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 .
Claims Pending
Applicant’s previous cancellation of claims 2 and 8 is acknowledged.
Claims 1, 3-7, and 9-15 are currently under examination.
Claim Objections
Claims 1, 3-7, and 9-15 are objected to because of the following informalities:
In Claim 1, “first detected a transition duration” (line 19), should read -first detected; a transition duration- (Examiner's Note: insertion of a semicolon, indentation, and “a transition duration” should be placed on the following line. Additional spacing and indentation changes apply, such as to better align the indentations and presentation of “a transition duration”, “a transition stability”, and “a frequency of transition”),
In Claim 1, “receive” (line 4), should read -receiving-,
In Claim 1, “receive” (line 8), should read -receiving-,
In Claim 1, “process” (line 11), should read -processing-,
In Claim 1, “process” (line 4 of page 4), should read -processing-,
In Claim 5, “has been trained” (Claim 5, lines 2), should read -is trained-,
In Claim 9, “continue” (line 2), should read -further comprising continuing-,
In Claim 9, “to obtain” (line 2), should read -obtaining-,
In Claim 14, -line break- (line 11), (Examiner's Note: removal of the line break),
In Claim 14, “first detected a transition duration” (lines 4-5 of page 7), should read -first detected; a transition duration- (Examiner's Note: insertion of a semicolon) (Examiner's Note: There are a plurality of additional indentation and spacing inconsistencies within claim 14 that need further correction, such as the break between in line 11 of claim 14. Additionally, the lack of an additional indentation for “a transition duration” (Page 7, lines 5) compared to “a transition stability” (Page 7, line 9) and “a frequency of transition” (Page 7, line 14), and subsequent additional indentation of “processing the values” (Page 7, line 17))
Claims 3-7 and 9-13 are dependent on claim 1 and as such are also objected to.
Claim 15 is dependent on claim 14, and as such is also objected to.
Appropriate correction is required.
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:
Claims 1 and 14: The claim limitation “sensory and monitoring unit adapted to detect a transition of the subject from the first brain state to the second brain state of the subject” has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses a generic placeholder “unit” coupled with functional language “adapted to detect a transition of the subject from the first brain state to the second brain state of the subject” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier that has a known structural meaning before the phrase “unit”,
Claims 1 and 14: The claim limitation “a brain monitoring system for monitoring brain activity” has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses a generic placeholder “system” coupled with functional language “for monitoring brain activity” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier that has a known structural meaning before the phrase “system”,
Claim 10: The claim limitation “sensory and monitoring unit adapted to detect a transition of the subject from a first brain state to a second brain state of the subject” has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses a generic placeholder “unit” coupled with functional language “adapted to detect a transition of the subject from a first brain state to a second brain state of the subject” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier that has a known structural meaning before the phrase “unit”,
Claim 11: The claim limitation “sensory and monitoring unit adapted to detect a transition based on at least one of: brain activity information, cardiorespiratory information, cardioballistography information, respiration rate, behavioral information and/or information corresponding to the subject's performance on a repetitive task” has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses a generic placeholder “unit” coupled with functional language “adapted to detect a transition based on at least one of: brain activity information, cardiorespiratory information, cardioballistography information, respiration rate, behavioral information and/or information corresponding to the subject's performance on a repetitive task” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier that has a known structural meaning before the phrase “unit”,
Claim 12: The claim limitation “sleep regulatory unit adapted to induce a change in brain state of the subject” has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses a generic placeholder “unit” coupled with functional language “adapted to induce a change in brain state of the subject” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier that has a known structural meaning before the phrase “unit”,
Claim 13: The claim limitation “sleep regulatory unit adapted to alternately induce sleep in the subject and wake the subject from sleep for a predetermined number of wake/sleep cycles” has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses a generic placeholder “unit” coupled with functional language “adapted to alternately induce sleep in the subject and wake the subject from sleep for a predetermined number of wake/sleep cycles” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier that has a known structural meaning before the phrase “unit”,
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.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation:
“the sensory and monitoring unit 140 may detect a transition based on brain activity data. This may be the brain activity data 125 obtained by the brain monitoring system 120, or brain activity data…” or equivalents thereof, as described on Page 12, lines 14-30 of the disclosure filed on 04/17/2023.
An MRI scanner, a PET scanner, EEG machine, or equivalents thereof, as described on Page 7, lines 4-9 of the disclosure filed on 04/17/2023.
“the sensory and monitoring unit 140 may detect a transition based on brain activity data. This may be the brain activity data 125 obtained by the brain monitoring system 120, or brain activity data…” or equivalents thereof, as described on Page 12, lines 14-30 of the disclosure filed on 04/17/2023.
“the sensory and monitoring unit 140 may detect a transition based on brain activity data. This may be the brain activity data 125 obtained by the brain monitoring system 120, or brain activity data…” or equivalents thereof, as described on Page 12, lines 14-30 of the disclosure filed on 04/17/2023.
A balloon or equivalents thereof, as described on Page 13, lines 18-21 of the disclosure filed on 04/17/2023. (Examiner's Note: While the applicant’s specification does state “sleep regulatory unit may use sound to induce sleep in the subject, for example, by generating a rhythmic ticking sound. The sleep regulatory unit may wake a subject from sleep by the use of, for example, sound, tactile feedback and/or changes in light levels. The sleep regulatory unit may induce a change in the subject from a first sleep state to a second, different sleep state by the use of sensory stimulation such as light, or gentle auditory or tactile signals.” (Page 13, lines 26-31), which does describe different kinds of feedback of light and sound. However, the applicant’s specification lacks sufficient detail as to the structures that produce light and sound, such as the presence of any type of speakers or lights. As such, the above structure is interpreted as the indicated balloon that provides tactile feedback).
A balloon or equivalents thereof, as described on Page 13, lines 18-21 of the disclosure filed on 04/17/2023 (Examiner's Note: While the applicant’s specification does state “sleep regulatory unit may use sound to induce sleep in the subject, for example, by generating a rhythmic ticking sound. The sleep regulatory unit may wake a subject from sleep by the use of, for example, sound, tactile feedback and/or changes in light levels. The sleep regulatory unit may induce a change in the subject from a first sleep state to a second, different sleep state by the use of sensory stimulation such as light, or gentle auditory or tactile signals.” (Page 13, lines 26-31), which does describe different kinds of feedback of light and sound. However, the applicant’s specification lacks sufficient detail as to the structures that produce light and sound, such as the presence of any type of speakers or lights. As such, the above structure is interpreted as the indicated balloon that provides tactile feedback).
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.
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.
Claim 1, 3-7, and 9-15 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites the limitation “process the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles”, where the applicant’s specification lacks sufficient detail in regards to the manner in which the values of transition parameters are processed and the manner in which the group that the subject most closely resembles is identified. The applicant does state “In some embodiments, the processing system 110 may identify which of a plurality of groups the subject most closely resembles by using a lookup table comprising values of transition parameters and the group of subjects the values correspond to.” (Page 10, lines 16-18 of applicant’s spec.), however, while this does indicate a table of values, this does not provide sufficient detail in regards to the manner in which the group that the subject most closely resembles is identified. The applicant further states “an artificial neural network 115 in order to identify which of a plurality of groups the subject most closely resembles. The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain…” (Page 10, lines 20-36 of applicant’s spec.), however, the applicant’s specification lacks sufficient detail in regards to any specific weights, biases, or layers that are used for the model itself. As such, the claim is rejected.
