DETAILED ACTION
Claims 20-44 are the current claims hereby under examination.
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 .
Election/Restrictions
Applicant’s election without traverse of group IV claims 20-25 in the reply filed on 08/20/2026 is acknowledged.
Claims 1-19 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 08/20/2026.
Claims 1-19 are cancelled.
New claims 26-44 have been added.
Claims 20-44 are hereby the present claims under consideration.
Claim Rejections - 35 USC § 112
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.
Claim 44 is 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 44 recites “identifying one or more changes in a lifestyle of the person relative to the one or more baseline behavior patterns” but it is unclear what metric, value, or other parameters is being compared to the baseline behavior patterns to identify changes in a lifestyle. It is further unclear what differences qualify as a change in lifestyle. It is unclear if any single difference qualifies as a change in lifestyle or if a change in lifestyle requires a set number, duration, or degree of difference to be detected. For the purposes of this examination, the limitation is interpreted as the current behavior patterns being compared to the baseline behavior patterns and a change in lifestyle being indicated by any difference between the two behavior patterns.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 20-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 20-44 are directed to a system and method of classifying sounds as behaviors and detecting changes in behavior over time using a computational algorithm, which is an abstract idea. Claims 20-44 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 20 is as follows:
Step 1: Claim 20 is drawn to a machine.
Step 2A — Prong One: Claim 20 recites an abstract idea. In particular, claim 20 recites the following limitations:
[A1] determine, based on the body noises detected and the external acoustic sound signals detected, a first plurality of activity classifications of activities of the person over the first period of time and a second plurality of activity classifications of activities of the person over the second period of time
[B1] analyze the first plurality of activity classifications to generate one or more baseline behavior patterns for the person
[C1] analyze the second plurality of activity classifications to generate one or more current behavior patterns for the person
[D1] analyze the one or more current behavior patterns relative to the one or more baseline behavior patterns to detect one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns
[E1] initiate or elicit a remedial action with respect to the person in response to detecting the one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns
These elements [A1]-[E1] of claim 20 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper. In particular, the claim is drawn towards the abstract idea of a clinician reviewing sound data to determine what a patient is doing, comparing their current activity to their normal activity, and providing any form of alert or action such as speaking to the user in response to determining they are doing something outside of their normal routine.
Step 2A — Prong Two: Claim 20 recites the following limitations that are beyond the judicial exception:
[A2] a first sensor configured to be surgically implanted in a person, wherein the first sensor is configured to detect body noises of the person over a first period of time and a second period of time
[B2] a second sensor configured to be surgically implanted in the person, wherein the second sensor is configured to detect external acoustic sound signals that are simultaneously received with one or more of the body noises of the first sensor over the first period of time and the second period of time
[C2] an activity classifier
[D2] a logging and analytics module
These elements [A2]-[D2] of claim 20 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[B2] are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). Furthermore, the elements [C2] and [D2] are merely instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Additionally, the element [C2] is nothing more than the computer implementation/automation of an abstract mental process of determining the activity of a patient, which is what the physician does in the above abstract idea.
Step 2B: Claim 20 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitations of [A2] and [B2] do not qualify as significantly more because, the collection of sound signals using these sensors is merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, the first and second sensors are nothing more than audio sensors detecting soundwaves from within and outside of the body. Such generic sensors are well known in the art as evidenced by Applicant’s lack of a particular description as to the structure and/or operation of the microphones in the specification and further evidenced by:
US Patent Application Publication Number US 20150157853 A1 (Verzal) discloses that implantable microphones are typical in paragraph 0057.
US Patent Application Publication Number US 20110178438 A1 (Gerwen) discloses an implantable microphone for use with conventional hearing prosthesis systems (Paragraph 0006).
US Patent Application Publication Number US 20100069768 A1 (Min) discloses that microphones are conventional and may be used in implantable devices in paragraph 0060, Abstract.
US Patent Application Publication Number US 20090227886 A1 (Baur) discloses that acoustic sensors are conventional and may be used in implantable devices in paragraphs 0026 and 0048.
