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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1, 5-14,16-21 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 5, 6-8, 10-12,14-20 of copending Application No. 18945507 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because when claims in the pending application are broader than the ones in the patent, the broad claims in the pending application are rejected under obviousness type double patenting over previously patented narrow claims, In re Van Ornum and Stang, 214 USPQ 761. For example, claim 1 of the pending application has the same limitations as claim 1 of the patent except for the following in bold.
Therefore, claim 1 of the pending application is broader than claim 1 of the patent.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Current Application:18946937
Co-pending application: 18945507
An electronic device comprising: a memory; an interface; and a processing unit; wherein the processing unit is configured to: obtain, via the interface, external sensor data from a hearing device; determine, based on the external sensor data, a health parameter indicative of a cognitive state of a user of the hearing device; determine whether the health parameter satisfies a first criterion indicative of a cognitive decline; and in accordance with the health parameter satisfying the first criterion, causes the interface to provide a health representation associated with the cognitive state.
21. A method performed by an electronic device, the method comprising: obtaining external sensor data from a hearing device; determining, based on the external sensor data, a health parameter indicative of a cognitive state of a user of the hearing device; determining whether the health parameter satisfies a first criterion indicative of a cognitive decline; and in accordance with the health parameter satisfying the first criterion, outputting a health representation associated with the cognitive state.
1. A hearing device comprising: a memory; an interface; a processing unit; and one or more sensors; wherein the processing unit is configured to: obtain sensor data from the one or more sensors; determine, based on the sensor data, a health parameter indicative of a cognitive state of a user of the hearing device; determine whether the health parameter satisfies a first criterion indicative of a cognitive decline; and in accordance with the health parameter satisfying the first criterion, cause the interface to provide a health representation associated with the cognitive state.
20. A method performed by a hearing device, the method comprising: obtaining, from one or more sensors of the hearing device, sensor data; determining, based on the sensor data, a health parameter indicative of a cognitive state of a user of the hearing device; determining whether the health parameter satisfies a first criterion indicative of a cognitive decline; and in accordance with the health parameter satisfying the first criterion, outputting a health representation associated with the cognitive state.
Claim Rejections - 35 USC § 102
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 1-9, 13-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sivan (US2020/0245869).
As to Claim 1, Sivan teaches an electronic device (computing system 104 comprising various computing devices 106A..106N, [0032], Figures 1, [0035] , Figure 3 teaches computing device 300, [0060]) comprising: a memory ( storage device 316, [0061]); an interface( communication unit(s) 304 may include network interface cards, Ethernet cards, optical transceivers, radio frequency transceivers, or other types of devices that are able to send and receive information, [0063]).; and a processing unit( processing circuits 302 of computing device 300); wherein the processing unit( 302) is configured to: obtain, via the interface( [0063] teaches on FIG. 3, communication unit(s) 304 include a radio 306 that enables computing device 300 to communicate wirelessly with other computing devices, such as ear-wearable device(s) 102 (FIG. 1). Examples of communication unit(s) 304 may include network interface cards, external sensor data from a hearing device (ear wearable device 102 including sensors. [0042], Figures 1 and sensors 210 of the ear wearable device 102, Figure 2); determine, based on the external sensor data (on Figure 6, an example system 600 that includes a wellness evaluation system 602 implemented in accordance with the techniques of this disclosure. Wellness evaluation system 602 may be implemented in various devices. For example, some or all of wellness evaluation system 602 may be implemented in one or more of ear-wearable device(s) 102. In some examples, some or all of wellness evaluation system 602 may be implemented in computing system 104. [0081]. Wellness evaluation system 602 receives a set of inputs 604. Inputs 604 include input 604A through input 604N. Inputs 604 may include data generated by one or more of sensors 210 of ear-wearable device(s) 102.); a health parameter indicative of a cognitive state of a user of the hearing device; determine whether the health parameter satisfies a first criterion indicative of a cognitive decline, [0089] teaches the provided information may inform the third-parties of signs of cognitive and/or physical decline. In some examples, the provided information may identify opportunities for intervention. In