Claim 14 recites the limitation “processing the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles”, where the applicant’s specification lacks sufficient detail in regards to the manner in which the values of transition parameters are processed and the manner in which the group that the subject most closely resembles is identified. The applicant does state “In some embodiments, the processing system 110 may identify which of a plurality of groups the subject most closely resembles by using a lookup table comprising values of transition parameters and the group of subjects the values correspond to.” (Page 10, lines 16-18 of applicant’s spec.), however, while this does indicate a table of values, this does not provide sufficient detail in regards to the manner in which the group that the subject most closely resembles is identified. The applicant further states “an artificial neural network 115 in order to identify which of a plurality of groups the subject most closely resembles. The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain…” (Page 10, lines 20-36 of applicant’s spec.), however, the applicant’s specification lacks sufficient detail in regards to any specific weights, biases, or layers that are used for the model itself. As such, the claim is rejected.
Claim 4 recites the limitation “inputting the brain activity data and/or the values of the one or more transition parameters into an artificial neural network”, where the applicant’s specification lacks sufficient detail in regards to the structure of the artificial neural network. The applicant’s specification does state “an artificial neural network 115 in order to identify which of a plurality of groups the subject most closely resembles. The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain…” (Page 10, lines 20-36), however, the applicant’s specification lacks sufficient detail in regards to any specific weights, biases, or layers that are used for the model itself. As such, the claim is rejected.
Claim 5 recites the limitation “wherein the artificial neural network has been trained using a training algorithm configured to receive an array of training inputs and known outputs”, where the applicant’s specification lacks sufficient detail in regards to the structure of artificial neural network. The applicant’s specification does state “an artificial neural network 115 in order to identify which of a plurality of groups the subject most closely resembles. The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain…” (Page 10, lines 20-36), however, the applicant’s specification lacks sufficient detail in regards to any specific weights, biases, or layers that are used for the model itself. As such, the claim is rejected.
Claims 3-7 and 9-13 are dependent on claim 1, and as such are also rejected.
Claim 15 is dependent on claim 14, and as such is also rejected.
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, 3-7, and 9-15 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 “process the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles”, which fails to effectively define the metes and bounds of the claim as it is unclear as to the manner in which the values are processed to determine which group the subject most closely resembles. How is the identification performed? Is there a specific threshold? How is “most closely” determined? The applicant’s specification does state “In some embodiments, the processing system 110 may identify which of a plurality of groups the subject most closely resembles by using a lookup table comprising values of transition parameters and the group of subjects the values correspond to.” (Page 10, lines 16-18), however, while this does indicate a table of values, this does not provide sufficient detail in regards to the manner in which the group that the subject most closely resembles is identified. The applicant’s spec. further states “an artificial neural network 115 in order to identify which of a plurality of groups the subject most closely resembles. The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain…” (Page 10, lines 20-36), however, the applicant’s specification lacks sufficient detail in regards to the manner in which the neural network is trained or the presence of any specific weights, biases, or layers that are used for the model itself in identifying the group. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this will be interpreted as any type of processing and any degree of resemblance to one of a plurality of groups.
Claim 14 recites the limitation “processing the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles”, which fails to effectively define the metes and bounds of the claim as it is unclear as to the manner in which the values are processed to determine which group the subject most closely resembles. How is the identification performed? Is there a specific threshold? How is “most closely” determined? What is “most closely” considered to be? The applicant’s specification does state “In some embodiments, the processing system 110 may identify which of a plurality of groups the subject most closely resembles by using a lookup table comprising values of transition parameters and the group of subjects the values correspond to.” (Page 10, lines 16-18), however, while this does indicate a table of values, this does not provide sufficient detail in regards to the manner in which the group that the subject most closely resembles is identified. The applicant’s spec. further states “an artificial neural network 115 in order to identify which of a plurality of groups the subject most closely resembles. The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain…” (Page 10, lines 20-36), however, the applicant’s specification lacks sufficient detail in regards to the manner in which the neural network is trained or the presence of any specific weights, biases, or layers that are used for the model itself in identifying the group. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this will be interpreted as any type of processing and any degree of resemblance to one of a plurality of groups.
Claim 1 recites the limitation “a transition stability, wherein a transition stability is a number of transitions between neuronal networks in the brain activity data from a moment at which activity in neuronal networks associated with the first brain state starts to become weaker to a moment at which a network associated with the second brain state becomes fully established”, which fails to effectively define the metes and bounds of the claim as it is unclear as to what “network” is being referred to by the phrase “a network associated with the second brain state becomes fully established”. Is this also a neuronal network? The rest of the claim uses the phrase “neuronal network”, where it is unclear as to why the applicant has made the switch. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, “a network” will be interpreted as -a neuronal network-.
Claim 14 recites the limitation “a transition stability, wherein a transition stability is a number of transitions between neuronal networks in the brain activity data from a moment at which activity in neuronal networks associated with the first brain state starts to become weaker to a moment at which a network associated with the second brain state becomes fully established”, which fails to effectively define the metes and bounds of the claim as it is unclear as to what “network” is being referred to by the phrase “a network associated with the second brain state becomes fully established”. Is this also a neuronal network? The rest of the claim uses the phrase “neuronal network”, where it is unclear as to why the applicant has made the switch. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, “a network” will be interpreted as -a neuronal network-.
Claim 3 recites the limitation “wherein the step of processing the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects further uses one or more characteristics of the subject to identify which of the plurality of groups the subject most closely resembles.” This is a “use” claim that is indefinite, as the applicant fails to effectively define the metes and bounds of the claim. The applicant merely recites the use, without providing further detail as to how the use is practiced, and as such the claim indefinite. For examination purposes, this will be interpreted as any type of use that involves one or more characteristics of the subject.
Claim 4 recites the limitation “inputting the brain activity data and/or the values of the one or more transition parameters into an artificial neural network”, which fails to effectively define the metes and bounds of the claim as it is unclear as to the structure of the artificial neural network. The applicant’s specification does state “an artificial neural network 115 in order to identify which of a plurality of groups the subject most closely resembles. The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain…” (Page 10, lines 20-36), however, the applicant’s specification lacks sufficient detail in regards to any specific weights, biases, or layers that are used for the model itself. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, the artificial neural network will be interpreted as a generic algorithm.
Claim 5 recites the limitation “wherein the artificial neural network has been trained using a training algorithm configured to receive an array of training inputs and known outputs”, which fails to effectively define the metes and bounds of the claim as it is unclear as to the structure of the artificial neural network. The applicant’s specification does state “an artificial neural network 115 in order to identify which of a plurality of groups the subject most closely resembles. The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain…” (Page 10, lines 20-36), however, the applicant’s specification lacks sufficient detail in regards to any specific weights, biases, or layers that are used for the model itself. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, the artificial neural network will be interpreted as a generic algorithm.