Further, the elements [C2]-[D2] do not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)). In particular, the elements require nothing more than the abstract idea to be implemented onto a computer having a processor for carrying out the recited functions.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above -judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 21-31 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 22: one or more auxiliary devices
Claim 25: The first and second sensors generate electrical signals representing the sensed signals over time; and a body noises processor
Claim 26: one or more auxiliary devices comprise one or more of: a temperature tracker, a heartrate monitor, a blood pressure sensor, and a body-worn fitness tracker
Claims 28-29: the first and second sensors comprising microphones
Claims 30-31: the classifier being a machine learning model or being trained with labelled samples
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claims 22, 25-26, and 28-29 are each merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
Also, each of these limitations does not recite additional elements that amount to significantly more than the judicial exception itself because they are merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, In particular, the limitations of 25 and 28-29 drawn towards first and second sensors being microphones and producing electric signals in response to sound do not qualify and significantly more because the first and second sensors are nothing more than microphones for detecting soundwaves from inside and outside the body which are routine and conventional as evidenced by Applicant’s lack of a particular description regarding their structure and/or function and further evidenced by Verzal, Gerwen, Min, and Baur as presented above. The generation of electrical signals in response to soundwaves is implicitly disclosed by their function. Additionally, the auxiliary sensors of claims 22 and 26 are each well-known, routine, and conventional as evidenced by Applicant’s alack of a specific description regarding the structure and/or operation of each of the different sensors and further evidenced by:
U.S. Patent Application Publication No. US 2019/0000375 A1 (Philips) discloses that body movements are conventionally recorded with an accelerometer implemented onto an actigraphy device (paragraph 0041 of Philips);
U.S. Patent Application Publication No. US 2017/0099711 A1 (Polley) discloses that conventional PPG sensors include the well-known pulse oximeter (paragraph 0005-0006 of Polley);
U.S. Patent Application Publication No. US 20080139955 A1 (Hansmann) discloses that temperature sensors are conventional (paragraph 0059 of Hansmann);
U.S. Patent Application Publication No. US 20050148883 A1 (Boesen) discloses that temperature sensors and heart rate sensors are conventional (paragraph 0030 of Boesen);
U.S. Patent Application Publication No. US 20110319769 A1 (Hedberg) discloses that blood pressure sensors such as pulse oximeters are typical (paragraph 0073 of Hedberg);
Also, the limitations from claims 25 and 30-31 drawn towards a body noises processor, a machine learning model, and the classifier being trained using labeled data are each simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions (that is, one of storage and processing) that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)). The claimed processor is a generic computer processor and the limitations drawn towards a machine learning model and the classifier being trained are merely recitations drawn towards a computer implementation of the decision making and judgment performed by a clinician carrying out the abstract idea. The limitations drawn towards training are merely a computer implementation of the experience and/or training of a trained clinician.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above -judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
The analysis of claim 32 is as follows:
Step 1: Claim 32 is drawn to a process.
Step 2A — Prong One: Claim 32 recites an abstract idea. In particular, claim 32 recites the following limitations:
[A1] determining, based on the body noises and the external acoustic sound signals, a first plurality of activity classifications of activities of the person over the first period of time and a second plurality of activity classifications of activities of the person over the second period of time
[B1] analyzing the first plurality of activity classifications to generate one or more baseline behavior patterns for the person
[C1] analyzing the second plurality of activity classifications to generate one or more current behavior patterns for the person
[D1] analyzing the one or more current behavior patterns relative to the one or more baseline behavior patterns to detect one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns
[E1] initiating or eliciting a remedial action with respect to the person in response to detecting the one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns
These elements [A1]-[E1] of claim 32 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper. In particular, the claim is drawn towards the abstract idea of a clinician reviewing sound data to determine what a patient is doing, comparing their current activity to their normal activity, and providing any form of alert or action such as speaking to the user in response to determining they are doing something outside of their normal routine.