some examples, the provided information may identify risks of depression, social isolation, activity limitation, falling, loneliness, and/or other factors associated with the mental, emotional, or physical health of the user of ear-wearable device(s) 102. In some other examples, the provided information may identify risks of one or more of a speech language pathology, delayed language development, attention deficit, learning disability, patterns of bullying or abuse, and/or other factors associated with the user, e.g., childhood, adolescent, or educational development of the user of ear-wearable device(s) 102. In some examples, the provided information may indicate whether ear-wearable device(s) 102 need to be adjusted to better serve the user and in accordance with the health parameter satisfying the first criterion, causes the interface to provide a health representation associated with the cognitive state ([0072] teaches Companion application 324 may store one or more of various types of data as historical data 326. Historical data 326 may comprise a database for storing historic data related to cognitive benefit. For example, companion application 324 may store, in historical data 326, brain wellness scores, body wellness scores, sub-component values, data from ear-wearable device(s) 102, and/or other data. Companion application 324 may retrieve data from historical data 326 to generate a GUI for display of past levels of one or more wellness measures of the user of ear-wearable device(s) 102. In at least one example, the wellness level data and statistic data may be stored and shared as an input into to a risk prediction model and optionally associated with, e.g., a detected or predicted balance event.
As to Claim 2, Sivan teaches the limitations of Claim 1, and wherein the interface comprises a screen configured to display a user interface providing the health representation, ([0074] teaches third-party computing device 400 includes one or more processors, i.e., processing circuit(s), 402, communication unit(s) 404 (which may include a radio 406), input device(s) 408, output device(s) 410, display screen 412).
As to Claim 3, Sivan teaches the limitations of Claim 1, and wherein in accordance with the first criterion being satisfied, [0089] teaches the provided information may inform the third-parties of signs of cognitive and/or physical decline. In some examples, the provided information may identify opportunities for intervention. the electronic device is configured to perform a cognitive test scheme, [0072] teaches historical data 326 may comprise a database for storing historic data related to cognitive benefit. For example, companion application 324 may store, in historical data 326, brain wellness scores, body wellness scores, sub-component values, data from ear-wearable device(s) 102, and/or other data. Companion application 324 may retrieve data from historical data 326 to generate a GUI for display of past levels of one or more wellness measures of the user of ear-wearable device(s) 102. In at least one example, the wellness level data and statistic data may be stored and shared as an input into to a risk prediction model and optionally associated with, e.g., a detected or predicted balance event.
As to Claim 4, Sivan teaches the limitations of Claim 1, and, wherein the external sensor data comprises microphone input data, ([0055] teaches receiver 204 comprises one or more speakers for generating sound. Microphone(s) 208 detect incoming sound and generate electrical signal(s) (e.g., analog or digital electrical signal(s)) representing the incoming sound and wherein the health parameter is based on the microphone input data, [0150] teaches the direction of origin of a voice sound can be determined based on the signals from an array of operatively connected microphones (e.g., directional microphones) and a model of the relative positioning of the array of microphones.
As to Claim 5, Sivan teaches the limitations of Claim 4, wherein the processing unit is configured to determine the health parameter by determining, based on the microphone input data, a first voice biomarker, [0130] teaches data preparation system 700 may process sound detected by ear-wearable device(s) 102 or another operatively connected microphone to generate a voice print. The voice print may comprise information that characterizes a voice of a person.
As to Claim 6, Sivan teaches the limitations of Claim 5, and wherein the first voice biomarker is based on one or more of: a linguistic parameter, an acoustic parameter, a verbal fluency parameter, a mumbling parameter, a voice pitch parameter, or a speech rhythm parameter, Sivan teaches on [0150] wellness evaluation system 602 may be trained to recognize patterns in communication patterns of the user, such as: fundamental frequency of voice, spectral distribution of voice formants, prosody patterns, communication content, mannerisms, and the like. In a least one example, wellness evaluation system 602 may differentiate the user's voice sounds from other peoples' voice sounds based on signals generated by directional microphones in ear-wearable device(s) 102. In this example, the direction of origin of a voice sound can be determined based on the signals from an array of operatively connected microphones (e.g., directional microphones) and a model of the relative positioning of the array of microphones.