Claim 7 recites the limitation “wherein the one or more transition parameters further comprise the one or more networks active during the transition”, which fails to effectively define the metes and bounds of the claim as it is unclear as to what the one or more transition parameters further comprise. For example, does this mean the transition parameter further includes the information of which networks are active? Does this mean that the transition parameter itself comprises a neuronal network that is active? Does this mean that the transition parameter is one of “transition stability, wherein a transition stability is a number of transitions between neuronal networks in the brain activity data from a moment at which activity in neuronal networks associated with the first brain state starts to become weaker to a moment at which a network associated with the second brain state becomes fully established” (Claim 1), “wherein a transition timing is a length of time between a time at which a transition is detected in the subject by the sensory and monitoring unit and a time at which changes in neuronal networks, identifiable in the subject's brain activity data, responsive to the change in sleep state are first detected” (Claim 1), “a transition duration, wherein a transition duration is a duration from a moment at which a neuronal network associated with the first brain state of the subject starts to become weaker to a moment at which a neuronal network associated with the second brain state becomes fully established” (Claim 1). As “frequency of transition” does not explicitly include a neuronal network as part of the definition, “a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period”, does this then mean that the transition parameter is not “a frequency of transition”? Additionally, it is unclear as to what network is being referred to, as claim 1 recites “transition stability, wherein a transition stability is a number of transitions between neuronal networks in the brain activity data from a moment at which activity in neuronal networks associated with the first brain state starts to become weaker to a moment at which a network associated with the second brain state becomes fully established”, which includes both “a network” and “neuronal networks”. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this will be interpreted as the transition parameter involving brain activity.
Claim 10 recites the limitation “a sensory and monitoring unit”, which fails to effectively define the metes and bounds of the claim as it is unclear whether this is the same “sensory and monitoring unit” from claim 1, which claim 10 is dependent on, or if this is a new sensory and monitoring unit. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this will be interpreted as the same sensory and monitoring unit from claim 1.
Claims 3-7 and 9-13 are dependent on claim 1, and as such are also rejected.
Claim 15 is dependent on claim 14, and as such is also rejected.
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.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because single "use" claims that do not purport to claim a process, machine, manufacture, or composition of matter fail to comply with 35 U.S.C. 101. Therefore, the claim is rejected as it does not fall under a statutory category of 35 U.S.C. 101.
Claims 1, 4-7, 9-11, and 14-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards a judicial exception without significantly more. These claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception or that are sufficient to amount to significantly more than the judicial exception.
Step 1 of the subject matter eligibility test
Claims 1 and 14 are directed towards a method and computer implemented method, respectively, which describes one of the four statutory categories of patentable subject matter.
Step 2A of the subject matter eligibility test
Prong 1: Claim 1 recites the abstract idea of a mental process as follows: “receive… “…brain activity data of the subject obtained during a transition of the subject from a first brain state to a second brain state of the subject, wherein at least one of the first brain state and/or the second brain state is a sleep state”, “receive… “…information corresponding to the detected transition”, “detect a transition of the subject from the first brain state to the second brain state of the subject”, “process the brain activity data and the information…” “…to obtain a value for one or more transition parameters of the brain activity data, a transition parameter being a parameter representative of the transition, wherein the one or more transition parameters comprise at least one of: a transition timing, wherein a transition timing is a length of time between a time at which a transition is detected in the subject by the sensory and monitoring unit and a time at which changes in neuronal networks, identifiable in the subject's brain activity data, responsive to the change in sleep state are first detected, a transition duration, wherein a transition duration is a duration from a moment at which a neuronal network associated with the first brain state of the subject starts to become weaker to a moment at which a neuronal network associated with the second brain state becomes fully established; a transition stability, wherein a transition stability is a number of transitions between neuronal networks in the brain activity data from a moment at which activity in neuronal networks associated with the first brain state starts to become weaker to a moment at which a network associated with the second brain state becomes fully established; and/or a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period”, “process the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles, wherein the plurality of groups of subjects comprises at least a first group and a second group, wherein the first group comprises healthy subjects and the second group comprises subjects having a mental disorder”.
Prong 1: Claim 14 recites the abstract idea of a mental process as follows: “receiving… “…brain activity data of the subject obtained during a transition of the subject from a first brain state to a second brain state of the subject, wherein at least one of the first brain state and/or the second brain state is a sleep state”, “receiving… “…information corresponding to the detected transition”, “detect a transition of the subject from the first brain state to the second brain state of the subject”, “processing the brain activity data and the information…” “…to obtain a value for one or more transition parameters of the brain activity data, a transition parameter being a parameter representative of the transition, wherein the one or more transition parameters comprise at least one of: a transition timing, a transition duration, a transition stability and/or a frequency of transition, wherein a transition timing is a length of time between a time at which a transition is detected in the subject and a time at which changes in neuronal networks, identifiable in the subject's brain activity data, responsive to the change in sleep state are first detected; a transition duration, wherein a transition duration is a duration from a moment at which a neuronal network associated with the first brain state of the subject starts to become weaker to a moment at which a neuronal network associated with the second brain state becomes fully established; a transition stability, wherein a transition stability is a number of transitions between neuronal networks in the brain activity data from a moment at which activity in neuronal networks associated with the first brain state starts to become weaker to a moment at which a network associated with the second brain state becomes fully established; and/or a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period”, “processing the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles, wherein the plurality of groups of subjects comprises at least a first group and a second group, wherein the first group comprises healthy subjects and the second group comprises subjects having a mental disorder”.
The receiving brain activity data of the subject obtained during a transition of the subject from a first brain state to a second brain state of the subject, wherein at least one of the first brain state and/or the second brain state is a sleep state, receiving information corresponding to the detected transition, detecting a transition of the subject from the first brain state to the second brain state of the subject, processing the brain activity data and the information to obtain a value for one or more transition parameters of the brain activity data, a transition parameter being a parameter representative of the transition, wherein the one or more transition parameters comprise at least one of: a transition timing, a transition duration, a transition stability and/or a frequency of transition, wherein a transition timing is a length of time between a time at which a transition is detected in the subject and a time at which changes in neuronal networks, identifiable in the subject's brain activity data, responsive to the change in sleep state are first detected; a transition duration, wherein a transition duration is a duration from a moment at which a neuronal network associated with the first brain state of the subject starts to become weaker to a moment at which a neuronal network associated with the second brain state becomes fully established; a transition stability, wherein a transition stability is a number of transitions between neuronal networks in the brain activity data from a moment at which activity in neuronal networks associated with the first brain state starts to become weaker to a moment at which a network associated with the second brain state becomes fully established; and/or a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period, and processing the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles, wherein the plurality of groups of subjects comprises at least a first group and a second group, wherein the first group comprises healthy subjects and the second group comprises subjects having a mental disorder can be practically performed by the human mind, with the aid of a pen and paper, but for performance on a generic processor, in a computer environment, or merely using the computer as a tool to perform the steps.
A person of ordinary skill in the art could reasonably receive brain activity data by being handed a piece of paper with brain activity data or with a generic computer. A person of ordinary skill in the art could reasonably detect a transition of a brain based on having brain activity data with a generic computer, mentally, or with a pen and paper. A person of ordinary skill in the art could reasonably receive information corresponding to a transition of a brain based on being handed a piece of paper with brain activity data or with a generic computer. A person of ordinary skill in the art could reasonably process brain activity data and transition data to obtain a transition parameter based on being handed a piece of paper with brain activity data and transition data mentally, with a generic computer, or with a pen and paper. A person of ordinary skill in the art could reasonably process transition parameters of brain activity for a subject and transition parameters for groups to mentally identify which group the brain activity for the subject resembles with a generic computer, mentally, or with a pen and paper based on being handed a piece of paper with transition parameters of brain activity for a subject and transition parameters for groups. There is currently nothing to suggest an undue level of complexity in the receiving, detecting, processing, or identifying steps. Therefore, a person would be able to practically be able to perform the detecting, processing, and identifying steps mentally or with the aid of pen and paper.