Step 2A — Prong Two: Claim 32 recites the following limitations that are beyond the judicial exception:
[A2] detecting body noises of a person over a first period of time and a second period of time
[B2] detecting external acoustic sound signals that are simultaneously received with one or more of the body noises over the first period of time and the second period of time
[C2] an activity classifier
These elements [A2]-[C2] of claim 32 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[B2] are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). Furthermore, the element [C2] is merely instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Additionally, the element [C2] is nothing more than the computer implementation/automation of an abstract mental process of determining the activity of a patient, which is what the physician does in the above abstract idea.
Step 2B: Claim 32 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitations of [A2] and [B2] do not qualify as significantly more because, the collection of sound signals merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, no particular structure is claimed for gathering the detecting the sound signals.
Further, the element [C2] does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)). In particular, the elements require nothing more than the abstract idea to be implemented onto a computer having a processor for carrying out the recited functions.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above -judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 33-44 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 33: one or more auxiliary devices
Claim 34: one or more auxiliary devices comprise one or more of: a temperature tracker, a heartrate monitor, a blood pressure sensor, and a body-worn fitness tracker
Claim 40: The body noises are detected using a sensor configured to be implanted in the person
Claims 41: the sensor comprising a microphone
Claims 42-43: the classifier being a machine learning model or being trained with labelled samples
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claims 33-34 and 40-41 are each merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
Also, each of these limitations does not recite additional elements that amount to significantly more than the judicial exception itself because they are merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, In particular, the limitations of claims 33-34 and 40-41 are drawn towards the use of sensors that are well-known, routine, and/or conventional as described with above with respect to claims 20-31 and evidenced by Applicants lack of a particular description regarding the structure and/or operation of each of the claimed sensors and further evidenced by Verzal, Gerwen, Min, Baur, Philips, Polley, Hansmann, Boeson, and Hedberg as presented above.
Also, the limitations from claims 42-43 drawn towards a machine learning model, and the classifier being trained using labeled data are each simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions (that is, one of storage and processing) that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)). The claimed limitations drawn towards a machine learning model and the classifier being trained are merely recitations drawn towards a computer implementation of the decision making and judgment performed by a clinician carrying out the abstract idea. The limitations drawn towards training are merely a computer implementation of the experience and/or training of a trained clinician.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above -judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 20-44 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shalon US Patent Application Publication Number US 2006/0064037 A1 hereinafter Shalon.
Regarding claim 20, Shalon discloses a system (Abstract), comprising:
a first sensor configured to be surgically implanted in a person, wherein the first sensor is configured to detect body noises of the person (Paragraphs 0116-0117: the microphones for picking up internal sounds which may be implanted in the user) over a first period of time and a second period of time (Paragraphs 0047, 0152-0153, and 0212: a baseline behavior signature is generated over a period of time, or a first time period; The database of activity signatures over a time period used to establish norms, averages, and/or trends; Paragraphs 0147 and 0260-0262: the system can gather data and detect current events, or a second time period);
a second sensor configured to be surgically implanted in the person, wherein the second sensor is configured to detect external acoustic sound signals that are simultaneously received with one or more of the body noises of the first sensor (Paragraphs 0116-0117: the microphones for picking up external sounds which may be implanted in the user; Paragraph 0147: the system continuously collects data from the one or more sensors; Paragraph 0317: ambient sounds can be used to detect a variety of activities and/or situations) over the first period of time and the second period of time (Paragraphs 0047 and 0152-0153: a baseline behavior signature is generated over a period of time, or a first time period; The database of activity signatures over a time period used to establish norms, averages, and/or trends; Paragraph 0147, 0260-0262, and 0317: the system can gather data and detect current events, or a second time period);
