller, navigation controller, handheld controller, fMRI-compatible mouse, wireless controller, wired controller, voice activated controller, video game controller…"; The controller is used for interaction in virtual reality, which is recognized as a type of game.) for playing computer games (para. [0021] describes tasks performed, which are can be considered to be games: "The task can comprise, but is not limited to: object recognition (e.g. recognition of virtual objects), virtual navigation, virtual walking, virtual steering, spatial navigation, object navigation spatial memory, kinesthetic imagery, virtual arrangement of images, standing, balancing and memorizing virtual objects. The VRE can be a three-dimensional (3D) virtual reality environment, the image can be moveable and the task can be performed by the person with the aid of interactive communications device(s)."); and wherein the data processing (CPU 102 and hard drive 103) device is adapted to load the sensor data (para. [0023]: " The performance data can be electronically inputted and recorded in the CPU and electronically reported (e.g. electronically outputted, e-mailed, transmitted or printed) from the CPU. The performance data can be electronically compared with the person's prior performance data or normal performance data from a data base. The performance data and comparison data can be electronically scored.") from foot bottom pressure sensors (para. [0027]: "In addition, motion tracking devices [i.e. force platform, Vicon, accelerometers, etc.] can be used in conjunction with the software to provide additional data from the subject's virtual experience and responses to VR scene manipulations."), from the gaming device or from another storage device (para. [0078]: "hard drive 103, which provides storage") of the system. Further, as detailed in the rejection of claim 7, Slobounov, in combination with Corpin, disclose the data processing device of claim 7 (see rejection of claim 7 above). However, neither reference expressly discloses at least two foot bottom pressure sensors that are connectable to the gaming device in a control signal transmitting manner.
Bunn discloses at least two foot bottom pressure sensors (para. [0059]: "One such board is the WiiBoard balance board for the game console Wii Fit by Nintendo, Wii balance board referenced in Wikipedia. The MATT has integrated pressure data from the WiiBoard data into the MATT system for collection of pressure data from the pressure sensors located on the four corners of the board. ") that are connectable to the gaming device in a control signal transmitting manner (para. [0059]: "The MATT has integrated pressure data from the WiiBoard data into the MATT system for collection of pressure data from the pressure sensors located on the four corners of the board." This shows that the data is transmitted from the board to MATT electronically. MATT has fuzzy logic computer machine learning which processes the data from the board.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and methods of Slobounov to further a pressure sensor with at least two foot sensors, as disclosed by Bunn. One of ordinary skill in the art would have recognized that the disclosed number of pressure sensors would have been effective in measuring abnormalities in biomechanical and neurological conditions in order to diagnose and treat conditions, which could be used to restore or improve a subject’s health (see para. [0026] of Bunn). Therefore, it would have been obvious to modify the method and systems of Slobounov to include at least two pressure sensors since Bunn discloses doing the same to diagnose and improve subject’s health.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OWEN LEWIS MARSH whose telephone number is (571)272-8584. The examiner can normally be reached 7:30am – 5pm (M-Th) and 8am – noon (F).
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/O.L.M./Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796
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