Prong Two: Claims 1 and 14 do not recite additional elements that integrate the mental process into a practical application. Therefore, the claims are “directed to” the mental process. 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 generic computer structure) and
Add insignificant extra-solution activity (the pre-solution activity of: using generic data-gathering components (e.g. a brain monitoring system (Examiner's Note: interpreted as An MRI scanner, a PET scanner, EEG machine, or equivalents thereof as indicated in the 112f interpretation above)),
For claims 1 and 14. The additional elements merely serve to gather data to be used by the abstract idea. The brain monitoring system is merely used as a pre-solution step of necessary data gathering to be used by the abstract idea. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing that is performed remains in the abstract realm, i.e. the gathered data is not used for a treatment or meaningful purpose. Additionally, there is no overall improvement to existing technology present. The mental process merely functions on generic computer elements that do not change the functionality of the device itself. 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 for Claims 1 and 14.
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example,
A brain monitoring system and computational device as disclosed by Stefancik (US Pub. No. 20020107443) hereinafter Stefancik “a MRI scanner and computer system capable of generating two-dimensional representations of three dimensional data, examples of which are well known to those skilled in the art, and include principally maximum intensity projection (MIP) and volume rendering (VR) means. MRI equipment may be acquired from General Electric, Siemens, Philips, Marconi and others, and typical workstations include General Electric's Advantage Windows, Siemens' 3D Virtuoso and Syngo, Philips' EasyVision, Vital Images' Vitrea, and Algotec's ProVision. A preferred programming language for implementing the method is IDL (Interactive Data Language, Research Systems), but in principle any language compatible with the hardware system may be appropriate.” (Par. 25) and Topgaard (US Pub. No. 20190011519) hereinafter Topgaard “The method may be performed using a state-of-the-art NMR spectrometer or MRI device. As is well-known in the art, such devices may include one or more processors for controlling the operation of the device, inter alia the generation of the magnetic gradient pulse sequences, the acquisition of signals as well as sampling and digitizing the measured signals for forming data representing the acquired signals.” (Par. 114)
are all well-understood, routine, and conventional.
Claims 4-7, 9-11, 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) as all of the elements are directed to the further describing of the abstract idea, pre-solution activities, and computer implementation.
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:
inputting the brain activity data and/or the values of the one or more transition parameters into an artificial neural network (Claim 4) (Examiner's Note: A person of ordinary skill in the art could reasonably input brain activity data into an algorithm with a generic computer)
the artificial neural network has been trained using a training algorithm configured to receive an array of training inputs and known outputs (Claim 5) (Examiner's Note: A person of ordinary skill in the art could reasonably train an algorithm with brain activity data based on being handed a piece of paper with brain activity training data with a generic computer),
wherein the transition is one of: a transition from a wakeful state to a sleep state; a transition from a sleep state to a wakeful state; or a transition from a first sleep state to a second, different sleep state (Claim 6),
wherein the one or more transition parameters further comprise the one or more networks active during the transition (Claim 7),
receiving brain activity data of the subject until a predefined number of transitions have been recorded (Claim 9),
obtain a value for one or more transition parameters of the brain activity data for each detected transition (Claim 9),
detect a transition of the subject from a first brain state to a second brain state of the subject, wherein at least one of the first brain state and/or the second brain state is a sleep state (Claim 10),
detect a transition based on at least one of: brain activity information, cardiorespiratory information, cardioballistography information, respiration rate, behavioral information and/or information corresponding to the subject's performance on a repetitive task (Claim 11).
Further describe the pre-solution activity (or structure used for such activity):
Computer structures (Claim 4, 5, 10, 11, and 15)
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example,
Computer structures as indicated by Stefancik and Topgaard above
are all well-understood, routine, and conventional.
Taken alone or 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. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, data gathering, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements improves the functioning of a mobile device, output device, improves technology other than the technical field of the claimed invention, 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.
The claims are generally directed towards a method of analyzing brain activity. The method comprises receiving brain activity data of a subject, receiving information corresponding to the transition of a brain from one state to another, processing the information to obtain a transition value, and identifying a group of data that resembles the transition parameter and brain activity.
Claim(s) 1, 3-4, 5-11, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabesan (US Pat. No. 9302109) hereinafter Sabesan, and further in view of Low (US Pat. No. 11690557) hereinafter Low and Levendowski (US Pub. No. 20180333558) hereinafter Levendowski.
Regarding claim 1, Sabesan discloses A processing method for analyzing brain activity of a subject during a transition between brain states (abstract (sleep cycle information)) (Col. 5, lines 7-40 (sleep stage monitoring)), the method comprising:
receive, from a brain monitoring system for monitoring brain activity (Col. 4, lines 14-35 (EEG sensor)), brain activity data of the subject obtained during a transition of the subject from a first brain state to a second brain state of the subject, wherein at least one of the first brain state and/or the second brain state is a sleep state (Col. 5, lines 7-40, “During operation, when the patient 102 is asleep, the sensor data collection system 106 may collect the body parameter data from the EEG sensor 140…”) (Col. 7, lines 47-57, “the EEG sensor 140 may be placed on the head of the patient 102 to detect brain electrical activity of the patient 102. The IMD 104 and/or the sensor data collection system 106 may analyze the EEG data (e.g., the brain electrical activities the patient 102) to determine whether the patient 102 is in stage 2 sleep or has transitioned into stage 2 sleep…”) (Col. 8-9, lines 57-3, “when the patient 102 transitions from a sleep stage to wakefulness, such a transition may be detected based on an increase in heart rate relative to a heart rate of the patient 102 in the sleep stage via the ECG data, an increase in a frequency of the brain electrical activities relative to a frequency of the brain electrical activities of the patient 102 in the sleep stage via the EEG data, and an increase in body movement relative to the body movements of the patient 102 in the sleep stage via the accelerometer data, the EMG data, or a combination thereof. Sleep stage determination sensitivity and specificity may be increased by using multiple sensors. For example, the combination of ECG, EEG, and accelerometer may provide a more accurate indication of a current sleep stage that any one of those sensor types alone.”);
receive from a sensory and monitoring unit adapted to detect a transition of the subject from the first brain state to the second brain state of the subject, information corresponding to the detected transition (Col. 8-9, lines 57-3, “when the patient 102 transitions from a sleep stage to wakefulness, such a transition may be detected based on an increase in heart rate relative to a heart rate of the patient 102 in the sleep stage via the ECG data, an increase in a frequency of the brain electrical activities relative to a frequency of the brain electrical activities of the patient 102 in the sleep stage via the EEG data, and an increase in body movement relative to the body movements of the patient 102 in the sleep stage via the accelerometer data, the EMG data, or a combination thereof. Sleep stage determination sensitivity and specificity may be increased by using multiple sensors. For example, the combination of ECG, EEG, and accelerometer may provide a more accurate indication of a current sleep stage that any one of those sensor types alone.”) (Col. 7, lines 58-64, “ECG sensor 144 may be placed on the torso of the patient 102 (e.g., near the chest of the patient 102) to detect electrical activities of the heart of the patient 102. The IMD 104 and/or the sensor data collection system 106 may analyze the ECG data (e.g., the electrical activities of the heart of the patient 102) to determine whether the patient 102 is in stage 2 sleep or has transitioned into stage 2 sleep.”).