an activity classifier (Paragraphs 0037, 0240, and 0256: the classifications performed by the classifier) configured to determine, based on the body noises detected via the first sensor and the external acoustic sound signals detected via the second sensor (Paragraphs 0260-0262, 0233, and 0317: the activity classification uses internal and ambient sounds; Paragraphs 0104-0107: the classification, or detection, of any type of activity using the input parameters such as urination using internal and/or external body sounds as described in paragraph 0125), a first plurality of activity classifications of activities of the person over the first period of time (Paragraphs 0047, 0152-0153, and 0265: a baseline behavior signature is generated over a period of time, or a first time period; The database of activity signatures over a time period used to establish norms, averages, and/or trends) and a second plurality of activity classifications of activities of the person over the second period of time (Paragraphs 0147 and 0260-0262: the system can gather data and detect current events, or a second time period); and
a logging and analytics module (Paragraph 0251: the signal processor and memory) configured to:
analyze the first plurality of activity classifications to generate one or more baseline behavior patterns for the person (Paragraphs 0047, 0152-0153, 0212, and 0265: the learning of norms and patterns);
analyze the second plurality of activity classifications to generate one or more current behavior patterns for the person (Paragraphs 0260-0262, 0266, and 0317: the current activity identified; Paragraph 0152: the detection of changes indicates that current activity is detected; Paragraph 0147: the detection of an eating event; Paragraphs 0104-0107: the identification of any current activity);
analyze the one or more current behavior patterns relative to the one or more baseline behavior patterns to detect one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns (Paragraph 0152: the detection of changes in the user’s eating patterns; Paragraph 0355: the detection of variances in eating or activity patterns); and
initiate or elicit a remedial action with respect to the person in response to detecting the one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns (Paragraphs 0111-0112: the monitoring and modification of behavior through feedback based on identified activity; Paragraph 0152: when irregular eating patterns are detected the system may send an alert to the user or a third party; Paragraph 0163: audio or visual feedback; Paragraph 0356: the feedback provided based on deviations from activity patterns).
Regarding claim 21, Shalon discloses the system of claim 20. Shalon further discloses the system wherein the logging and analytics module is configured to: receive a plurality of auxiliary health inputs for the person from one or more auxiliary devices (Paragraphs 0104-0106 and 0109: the various inputs used to detect activities; Paragraphs 0299-0300: the system can measure a plurality of other parameters such as temperature, blood pressure, and body motion); and, generate the one or more baseline behavior patterns for the person are generated based on the plurality of auxiliary health inputs and the first plurality of activity classifications (Paragraphs 0152-0153: the generation of norms and patterns for a subject)
Regarding claim 22, Shalon discloses the system of claim 20. Shalon further discloses the system wherein in response to detecting the one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns, the logging and analytics module is configured to generate one or more messages configured to initiate or elicit the remedial action (Paragraphs 0111-0112, 0152, 0153, and 0356: the feedback provided to the user; Paragraphs 0160 and 0164: the feedback may be messages).
Regarding claim 23, Shalon discloses the system of claim 20. Shalon further discloses the system wherein the logging and analytics module is configured to log the first plurality of activity classifications for the person (Paragraphs 0152-0153: the databases, or logs, of activities and measured data; Paragraph 0221: eating logs over time; Fig. 4: the event log generated during the generation of behavior related activity signatures, or the first time period).
Regarding claim 24, Shalon discloses the system of claim 23. Shalon further discloses the system wherein the logging and analytics module is configured to log the first plurality of activity classifications with time information indicating at least one of a time-of-day or date when each of the first plurality of activity classifications was generated (Paragraphs 0152-0153: the databases, or logs, of activities and measured data; Paragraph 0221: the system may produce logs including times and durations; Paragraph 0261: the timestamp for extracted features; Paragraph 0266: the day and time of events is recorded in the event long to establish patterns for context determination of a present activity; Fig. 4: the event log).