As to Claim 7, Sivan teaches the limitations of Claim 1, and wherein the external sensor data comprises physiological data, and wherein the health parameter is based on the physiological data, [0117] teaches wellness measure levels and statistics relative to wellness measures observed over time. Outcome measures may also include other physiological and behavioral observations which may be measured or reported by one or more sensors, monitoring devices, and health record systems operatively connected the computing device. And [0052] teaches sensors 210 also include a heart rate sensor 222, a body temperature sensor 224, and an electroencephalography (EEG) sensor 226. In other examples, ear-wearable device 102A may include more, fewer, or different components.)
As to Claim 8, Sivan teaches the limitations of Claim 1, and wherein the processing unit is configured to determine the health parameter by determining, based on the physiological data, a first physiological biomarker, Sivan teaches on [0082] Fig. 6, wellness evaluation system 602 receives a set of inputs 604. Inputs 604 include input 604A through input 604N. Inputs 604 may include data generated by one or more of sensors 210 of ear-wearable device(s) 102. In some examples, inputs 604 include data generated by one or more other devices, such as a mobile device associated with the user of ear-wearable device(s) 102. For instance, in some examples, inputs 604 may include data generated by one or more sensors of the mobile device. In some examples where wellness evaluation system 602 is implemented in ear-wearable device(s) 102, inputs 604 may include data received from the mobile device. In some examples where wellness evaluation system 602 is implemented in mobile device(s) 102 (FIG. 1), inputs 604 may include data received from ear-wearable device(s) 102.
As to Claim 9, Sivan teaches the limitations of Claim 8, and wherein the first physiological biomarker is based on one or more of: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiratory parameter, a temperature parameter, an oxygen parameter, or a brain activity parameter, [0052] teaches FIG. 2, sensors 210 also include a heart rate sensor 222, a body temperature sensor 224, and an electroencephalography (EEG) sensor 226. In other examples, ear-wearable device 102A may include more, fewer, or different components. For instance, in other examples, ear-wearable device 102A does not include one or more of the sensors shown in the example of FIG. 2. In some examples, heart rate sensor 222 comprises a visible light sensor and/or a pulse oximetry sensor.
As to Claim 13, Sivan teaches the limitations of Claim 1, and wherein the electronic device comprises one or more sensors, and wherein the processing unit is configured to obtain internal sensor data from the one or more sensors, and wherein the health parameter is based on the internal sensor data, Sivan on [0225] teaches MU data may or other types of data generated by sensors (e.g., sensors for detecting changes of shape of the ear canal, internal microphones for detecting eating/drinking sounds, etc.) of ear-wearable device(s) 102 may be indicative or eating and drinking activities (e.g., jaw movement, swallowing, etc.). Classification engines 702 of wellness evaluation system 602 may be trained to recognize eating and drinking activities based on the provided input data.
As to Claim 14, Sivan teaches the limitations of Claim 1, and wherein the processing unit comprises machine learning circuitry configured to operate according to a machine learning model, and wherein the processing unit is configured to determine the health parameter based on the external sensor data using the machine learning model, Sivan on [0238] teaches a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input; determining, by the one or more processing circuits, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse; and performing, by the one or more processing circuits, an action in response to determining that the user of the ear-wearable device has potentially been subject to the type of physical abuse.
