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 Rejections - 35 USC § 103
Claim(s) 71-90 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barsa (4,570,640) and Wybo et al (2021/0060337).
71. A method for determining an effect of anaesthesia in a subject, comprising:
stimulating a body part of a subject at one or more stimulation sites, wherein said body part
is under local or regional anaesthesia and wherein said stimulation is lower than a pain sensation threshold of the subject; (see at least col. 14:38+, abstract, col. 15:29 of Barsa)
measuring a neurological response caused by said stimulation using an electrode
configured to measure event-related potentials (ERP), wherein said electrode is positioned on the head or nape of the subject, (see at least col. 15:8-19, and col. 18:15, and figures 3,4 of Barsa which show sensing and stimulating electrodes on the back and nape of neck)
applying a trained machine learning algorithm on the measured neurological response to
determine an effect of said local or regional anesthesia. (Barsa teaches determining effect of anesthesia, see abstract, but is silent as to machine learning. Wybo teaches detecting induced neuromuscular responses using machine learning, see at least ¶81-84. It would have been obvious to analyze the responses of Barsa using machine learning methods since it would provide a fast and efficient manner of analyzing results in a predictable way.)
72. The method according to claim 71, wherein said measuring comprises measuring
said response in up to 300 milliseconds following said stimulation. (the electrodes of Barsa can measure up to 300ms, which is the expected range of the response)
73. The method according to claim 71, wherein determining said regional anaesthesia
effect comprises determining a depth of said local or regional anaesthesia. (see at least abstract of Barsa)
74. The method according to claim 71, wherein the anaesthesia is regional anesthesia (abstract of Barsa teaches spinal nerve blocks, which provide anaesthsia of the spinal region)
and wherein the method further comprises repeating said stimulating at two or more axially
spaced-apart stimulation sites, and wherein said machine learning algorithm is further configured to assess an axial distribution of said regional anaesthesia. (at least the abstract of Barsa teaches repeated and sequential scanning spaced points. The points are in the axial direction of the person, as shown in figure 4. As mentioned, Wybo teaches machine learning which can be used to efficiently determine the distribution of anesthesia along the axial direction so that it can be determined if the anaestheisa is spread across the patient appropriately)
75. The method according to claim 74, wherein said machine learning algorithm is
further configured to output a prediction regarding the effect of the regional anaesthesia effect,
based on the axial distribution. (see at least ¶83,85 of Wybo)
76. The method of claim 74, wherein the body part is a back of the subject. (see figure 4 of Barsa)
77. The method according to claim 74, wherein said one or more stimulation sites
comprise at least one stimulation site in one or more dermatomes located between S5 to T2
dermatomes. (see at least figure 6 of Barsa and col. 14:20-30)
78.The method according to claim 71, wherein said stimulating comprises initially
stimulating said body part before administering said local or regional anesthesia, and wherein a neurological response to the initial stimulation is further provided as an input to the machine
learning algorithm. (Barsa is silent as to initially stimulating. However, such step is considered to have been obvious since it would yield predictable results, such as allowing the user to determine patient response before anesthesia is applied so that a baseline response can be measured, increasing the accuracy of further measurements)
79. The method according to claim 71, wherein said stimulating comprises delivering
an electric field to said body part using a stimulating electrode, wherein said delivered electric
field has an intensity value in a range between 0.5-40 mA and/or a frequency value in a range
between 1-4000 Hz. (see at least col. 18:21-35 of Barsa)
80. The method according to claim 71, wherein said stimulating comprises delivering a
temperature stimuli. (see at least col. 10:20 of Barsa)
81.The method according to claim 71, further comprising generating an alert signal if
the effect of the local or regional anaesthesia deviates or predicted to deviate from a planned effect of the anaesthesia. (Barsa is silent as to an alert. Wybo teaches giving an alert, see at least ¶24,25 of Wybo. It would have been obvious to use an alert with the device of Barsa since it would notify the user of a dangerous condition in a predictable manner)
82. The method according to claim 71, wherein said machine learning algorithm is
further configured to identify hemiparesis in said subject, based on said determined regional
anaesthesia effect. (Barsa is silent as to hemiparesis. However, hemiparesis, or paralysis on one side of the body, is considered to be obvious to check for in the patient since each patient may have different reactions to the various stimulation applied, such as a non-symmetrical reaction to the stimulation)
83. The method according to claim 71, wherein said machine learning algorithm is
further configured to output a pharmacodynamic profile of one or more anaesthetic compounds
used for said anaesthesia in said subject, a trend of said anaesthesia effect and/or a prediction of said anaesthesia effect, based on said determined anaesthesia effect. (Barsa is silent as to pharmacodynamic profile or prediction. However, Wybo teaches making a prediction, see at least ¶83,85. It would have been obvious to use such with the device of Barsa since it would yield important information for the user re the anesthetic effect so that informed choices as to anesthesia can be made)
84. A system for monitoring an effect of anaesthesia in a subject, the system comprising:
at least one stimulator configured to deliver stimulation to at least one stimulation site on
a body part of the subject, wherein said stimulation is lower than a pain sensation threshold of the subject; (see at least abstract and col. 15:20-31 of Barsa)
at least one event-related potentials (ERP) electrode configured to measure the neurological
response to the stimulation, wherein the at least one ERP electrode is configured to be positioned on a head or nape of the subject; (see at least figure 3 which shows electrodes 55 and figure 4 which shows electrode strip at nape of neck, and col. 18:15 of Barsa)
a memory; (col. 16:19 of Barsa)
a control circuitry operationally connected to said memory, said at least one stimulator and
said at least one ERP electrode; (see at least figure 2 (47) of Barsa)
wherein said control circuitry is configured to:
activate said at least one stimulator to deliver a stimulation to said at least one stimulation
site, according to stimulation parameters values stored in said memory; (col. 15:20-30 of Barsa)
receive at least one signal from said at least one ERP electrode measured in response to
said stimulation; (col. 15:8-20 of Barsa)
to apply a trained machine learning algorithm on the received signal to thereby determine
the anaesthesia effect on said body part. (Barsa teaches determining effect of anesthesia, see abstract, but is silent as to machine learning. Wybo teaches detecting induced neuromuscular responses using machine learning, see at least ¶81-84. It would have been obvious to analyze the responses of Barsa since it would provide a fast and efficient manner of analyzing results in a predictable way.)