Regarding claim 25, Shalon discloses the system of claim 23. Shalon further discloses the system wherein the first sensor is configured to generate a first electrical signal representing the body noises over the first period of time and the second period of time, and the second sensor is configured to generate a second electrical signal representing the external acoustic sound signals over the first period of time and the second period of time (Paragraphs 0116-0117: the microphones for collecting the signals during the first and second periods of time; Paragraph 0118: the microphone may be a MEMS microphone and thus inherently generates an electrical signal in response to the sensed audio waves; Paragraph 0167: a transducer may serve as a microphone and generate electrical signals in response to sound), and wherein the system further comprises:
a body noises processor (Paragraph 0235: the processor) configured to:
extract features of the body noises from the first electrical signal of the first sensor over the first period of time and the second period of time; and extract features of the external acoustic sound signals from the second electrical signal of the second sensor over the first period of time and the second period of time (Paragraphs 0107: each activity has a signature of specific features; Paragraphs 0117 the use of the microphones to detect internal and external events; Paragraphs 0252 and 0317: the extraction of features from sound signals to classify the sounds into specific events; Paragraphs 0152-0153 and 0260-0262: activities are determined for past and current time periods);
wherein the activity classifier is configured to: determine the first plurality of activity classifications of the person based on the features of the body noises extracted from the first electrical signal of the first sensor and the features of the external acoustic sound signals extracted from the second electrical signal of the second sensor over the first period of time; and determine the second plurality of activity classifications of the person based on the features of the body noises extracted from the first electrical signal of the first sensor and the features of the external acoustic sound signals extracted from the second electrical signal of the second sensor over the second period of time (Paragraphs 0104-0106: the detection of any activity from activity related signatures, or feature sets; Paragraphs 0117 the use of the microphones to detect internal and external events; Paragraphs 0252 and 0317: the extraction of features from sound signals to classify the sounds into specific events; Paragraphs 0152-0153 and 0260-0262: activities are determined for past and current time periods).
Regarding claim 26, Shalon discloses the system of claim 21. Shalon further discloses the system wherein the one or more auxiliary devices comprise one or more of: a temperature tracker, a heartrate monitor, a blood pressure sensor, and a body-worn fitness tracker (Paragraphs 0104-0106 and 0109: the various inputs used to detect activities; Paragraphs 0299-0300: the system can measure a plurality of other parameters such as temperature, blood pressure, and body motion and uses corresponding sensors for each).
Regarding claim 27, Shalon discloses the system of claim 25. Shalon further discloses the system wherein the body noises processor is configured to extract one or more of: time information, one or more signal levels, a frequency, and one or more measures regarding a static or dynamic nature of the body noises and the external acoustic sound signals (Paragraphs 0260-261: the signals received from the sensor unit and other input systems are first digitized then analyzed. The spectral analysis and raw signal are used to extract features and categorize them with a time stamp, or time information. Parameters that may accompany features include intensity, or signal level, frequency, and duration, or time information).
Regarding claims 28-29, Shalon discloses the system of claim 25. Shalon further discloses the system wherein the first sensor or the second sensor comprises a microphone, or wherein the first sensor comprises a first microphone and the second sensor comprises a second microphone (Paragraphs 0116-0117: the microphones for internal and external sound detection).
Regarding claim 30, Shalon discloses the system of claim 20. Shalon further discloses the system wherein the activity classifier comprises a machine learning algorithm (Paragraphs 0256-0257: the classifier may be a neural network which is a type of machine learning model).
Regarding claim 31, Shalon discloses the system of claim 20. Shalon further discloses the system wherein the activity classifier is trained using one or more labeled noise samples (Paragraph 0153: databases of acoustic or motion patterns can be used to train the algorithms; Paragraphs 0256-0257: human experts may annotate the training data and/or known databases may be used for training the system. The labels include labelled noise as evidenced by the experts being “expert listeners”; Paragraph 0263: the user may be asked to eat certain foods and drink liquids to train the system. Thus, the system is receiving data with a known classification since the user was asked to eat certain foods; Paragraphs 0401-0407: the models are trained using labeled data including labelled audio data).