As to Claim 15, Sivan teaches the limitations of Claim 1, and wherein the interface comprises one or more of: a Bluetooth interface, Bluetooth low energy interface, and a magnetic induction interface, Sivan on [0063] teaches communication unit(s) 304 may enable computing device 300 to send data to and receive data from one or more other computing devices (e.g., via a communications network, such as a local area network, mesh network, or the Internet). In some examples, communication unit(s) 304 may include wireless transmitters and receivers that enable computing device 300 to communicate wirelessly with the other computing devices. For instance, in the example of FIG. 3, communication unit(s) 304 include a radio 306 that enables computing device 300 to communicate wirelessly with other computing devices, such as ear-wearable device(s) 102 (FIG. 1). Examples of communication unit(s) 304 may include network interface cards, Ethernet cards, optical transceivers, radio frequency transceivers, or other types of devices that are able to send and receive information. Other examples of such communication units may include 900 MHz, Bluetooth, 3G, and WI-FI™ radios, Universal Serial Bus (USB) interfaces, etc. Computing device 300 may use communication unit(s) 304 to communicate with one or more ear-wearable devices (e.g., ear-wearable device 102 (FIG. 1, FIG. 3)). Additionally, computing device 300 may use communication unit(s) 304 to communicate with one or more other remote devices (e.g., server device 108 (FIG. 1)).
As to Claim 16, Sivan teaches the limitations of Claim 1, and wherein the health representation comprises information regarding a degree of cognitive decline of the user, [0099] teaches a “brain wellness score,” which may correspond to an overall level of mental wellness of the user of ear-wearable device(s), may be based on one or more of the use score, the engagement score, the active listening score, and/or other types of scores or data. For instance, the “brain wellness score” may be the weighted sum of e.g., the use score, the engagement score, the active listening score and/or other types of scores or data, thus teaching cognitive decline data.
As to Claim 17, Sivan teaches the limitations of Claim 1, and wherein the health representation comprises a cognitive score, [0099] teaches a “brain wellness score,” which may correspond to an overall level of mental wellness of the user of ear-wearable device(s), may be based on one or more of the use score, the engagement score, the active listening score, and/or other types of scores or data. For instance, the “brain wellness score” may be the weighted sum of e.g., the use score, the engagement score, the active listening score and/or other types of scores or data.
As to Claim 18, Sivan teaches the limitations of Claim 1, and, wherein the health representation comprises one or more of: a representation of a voice biomarker, a representation of a physiological biomarker, or a representation of a biokinetic biomarker, [0150] teaches wellness evaluation system 602 may be trained to recognize patterns in communication patterns of the user, such as: fundamental frequency of voice, spectral distribution of voice formants, prosody patterns, communication content, mannerisms, and the like. In a least one example, wellness evaluation system 602 may differentiate the user's voice sounds from other peoples' voice sounds based on signals generated by directional microphones in ear-wearable device(s) 102. In this example, the direction of origin of a voice sound can be determined based on the signals from an array of operatively connected microphones (e.g., directional microphones) and a model of the relative positioning of the array of microphones.
As to Claim 19, Sivan teaches the limitations of Claim 1, and wherein the processing unit comprises hardware, [0269] teaches various units may be combined in a hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and/or firmware.
As to Claim 20, Sivan teaches the limitations of Claim 1, and, wherein the processing unit comprises a processor, [0268] teaches processing circuitry may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry.