Sabesan fails to explicitly disclose process the brain activity data and the information from the sensory and monitoring unit to obtain a value for one or more transition parameters of the brain activity data, a transition parameter being a parameter representative of the transition, wherein the one or more transition parameters comprise at least one of: a transition timing, wherein a transition timing is a length of time between a time at which a transition is detected in the subject by the sensory and monitoring unit and a time at which changes in neuronal networks, identifiable in the subject's brain activity data, responsive to the change in sleep state are first detected a transition duration, wherein a transition duration is a duration from a moment at which a neuronal network associated with the first brain state of the subject starts to become weaker to a moment at which a neuronal network associated with the second brain state becomes fully established; a transition stability, wherein a transition stability is a number of transitions between neuronal networks in the brain activity data from a moment at which activity in neuronal networks associated with the first brain state starts to become weaker to a moment at which a network associated with the second brain state becomes fully established; and/or a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period.
However, Sabesan does disclose process the brain activity data and the information from the sensory and monitoring unit to obtain a value for one or more transition parameters of the brain activity data (Col. 8-9, lines 57-3, “when the patient 102 transitions from a sleep stage to wakefulness, such a transition may be detected based on an increase in heart rate relative to a heart rate of the patient 102 in the sleep stage via the ECG data, an increase in a frequency of the brain electrical activities relative to a frequency of the brain electrical activities of the patient 102 in the sleep stage via the EEG data, and an increase in body movement relative to the body movements of the patient 102 in the sleep stage via the accelerometer data, the EMG data, or a combination thereof. Sleep stage determination sensitivity and specificity may be increased by using multiple sensors. For example, the combination of ECG, EEG, and accelerometer may provide a more accurate indication of a current sleep stage that any one of those sensor types alone.”) (Col. 7-8, lines 47-4, “In addition or alternatively, the EEG sensor 140 may be placed on the head of the patient 102 to detect brain electrical activity of the patient 102…” “… consistent occurrences of the orderly ECG patterns may indicate that the patient 102 is in stage 2 sleep. Stage 1 sleep and stage 2 sleep are considered light sleep stages.”), wherein the one or more transition parameters comprise at least one of: a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period (Col. 7-8, lines 47-4, “In addition or alternatively, the EEG sensor 140 may be placed on the head of the patient 102 to detect brain electrical activity of the patient 102…” “… consistent occurrences of the orderly ECG patterns may indicate that the patient 102 is in stage 2 sleep. Stage 1 sleep and stage 2 sleep are considered light sleep stages.” (occurrence of a single transition)).
Low teaches process data to obtain a value for one or more transition parameters of the brain activity data, a transition parameter being a parameter representative of the transition (Col. 12 lines 42-53, “In any of the technologies described herein, any variety of statistics can be generated from adjusted source data. For example, sleep statistics can be generated from adjusted source EEG data that has been classified into sleep states. Exemplary sleep statistics can include information including sleep stage densities, number of sleep stage episodes, sleep stage average duration, cycle time, interval time between sleep stages, sleep stage separation statistics, onset of sleep, rapid eye movement sleep latency, regression coefficients of trends, measures of statistical significance of trends, and the like.”);
wherein the one or more transition parameters comprise at least one of: a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period (Col. 12, lines 42-53, “In any of the technologies described herein, any variety of statistics can be generated from adjusted source data. For example, sleep statistics can be generated from adjusted source EEG data that has been classified into sleep states. Exemplary sleep statistics can include information including sleep stage densities, number of sleep stage episodes, sleep stage average duration, cycle time, interval time between sleep stages, sleep stage separation statistics, onset of sleep, rapid eye movement sleep latency, regression coefficients of trends, measures of statistical significance of trends, and the like.”).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Sabesan with that of Low to include process the brain activity data and the information from the sensory and monitoring unit of Sabesan to obtain a value for one or more transition parameters of the brain activity data, a transition parameter being a parameter representative of the transition, wherein the one or more transition parameters comprise at least one of: a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period through the combination of references as differing sleep statistics are known (Low (Col. 12 lines 42-53)) and it would have yielded the predictable result of providing additional sleep data regarding the user.
Modified Sabesan fails to explicitly disclose process the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles, wherein the plurality of groups of subjects comprises at least a first group and a second group, wherein the first group comprises healthy subjects and the second group comprises subjects having a mental disorder.
However, Levendowski teaches process the data and corresponding values for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles (Par. 80, “The patient data store may store patient related data, e.g. a patient identifier and/or patient demographic information. Patient data store may also include information of related ailments, diseases, etc. indicative of the acute status of the patient…” “… The comparative patient data can be used, in part, to assess the sleep quality of a patient by providing a baseline of healthy and ill patients against which a user's data can be compared.” (healthy patients and those with chronic illness)), wherein the plurality of groups of subjects comprises at least a first group and a second group, wherein the first group comprises healthy subjects and the second group comprises subjects having a mental disorder (Par. 80, “The patient data store may store patient related data, e.g. a patient identifier and/or patient demographic information. Patient data store may also include information of related ailments, diseases, etc. indicative of the acute status of the patient…” “… The comparative patient data can be used, in part, to assess the sleep quality of a patient by providing a baseline of healthy and ill patients against which a user's data can be compared.” (healthy patients and those with chronic illness)) (Par. 163, “In alternative embodiments, alone or in combination, the detection of sleep stages can be performed using more sophisticated linear or non-linear mathematical models (e.g., discriminant function, neural network, etc.) with variables that can be obtained from the EEG, EOG and ECG signals…” “… N1, N2, N3 (SWS) and REM states in the delta, theta and alpha ranges can be used to identify abnormal characteristics associated with abnormal sleep characteristics.”).
Sabesan, Low, and Levendowski are considered to be analogous art to the claimed invention as they are involved with sleep measurements.
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Sabesan and Low with that of Levendowski to include process the values of the one or more transition parameters of the brain activity data of Sabesan and Low and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles, wherein the plurality of groups of subjects comprises at least a first group and a second group, wherein the first group comprises healthy subjects and the second group comprises subjects having a mental disorder through the combination of references as it would have yielded the predictable result of monitoring disease in a user (Levendowski (Par. 80)).
Regarding claim 3, modified Sabesan fails to explicitly disclose the limitation of the claim.
However, Levendowski further teaches wherein the step of processing the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects further uses one or more characteristics of the subject to identify which of the plurality of groups the subject most closely resembles (Levendowski (Par. 80, “The patient data store may store patient related data, e.g. a patient identifier and/or patient demographic information. Patient data store may also include information of related ailments, diseases, etc. indicative of the acute status of the patient…” “… The comparative patient data can be used, in part, to assess the sleep quality of a patient by providing a baseline of healthy and ill patients against which a user's data can be compared.” (healthy patients and those with chronic illness)) (Par. 163, “In alternative embodiments, alone or in combination, the detection of sleep stages can be performed using more sophisticated linear or non-linear mathematical models (e.g., discriminant function, neural network, etc.) with variables that can be obtained from the EEG, EOG and ECG signals…” “… N1, N2, N3 (SWS) and REM states in the delta, theta and alpha ranges can be used to identify abnormal characteristics associated with abnormal sleep characteristics.”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Sabesan, Low, and Levendowski with that of Levendowski to include wherein the step of processing the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects further uses one or more characteristics of the subject to identify which of the plurality of groups the subject most closely resembles for the reasoning as indicated in claim 1 above.