Regarding claim 32, Shalon discloses a method (Abstract; Paragraph 0002) comprising:
detecting body noises of a person over a first period of time and a second period of time (Paragraphs 0116-0117: the microphones for picking up internal sounds which may be implanted in the user; Paragraphs 0047, 0152-0153, and 0212: a baseline behavior signature is generated over a period of time, or a first time period; The database of activity signatures over a time period used to establish norms, averages, and/or trends; Paragraphs 0147 and 0260-0262: the system can gather data and detect current events, or a second time period);
detecting external acoustic sound signals that are simultaneously received with one or more of the body noises over the first period of time and the second period of time (Paragraphs 0116-0117: the microphones for picking up external sounds which may be implanted in the user; Paragraph 0147: the system continuously collects data from the one or more sensors; Paragraph 0317: ambient sounds can be used to detect a variety of activities and/or situations; Paragraphs 0047 and 0152-0153: a baseline behavior signature is generated over a period of time, or a first time period; The database of activity signatures over a time period used to establish norms, averages, and/or trends; Paragraph 0147, 0260-0262, and 0317: the system can gather data and detect current events, or a second time period);
determining, using an activity classifier (Paragraphs 0037, 0240, and 0256: the classifications performed by the classifier) and based on the body noises and the external acoustic sound signals (Paragraphs 0260-0262, 0233, and 0317: the activity classification uses internal and ambient sounds; Paragraphs 0104-0107: the classification, or detection, of any type of activity using the input parameters such as urination using internal and/or external body sounds as described in paragraph 0125), a first plurality of activity classifications of activities of the person over the first period of time (Paragraphs 0047, 0152-0153, and 0265: a baseline behavior signature is generated over a period of time, or a first time period; The database of activity signatures over a time period used to establish norms, averages, and/or trends) and a second plurality of activity classifications of activities of the person over the second period of time (Paragraphs 0147 and 0260-0262: the system can gather data and detect current events, or a second time period);
analyzing the first plurality of activity classifications to generate one or more baseline behavior patterns for the person (Paragraphs 0047, 0152-0153, 0212, and 0265: the learning of norms and patterns);
analyzing the second plurality of activity classifications to generate one or more current behavior patterns for the person (Paragraphs 0260-0262, 0266, and 0317: the current activity identified; Paragraph 0152: the detection of changes indicates that current activity is detected; Paragraph 0147: the detection of an eating event; Paragraphs 0104-0107: the identification of any current activity);
analyzing the one or more current behavior patterns relative to the one or more baseline behavior patterns to detect one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns (Paragraph 0152: the detection of changes in the user’s eating patterns; Paragraph 0355: the detection of variances in eating or activity patterns); and
initiating or eliciting a remedial action with respect to the person in response to detecting the one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns (Paragraphs 0111-0112: the monitoring and modification of behavior through feedback based on identified activity; Paragraph 0152: when irregular eating patterns are detected the system may send an alert to the user or a third party; Paragraph 0163: audio or visual feedback; Paragraph 0356: the feedback provided based on deviations from activity patterns).
Regarding claim 33, Shalon discloses the method of claim 32. Shalon further discloses the method wherein analyzing the first plurality of activity classifications to generate the one or more baseline behavior patterns for the person comprises: receiving a plurality of auxiliary health inputs for the person from one or more auxiliary devices (Paragraphs 0104-0106 and 0109: the various inputs used to detect activities; Paragraphs 0299-0300: the system can measure a plurality of other parameters such as temperature, blood pressure, and body motion); and analyzing the first plurality of activity classifications and the plurality of auxiliary health inputs to generate the one or more baseline behavior patterns for the person (Paragraphs 0152-0153: the generation of norms and patterns for a subject).
Regarding claim 34, Shalon discloses the method of claim 33. Shalon further discloses the method wherein the one or more auxiliary devices comprise one or more of: a temperature tracker, a heartrate monitor, a blood pressure sensor, and a body-worn fitness tracker (Paragraphs 0104-0106 and 0109: the various inputs used to detect activities; Paragraphs 0299-0300: the system can measure a plurality of other parameters such as temperature, blood pressure, and body motion and uses corresponding sensors for each).
Regarding claim 35, Shalon discloses the method of claim 32. Shalon further discloses the method wherein initiating or eliciting a remedial action with respect to the person in response to detecting the one or more differences between the one or more current behavior patterns and the one or more baseline behavior patterns comprises generating one or more messages configured to initiate or elicit the remedial action with respect to the person in response to detecting the one or more differences (Paragraphs 0111-0112, 0152, 0153, and 0356: the feedback provided to the user; Paragraphs 0160 and 0164: the feedback may be messages).