85. The system of claim 84, wherein the at least one ERP electrode is configured to
measure the neurological response in up to 300 ms after the stimulation. (the electrodes of Barsa can measure up to 300ms, which is the expected range of the response)
86. The system according to claim 84, wherein determining said anaesthesia effect
comprises determining an axial distribution of the anaesthesia and/or a depth of the anaesthesia. (see at least figure 4 which shows electrodes along an axis of the patient, and figure 7 of Barsa)
87. The system according to claim 84, wherein said at least one stimulator comprises at
least one stimulating electrode shaped and sized to be positioned at said at least one stimulation site, wherein said system further comprises at least one pulse generator functionally connected to said at least one stimulating electrode, and wherein said control circuitry is configured to: (see at least figure 7,10 of Barsa which shows electrodes 55, and pulse generator 49 in figure 2)
activate said pulse generator to generate and deliver an electric field to said at least one stimulating electrode, wherein said electric field is generated according to electric field parameter values stored in said memory. (figure 10 of Barsa shows a stimulator, and col. 15:66 teaches pules, and col. 15:61 of Barsa teaches a memory)
88. The system according to claim 84, wherein said control circuitry determines a
effect of said anaesthesia by activating said at least one pulse generator to generate and deliver two or more electric fields separated in time and in a stimulation location, by measuring a neurological response to the two or more electric fields, and by determining a relation between a first measured neurological response to a first electric field delivery, and a second neurological response to a second electric field delivery. (see at least col. 16:40-65 of Barsa which teaches scanning the electrodes and comparing the result to a reference. The scanning operation stimulates the patient over different areas, and determines perception of sensation at each location by comparing to a refence level or criteria)
89. The system according to claim 88, wherein said control circuitry activates said pulse
generator to generate and deliver two consecutive electric fields with an interval between the two consecutive electric field which is higher than 180 milliseconds. (Barsa teaches stimulating at two sites, see at least col. 15:25-30, col. 16:29-40. Barsa is silent as to 180 ms interval. However, a 180ms interval is considered to have been an obvious design choice in order to avoid the two electric fields from interfering with each other)
90. The system according to claim 89, wherein an intensity of said generated electric
field is in a range between 0.5 mA - 40 mA and/or wherein a frequency of said generated electric field is in a range between 0.1 Hz-4000 Hz. (see at least col. 18:21-35 of Barsa)
Claim Rejections - 35 USC § 101
Claims 71-90 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 71-90 are for a method and apparatus. Thus, the claims are for statutory subject matter.
Step 2a, prong 1
Claim 71 includes a machine learning algorithm. This algorithm is considered to be an abstract idea in the form of mathematical calculations. Similarly, claim 84 includes a machine learning algorithm. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) See MPEP 2106.04(a).
Step 2a, prong 2
Claim 71 includes the steps of stimulating and measuring. These steps are considered to gather data for use in the algorithm, and thus do not serve to integrate the algorithm into a practical application. Claim 72-83 include various details re gathering data for use with the algorithm or further steps used in the algorithm but fail to include a step that integrate the abstract idea into a practical application. Similarly, claim 84 includes a stimulator, control circuitry, and memory. All of which are used to perform the abstract idea, but do not integrate it into a practical application. Also, claims 85-90 only add further data gathering steps, or further steps of the abstract idea, and do not integrate the abstract idea into a practical application. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). See MPEP 2106.05(f).
Step 2b
The claims include memory, stimulating circuitry, electrodes, measuring circuitry that are set forth in general terms. All of this structure is considered to be well understood, routine in the art either considered by themselves or as a whole with the abstract idea. For example, Bray (2021/0145358), cited by applicant, shows a processor, stimulus generator and recording monitor that records muscle/nerve activity. Gray (2012/0015403), cited by applicant, teaches processor, memory, electrodes and neural response circuitry, see at least ¶33-35.
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/Scott M. Getzow/Primary Examiner, Art Unit 3792