Regarding claim 4, modified Sabesan fails to explicitly disclose the limitation of the claim.
However, Levendowski further teaches wherein the step of processing the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles comprises (As indicated in claim 1 above):
inputting the brain activity data and/or the values of the one or more transition parameters into an artificial neural network (Levendowski (Par. 185, “machine learning techniques may be utilized to employ the remaining steps of FIG. 15 in order to differentiate ASWA from other conditions, e.g., ASWA vs. healthy slow wave activity, healthy awake, FIRDA, ocular activity, or artifact, etc. For example, the acquired physiological signal data can be downloaded to an external computer system 390 comprising machine learning software executed by a processor for processing and identification of the ASWA...”) (Par. 186, “It will be appreciated that machine learning techniques can be utilized to detect any of the abnormal physiological signals patterns described throughout this disclosure…”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Sabesan, Low, and Levendowski with that of Levendowski to include wherein the step of processing the values of the one or more transition parameters of the brain activity data of Sabesan and Low and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles comprises: inputting the brain activity data of Sabesan and Low and/or the values of the one or more transition parameters into an artificial neural network through the combination of references as machine learning techniques are known in the art (Levendowski (Par. 186)) and it would have yielded the predictable result of improving the recognition capabilities and accuracy.
Regarding claim 6, modified Sabesan further discloses wherein the transition is one of:
a transition from a wakeful state to a sleep state;
a transition from a sleep state to a wakeful state; or
a transition from a first sleep state to a second, different sleep state (Sabesan (Col. 5, lines 7-40, “… the sleep cycle information to detect a sleep stage transition. For example, the sleep stage transition may include a transition from stage 1 sleep to stage 2 sleep, a transition from stage 2 sleep to stage 3 sleep, a transition from stage 3 sleep to REM stage sleep, a transition from REM stage sleep to stage 1 sleep, a transition from one of stage 1 sleep, stage 2 sleep, stage 3 sleep, and/or REM stage sleep to wakefulness, or a combination thereof.”)).
Regarding claim 7, modified Sabesan further discloses wherein the one or more transition parameters further comprise the one or more networks active during the transition (Sabesan (Col. 7-8, lines 47-4, “In addition or alternatively, the EEG sensor 140 may be placed on the head of the patient 102 to detect brain electrical activity of the patient 102…” “… consistent occurrences of the orderly ECG patterns may indicate that the patient 102 is in stage 2 sleep. Stage 1 sleep and stage 2 sleep are considered light sleep stages.” (occurrence of a single transition)) (Col. 8, lines 19-30))).
Regarding claim 9, modified Sabesan fails to explicitly disclose the limitations of the claim.
However, Sabesan does further disclose continue receiving brain activity data of the subject until a predefined number of transitions have been recorded (Sabesan (Col. 6, lines 33-51 (continuously monitor)) (Col. 2, lines 1-18 (monitor transitions)) (Col. 7-8, lines 47-4, “In addition or alternatively, the EEG sensor 140 may be placed on the head of the patient 102 to detect brain electrical activity of the patient 102…” “… consistent occurrences of the orderly ECG patterns may indicate that the patient 102 is in stage 2 sleep. Stage 1 sleep and stage 2 sleep are considered light sleep stages.” (occurrence of transition))).
Low further teaches obtain a value for one or more transition parameters of the brain activity data for each detected transition. (Col. 12 lines 42-53, “In any of the technologies described herein, any variety of statistics can be generated from adjusted source data. For example, sleep statistics can be generated from adjusted source EEG data that has been classified into sleep states. Exemplary sleep statistics can include information including sleep stage densities, number of sleep stage episodes, sleep stage average duration, cycle time, interval time between sleep stages, sleep stage separation statistics, onset of sleep, rapid eye movement sleep latency, regression coefficients of trends, measures of statistical significance of trends, and the like.”).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Sabesan, Low, and Levendowski with that of Sabesan and Low to include continue receiving brain activity data of the subject until a predefined number of transitions have been recorded, and to obtain a value for one or more transition parameters of the brain activity data of Sabesan and Low for each detected transition through the combination of references as Sabesan discloses monitoring continuously (as indicated above) and as such is highly capable of monitoring for a predefined number of transitions and as differing sleep statistics are known (Low (Col. 12 lines 42-53)) it would have yielded the predictable result of providing additional sleep data regarding the user.
Regarding claim 10, modified Sabesan further discloses A system comprising: a sensory and monitoring unit adapted to detect a transition of the subject from a first brain state to a second brain state of the subject, wherein at least one of the first brain state and/or the second brain state is a sleep state (as indicated above in claim 1); and
a processor (Col. 16, lines 25-38) configured to perform the processing system processing method of claim 1 (the method of claim 1 above as taught by Sabesan, Low, and Levendowski).
Regarding claim 11, modified Sabesan further discloses wherein the sensory and monitoring unit is adapted to detect a transition based on at least one of: brain activity information, cardiorespiratory information (Col. 8-9, lines 57-3, “when the patient 102 transitions from a sleep stage to wakefulness, such a transition may be detected based on an increase in heart rate relative to a heart rate of the patient 102 in the sleep stage via the ECG data, an increase in a frequency of the brain electrical activities relative to a frequency of the brain electrical activities of the patient 102 in the sleep stage via the EEG data, and an increase in body movement relative to the body movements of the patient 102 in the sleep stage via the accelerometer data, the EMG data, or a combination thereof. Sleep stage determination sensitivity and specificity may be increased by using multiple sensors. For example, the combination of ECG, EEG, and accelerometer may provide a more accurate indication of a current sleep stage that any one of those sensor types alone.”) (Col. 7, lines 58-64, “ECG sensor 144 may be placed on the torso of the patient 102 (e.g., near the chest of the patient 102) to detect electrical activities of the heart of the patient 102. The IMD 104 and/or the sensor data collection system 106 may analyze the ECG data (e.g., the electrical activities of the heart of the patient 102) to determine whether the patient 102 is in stage 2 sleep or has transitioned into stage 2 sleep.”), cardioballistography information, respiration rate, behavioral information and/or information corresponding to the subject's performance on a repetitive task.