As to Claim 21, Sivan teaches a method performed by an electronic device an electronic device (computing system 104 comprising various computing devices 106A..106N, [0032], Figures 1, [0035] , Figure 3 teaches computing device 300, [0060] and Figure 9, [0154] teaches a method performed by the evaluation system 602) comprising: : obtaining external sensor data from a hearing device ( step 900, Figure 2 and Figure 9); determining, based on the external sensor data, on Figure 6, an example system 600 that includes a wellness evaluation system 602 implemented in accordance with the techniques of this disclosure. Wellness evaluation system 602 may be implemented in various devices. For example, some or all of wellness evaluation system 602 may be implemented in one or more of ear-wearable device(s) 102. In some examples, some or all of wellness evaluation system 602 may be implemented in computing system 104. [0081]. Wellness evaluation system 602 receives a set of inputs 604. Inputs 604 include input 604A through input 604N. Inputs 604 may include data generated by one or more of sensors 210 of ear-wearable device(s) 102.); a health parameter indicative of a cognitive state of a user of the hearing device; determining whether the health parameter satisfies a first criterion indicative of a cognitive decline, [0089] teaches the provided information may inform the third-parties of signs of cognitive and/or physical decline. In some examples, the provided information may identify opportunities for intervention. In some examples, the provided information may identify risks of depression, social isolation, activity limitation, falling, loneliness, and/or other factors associated with the mental, emotional, or physical health of the user of ear-wearable device(s) 102. In some other examples, the provided information may identify risks of one or more of a speech language pathology, delayed language development, attention deficit, learning disability, patterns of bullying or abuse, and/or other factors associated with the user, e.g., childhood, adolescent, or educational development of the user of ear-wearable device(s) 102. In some examples, the provided information may indicate whether ear-wearable device(s) 102 need to be adjusted to better serve the user and in accordance with the health parameter satisfying the first criterion, causes the interface to provide a health representation associated with the cognitive state ([0072] teaches Companion application 324 may store one or more of various types of data as historical data 326. Historical data 326 may comprise a database for storing historic data related to cognitive benefit. For example, companion application 324 may store, in historical data 326, brain wellness scores, body wellness scores, sub-component values, data from ear-wearable device(s) 102, and/or other data. Companion application 324 may retrieve data from historical data 326 to generate a GUI for display of past levels of one or more wellness measures of the user of ear-wearable device(s) 102. In at least one example, the wellness level data and statistic data may be stored and shared as an input into to a risk prediction model and optionally associated with, e.g., a detected or predicted balance event.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
1. Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable Sivan ( (US20200245869) in view of Chen (US20220273227).
As to Claim 10, Sivan teaches the limitations of Claim 8, but does not explicitly teach wherein the external sensor data comprises biokinetic data, and wherein the health parameter is based on the biokinetic data. However, Chen in related field (devices to analyze user’s cognitive state) teaches on [0032]-[0034] and [0084] teaches the sensor(s) 202 may also be able to sense and/or record behavioral parameters about the subject. The physiological parameters, the environmental parameters, and the behavioral parameters may be collectively referred to herein as sensed data 216.
[0033] In some embodiments, the collection component 204 is configured to collect, receive, store, supplement, and/or process the sensed data 216 from the sensor(s) 202, as shown in FIG. 3 (block 302). [0084] teaches the detection algorithm 222 may comprise a decision tree that uses digital biomarkers calculated from the raw collected data 218 to determine whether a subject 104 is experiencing cognitive decline. The decision tree may comprise one or more processing steps for calculating digital biomarkers from the raw collected data 218, and/or to compare the processed digital biomarkers against thresholds or expected ranges. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention to further record biokinetic data as taught by Chen to use digital biomarkers calculated from the raw collected data 218 to determine whether a subject 104 is experiencing cognitive decline based on the biokinetic data collected.
As to claim 11, Sivan in view of Chen teaches the limitations of Claim 10 and wherein the processing unit is configured to determine the health parameter by determining, based on the biokinetic data, a first biokinetic biomarker, Chen teaches 0084] teaches the detection algorithm 222 may comprise a decision tree that uses digital biomarkers calculated from the raw collected data 218 to determine whether a subject 104 is experiencing cognitive decline. The decision tree may comprise one or more processing steps for calculating digital biomarkers from the raw collected data 218, and/or to compare the processed digital biomarkers against thresholds or expected ranges
As to claim 12, Sivan in view of Chen teaches the limitations of Claim 11 and wherein the first biokinetic biomarker is based on one or more of: a motion parameter, a trembling parameter, a shaking parameter, or a tic parameter, e sensor(s) 202 may be configured to sense physiological parameters such as one or more signals indicative of a patient's physical activity level and/or activity type (e.g., using an accelerometer), metabolic level and/or other parameters relating to a human body, such as heart rate (e.g., using a photoplethysmogram), temperature (e.g., using a thermometer), blood pressure (e.g., using a sphygmomanometer), blood characteristics (e.g., glucose levels), diet, relative geographic position (e.g., using a Global Positioning System (GPS)), and/or the like. See at least Chen on [0032]
Conclusion
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/SUNITA JOSHI/Primary Examiner, Art Unit 2691
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