Regarding claim 36, Shalon discloses the method of claim 32. Shalon further discloses the method further comprising logging the first plurality of activity classifications for the person (Paragraphs 0152-0153: the databases, or logs, of activities and measured data; Paragraph 0221: eating logs over time; Fig. 4: the event log generated during the generation of behavior related activity signatures, or the first time period).
Regarding claim 37, Shalon discloses the method of claim 32. Shalon further discloses the method further comprising logging the first plurality of activity classifications with time information indicating at least one of a time-of-day or date when each of the first plurality of activity classifications was generated (Paragraphs 0152-0153: the databases, or logs, of activities and measured data; Paragraph 0221: the system may produce logs including times and durations; Paragraph 0261: the timestamp for extracted features; Paragraph 0266: the day and time of events is recorded in the event log to establish patterns for context determination of a present activity; Fig. 4: the event log).
Regarding claim 38, Shalon discloses the method of claim 32. Shalon further discloses the method wherein the body noises are represented by one or more first electrical signals and the external acoustic sound signals are represented by one or more second electrical signals (Paragraphs 0116-0117: the microphones for collecting the signals during the first and second periods of time; Paragraph 0118: the microphone may be a MEMS microphone and thus inherently generates an electrical signal in response to the sensed audio waves; Paragraph 0167: a transducer may serve as a microphone and generate electrical signals in response to sound).
Regarding claim 39, Shalon discloses the method of claim 32. Shalon further discloses the method further comprising extracting one or more of: time information, one or more signal levels, a frequency, and one or more measures regarding a static or dynamic nature of the body noises and the external acoustic sound signals (Paragraphs 0260-261: the signals received from the sensor unit and other input systems are first digitized then analyzed. The spectral analysis and raw signal are used to extract features and categorize them with a time stamp, or time information. Parameters that may accompany features include intensity, or signal level, frequency, and duration, or time information).
Regarding claims 40-41, Shalon discloses the method of claim 32. Shalon further discloses the method wherein the body noises or the external acoustic sound signals are detected using a sensor configured to be implanted in the person, and wherein the sensor comprises a microphone (Paragraphs 0116-0117: the microphones for internal and external sound detection may be on the surface of the skin, implanted subcutaneously, or implanted completely internal to the body).
Regarding claim 42, Shalon discloses the method of claim 32. Shalon further discloses the method wherein the activity classifier comprises a machine learning algorithm (Paragraphs 0256-0257: the classifier may be a neural network which is a type of machine learning model).
Regarding claim 43, Shalon discloses the method of claim 32. Shalon further discloses the method wherein the activity classifier is trained using one or more labeled noise samples (Paragraph 0153: databases of acoustic or motion patterns can be used to train the algorithms; Paragraphs 0256-0257: human experts may annotate the training data and/or known databases may be used for training the system. The labels include labelled noise as evidenced by the experts being “expert listeners”; Paragraph 0263: the user may be asked to eat certain foods and drink liquids to train the system. Thus, the system is receiving data with a known classification since the user was asked to eat certain foods; Paragraphs 0401-0407: the models are trained using labeled data including labelled audio data).
Regarding claim 44, Shalon discloses the method of claim 32. Shalon further discloses the method, further comprising generating output information identifying one or more changes in a lifestyle of the person relative to the one or more baseline behavior patterns (Paragraph 0152: the detection of changes in the user’s eating patterns; Paragraph 0355: the detection of variances in eating or activity patterns; Paragraph 0154: notices changes in user food preferences over time; Paragraph 0210: the system may communicate statistics regarding the user’s activities including eating and activity levels and show the rate of change or trend in each; Paragraphs 0112 and 0351: the system may be adapted to eliminate certain lifestyle behaviors or habits such as smoking. Thus, the system may communicate summaries, or output information, identifying trends and rates of change, or one or more changes relative to the baseline, of the various behaviors and activities being monitored)
Conclusion
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/MATTHEW ERIC OGLES/Examiner, Art Unit 3791
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