Regarding claim 14, Sabesan discloses A computer-implemented method for analyzing the brain activity of a subject during a transition between brain states (abstract (sleep cycle information)) (Col. 5, lines 7-40 (sleep stage monitoring))(Col. 16-17, lines 39-30 (computer implementation)), the computer-implemented method comprising:
receiving, from a brain monitoring system for monitoring brain activity (Col. 4, lines 14-35 (EEG sensor)), brain activity data of the subject obtained during a transition of the subject from a first brain state to a second brain state of the subject, wherein at least one of the first brain state and/or the second brain state is a sleep state Col. 5, lines 7-40, “During operation, when the patient 102 is asleep, the sensor data collection system 106 may collect the body parameter data from the EEG sensor 140…”) (Col. 7, lines 47-57, “the EEG sensor 140 may be placed on the head of the patient 102 to detect brain electrical activity of the patient 102. The IMD 104 and/or the sensor data collection system 106 may analyze the EEG data (e.g., the brain electrical activities the patient 102) to determine whether the patient 102 is in stage 2 sleep or has transitioned into stage 2 sleep…”) (Col. 8-9, lines 57-3, “when the patient 102 transitions from a sleep stage to wakefulness, such a transition may be detected based on an increase in heart rate relative to a heart rate of the patient 102 in the sleep stage via the ECG data, an increase in a frequency of the brain electrical activities relative to a frequency of the brain electrical activities of the patient 102 in the sleep stage via the EEG data, and an increase in body movement relative to the body movements of the patient 102 in the sleep stage via the accelerometer data, the EMG data, or a combination thereof. Sleep stage determination sensitivity and specificity may be increased by using multiple sensors. For example, the combination of ECG, EEG, and accelerometer may provide a more accurate indication of a current sleep stage that any one of those sensor types alone.”);
receiving, from a sensory and monitoring unit adapted to detect a transition of the subject from the first brain state to the second brain state of the subject, information corresponding to the detected transition (Col. 8-9, lines 57-3, “when the patient 102 transitions from a sleep stage to wakefulness, such a transition may be detected based on an increase in heart rate relative to a heart rate of the patient 102 in the sleep stage via the ECG data, an increase in a frequency of the brain electrical activities relative to a frequency of the brain electrical activities of the patient 102 in the sleep stage via the EEG data, and an increase in body movement relative to the body movements of the patient 102 in the sleep stage via the accelerometer data, the EMG data, or a combination thereof. Sleep stage determination sensitivity and specificity may be increased by using multiple sensors. For example, the combination of ECG, EEG, and accelerometer may provide a more accurate indication of a current sleep stage that any one of those sensor types alone.”) (Col. 7, lines 58-64, “ECG sensor 144 may be placed on the torso of the patient 102 (e.g., near the chest of the patient 102) to detect electrical activities of the heart of the patient 102. The IMD 104 and/or the sensor data collection system 106 may analyze the ECG data (e.g., the electrical activities of the heart of the patient 102) to determine whether the patient 102 is in stage 2 sleep or has transitioned into stage 2 sleep.”).
Sabesan fails to explicitly disclose processing the brain activity data and the information from the sensory and monitoring unit to obtain a value for one or more transition parameters of the brain activity data, a transition parameter being a parameter representative of the transition, wherein the one or more transition parameters comprise at least one of: a transition timing, wherein a transition timing is a length of time between a time at which a transition is detected in the subject and a time at which changes in neuronal networks, identifiable in the subject's brain activity data, responsive to the change in sleep state are first detected; a transition duration, wherein a transition duration is a duration from a moment at which a neuronal network associated with the first brain state of the subject starts to become weaker to a moment at which a neuronal network associated with the second brain state becomes fully established; a transition stability, wherein a transition stability is a number of transitions between neuronal networks in the brain activity data from a moment at which activity in neuronal networks associated with the first brain state starts to become weaker to a moment at which a network associated with the second brain state becomes fully established; and/or a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period.
However, Sabesan does disclose processing the brain activity data and the information from the sensory and monitoring unit to obtain a value for one or more transition parameters of the brain activity data (Col. 8-9, lines 57-3, “when the patient 102 transitions from a sleep stage to wakefulness, such a transition may be detected based on an increase in heart rate relative to a heart rate of the patient 102 in the sleep stage via the ECG data, an increase in a frequency of the brain electrical activities relative to a frequency of the brain electrical activities of the patient 102 in the sleep stage via the EEG data, and an increase in body movement relative to the body movements of the patient 102 in the sleep stage via the accelerometer data, the EMG data, or a combination thereof. Sleep stage determination sensitivity and specificity may be increased by using multiple sensors. For example, the combination of ECG, EEG, and accelerometer may provide a more accurate indication of a current sleep stage that any one of those sensor types alone.”) (Col. 7-8, lines 47-4, “In addition or alternatively, the EEG sensor 140 may be placed on the head of the patient 102 to detect brain electrical activity of the patient 102…” “… consistent occurrences of the orderly ECG patterns may indicate that the patient 102 is in stage 2 sleep. Stage 1 sleep and stage 2 sleep are considered light sleep stages.”), wherein the one or more transition parameters comprise at least one of: a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period (Col. 7-8, lines 47-4, “In addition or alternatively, the EEG sensor 140 may be placed on the head of the patient 102 to detect brain electrical activity of the patient 102…” “… consistent occurrences of the orderly ECG patterns may indicate that the patient 102 is in stage 2 sleep. Stage 1 sleep and stage 2 sleep are considered light sleep stages.” (occurrence of a single transition)).
Low teaches processing the data to obtain a value for one or more transition parameters of the brain activity data, a transition parameter being a parameter representative of the transition (Col. 12 lines 42-53, “In any of the technologies described herein, any variety of statistics can be generated from adjusted source data. For example, sleep statistics can be generated from adjusted source EEG data that has been classified into sleep states. Exemplary sleep statistics can include information including sleep stage densities, number of sleep stage episodes, sleep stage average duration, cycle time, interval time between sleep stages, sleep stage separation statistics, onset of sleep, rapid eye movement sleep latency, regression coefficients of trends, measures of statistical significance of trends, and the like.”),
wherein the one or more transition parameters comprise at least one of: a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period (Col. 12, lines 42-53, “In any of the technologies described herein, any variety of statistics can be generated from adjusted source data. For example, sleep statistics can be generated from adjusted source EEG data that has been classified into sleep states. Exemplary sleep statistics can include information including sleep stage densities, number of sleep stage episodes, sleep stage average duration, cycle time, interval time between sleep stages, sleep stage separation statistics, onset of sleep, rapid eye movement sleep latency, regression coefficients of trends, measures of statistical significance of trends, and the like.”).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Sabesan with that of Low to include processing the brain activity data and the information from the sensory and monitoring unit of Sabesan to obtain a value for one or more transition parameters of the brain activity data, a transition parameter being a parameter representative of the transition, wherein the one or more transition parameters comprise at least one of: a frequency of transition, wherein a frequency of transition is a measure of the number of times a transition from the first brain state to the second brain state occurs in a set time period through the combination of references as differing sleep statistics are known (Low (Col. 12 lines 42-53)) and it would have yielded the predictable result of providing additional sleep data regarding the user.
Modified Sabesan fails to explicitly disclose processing the values of the one or more transition parameters of the brain activity data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles, wherein the plurality of groups of subjects comprises at least a first group and a second group, wherein the first group comprises healthy subjects and the second group comprises subjects having a mental disorder.
However, Levendowski teaches processing the data and corresponding values of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles (Par. 80, “The patient data store may store patient related data, e.g. a patient identifier and/or patient demographic information. Patient data store may also include information of related ailments, diseases, etc. indicative of the acute status of the patient…” “… The comparative patient data can be used, in part, to assess the sleep quality of a patient by providing a baseline of healthy and ill patients against which a user's data can be compared.” (healthy patients and those with chronic illness)), wherein the plurality of groups of subjects comprises at least a first group and a second group, wherein the first group comprises healthy subjects and the second group comprises subjects having a mental disorder (Par. 80, “The patient data store may store patient related data, e.g. a patient identifier and/or patient demographic information. Patient data store may also include information of related ailments, diseases, etc. indicative of the acute status of the patient…” “… The comparative patient data can be used, in part, to assess the sleep quality of a patient by providing a baseline of healthy and ill patients against which a user's data can be compared.” (healthy patients and those with chronic illness)) (Par. 163, “In alternative embodiments, alone or in combination, the detection of sleep stages can be performed using more sophisticated linear or non-linear mathematical models (e.g., discriminant function, neural network, etc.) with variables that can be obtained from the EEG, EOG and ECG signals…” “… N1, N2, N3 (SWS) and REM states in the delta, theta and alpha ranges can be used to identify abnormal characteristics associated with abnormal sleep characteristics.”).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Sabesan and Low with that of Levendowski to include processing the values of the one or more transition parameters of the brain activity data of Sabesan and Low and corresponding values of Sabesan and Low of the one or more transition parameters for a plurality of groups of subjects to identify which of the plurality of groups the subject most closely resembles, wherein the plurality of groups of subjects comprises at least a first group and a second group, wherein the first group comprises healthy subjects and the second group comprises subjects having a mental disorder through the combination of references as it would have yielded the predictable result of monitoring disease in a user (Levendowski (Par. 80)).
Regarding claim 15, modified Sabesan further discloses A computer program product comprising computer program code including executable instructions stored on a non-transitory computer readable medium (Sabesan (Col. 16-17, lines 39-30 (computer implementation))) which, when executed on a computing device having a processing system (Sabesan (Col. 16-17, lines 39-30 (computer implementation))), cause the processing system to perform all of the steps of the method according to claim 14 (Sabesan (abstract (sleep cycle information)) (Col. 5, lines 7-40 (sleep stage monitoring)) (Col. 16-17, lines 39-30 (computer implementation)))(The method as indicated in claim 14 above).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabesan in view of Low and Levendowski as applied to claim 4 above, and further in view of Yang (US Pub. No. 20210225510) hereinafter Yang.
Sabesan, Low, and Levendowski teach the method of claim 4 above.
Regarding claim 5, modified Sabesan fails to explicitly disclose the limitations of the claim.
However, Yang teaches wherein the artificial neural network has been trained using a training algorithm configured to receive an array of training inputs and known outputs, wherein the training inputs comprise activity data and/or values of one or more transition parameters during transitions from a first brain state to a second brain state, and the known outputs comprise a determination of which of a plurality of groups of subjects the activity data belongs to (Par. 44, “In the present application, the data trained by the artificial intelligence learning model will be used as the physiological evaluation index, and can be used to identify whether an individual is in a sick state or a normal state…”) (Par. 54, “The intermediate layer dimension reduction output data obtained when the sleep data y with a disease tag is sent into the autoencoder network will be further used as an input of an SVM classifier to train the classifier model, such that the classifier model can identify disease types corresponding to different data. The classifier model obtained by training can identify the specific type of disease of the individual generating the sleep data.”) (Par. 42).
Low does teach training classifiers (Low (Col. 22, lines 2-42)).
Sabesan, Low, Levendowski, and Yang are considered to be analogous art to the claimed invention as they are involved with sleep measurements.
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Sabesan, Low, and Levendowski with that of Yang to include wherein the artificial neural network of Levendowski has been trained using a training algorithm configured to receive an array of training inputs and known outputs, wherein the training inputs comprise brain activity data of Sabesan and Low and/or values of one or more transition parameters during transitions from a first brain state to a second brain state of Sabesan and Low, and the known outputs comprise a determination of which of a plurality of groups of subjects the brain activity data belongs to through the combination of references and applying the model training of Yang to the model of Levendowski as it would have yielded the predictable result of improving the model accuracy (Yang (Par. 38)) and allow for automatic prediction (Low (Col. 22, lines 2-9)).
Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabesan in view of Low and Levendowski as applied to claim 10 above, and further in view of Liu (US Pub. No. 20230414007) hereinafter Liu.
Sabesan, Low, and Levendowski teach the system of claim 10 above.
Regarding claim 12, modified Sabesan fails to explicitly disclose the limitations of the claim.
However, Sabesan does teach wherein the system further comprises a sleep regulatory unit adapted to induce a change in brain state of the subject (Sabesan (Col. 3, lines 50-61 (therapy delivery unit)) (Col. 5-6 (lines 59-22 (stimulation to change state)))) (Examiner's Note: Sabesan fails to explicitly disclose that the sleep regulatory unit is a balloon) (Examiner's Note: Interpreted under 112f as indicated above).
However, Liu teaches a sleep regulatory unit adapted to induce a change in brain state of the subject (Par. 54, “The airbag assembly 2 in the present embodiment includes several airbags with controllable air volume and inflation sequence, and may support the back, waist, legs and feet of the human body in varying actions, and through various changing actions of the supporting positions of the airbags, the purpose of sleep aid, wake-up, relaxing and massage is achieved. The setting of heights of the several airbags of the airbag assembly 2 is suitably matched with the normal physiological curve structure of the human body, and is freely adjusted and set according to user requirements. A heating assembly 5 can be arranged on the airbag of the airbag assembly 2 to improve the user's experience” (the airbag assembly is capable of the indicated function)).
Sabesan, Low, Levendowski, and Liu are considered to be analogous art to the claimed invention as they are involved with sleep measurements.
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Sabesan, Low, and Levendowski with that of Liu to include wherein the system further comprises a sleep regulatory unit adapted to induce a change in brain state of the subject through the combination of references as it would have yielded the same or similar results of the electrical stimulation of Sabesan of changing the sleep state of the user.
Regarding claim 13, modified Sabesan fails to explicitly disclose the limitations of the claim.
However, Sabesan does teach wherein the sleep regulatory unit is adapted to alternately induce sleep in the subject and wake the subject from sleep for a predetermined number of wake/sleep cycles (Sabesan (Col. 3, lines 50-61 (therapy delivery unit)) (Col. 5-6 (lines 59-22 (stimulation to change state)))) (Examiner's Note: Sabesan fails to explicitly disclose that the sleep regulatory unit is a balloon) (Examiner's Note: Interpreted under 112f as indicated above).
However, Liu teaches wherein the sleep regulatory unit is adapted to alternately induce sleep in the subject and wake the subject from sleep for a predetermined number of wake/sleep cycles (Par. 54, “The airbag assembly 2 in the present embodiment includes several airbags with controllable air volume and inflation sequence, and may support the back, waist, legs and feet of the human body in varying actions, and through various changing actions of the supporting positions of the airbags, the purpose of sleep aid, wake-up, relaxing and massage is achieved. The setting of heights of the several airbags of the airbag assembly 2 is suitably matched with the normal physiological curve structure of the human body, and is freely adjusted and set according to user requirements. A heating assembly 5 can be arranged on the airbag of the airbag assembly 2 to improve the user's experience” (the airbag assembly is capable of the indicated function)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Sabesan, Low, Levendowski, and Liu with that of Liu to include wherein the sleep regulatory unit is adapted to alternately induce sleep in the subject and wake the subject from sleep for a predetermined number of wake/sleep cycles through the combination of references for the reasoning as indicated in claim 12 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Raniere (US Pat. No. 7041049) hereinafter Raniere.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARI SINGH KANE PADDA whose telephone number is (571)272-7228. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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/ARI S PADDA/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791