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 .
This action is responsive to the Application filed on April 12, 2024. Claims 1-20 are pending in the case. Claims 1, 11, and 16 are the independent claims.
This action is non-final.
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 §§ 706.02(l)(1) – 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-7, 10, 16, 19, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7, 10, 16, 19, and 20 of U.S. Patent Application No. 8,634,944. Although the claims at issue are not identical, they are not patentably distinct from each other because any differences between the language of the claims can be considered to have the same meaning.
Instant Application
(representative claims; different text considered to have equivalent meaning in bold italics)
US Patent Application No. 18/634,521
(representative claims; different text considered to have equivalent meaning in bold italics)
1. A method, comprising: collecting, by a system comprising at least one processor from a sensor, sensor data associated with a first condition, resulting in collected sensor data;
based on the collected sensor data, using, by the system, a transformation function to create
simulated sensor data
associated with a second condition that is different from the first condition,
wherein the transformation function implements a known relationship between the collected sensor data and the simulated sensor data; and
training a machine learning model using the combination of the collected sensor data and the simulated data,
wherein the first condition being present represents that the sensor is in a first arrangement, and
wherein the second condition being present represents that the sensor is in a second arrangement that is different from the first arrangement.
2. The method of claim 1, wherein the collecting comprises collecting the sensor data from a wearable motion sensor.
3. The method of claim 1, wherein the collecting comprises collecting the sensor data from a microphone.
4. The method of claim 1, wherein, in the first condition, the wearable sensor is situated in the left ear of a user of the wearable sensor, and,
in the second condition, the wearable sensor is situated in a right ear of the user of the wearable sensor.
5. The method of claim 1, wherein, in the first condition, the wearable sensor is situated on the left hand of a user of the wearable sensor, and, in the second condition, the wearable sensor is situated on the right hand of the user of the wearable sensor.
6. The method of claim 1, wherein the sensor data associated with the first condition refers to the sensor data generated by a head gesture of a user of the wearable sensor.
7. The method of claim 1, wherein the sensor data associated with the first condition refers to the sensor data generated by a hand gesture of a user of the wearable sensor.
10. The method of claim 1, further comprising:
collecting, by the system comprising at least one processor from a sensor, sensor data associated with the second condition, resulting in collected sensor data;
based on the collected sensor data, using, by the system, a transformation function to create simulated sensor data associated with
first condition that is different from the first condition,
wherein the transformation function implements a known relationship between the collected sensor data and the simulated sensor data; and
training a machine learning model using the combination of the collected sensor data for the first and second conditions and the simulated data for the first and the second conditions.
16. A system, comprising:
a first component comprising a first integrated circuit chip, wherein the first component is configured to train a machine learning model by using collected sensor data in a first condition and simulated sensor data created by transforming the collected sensor data by using a plurality of transfer functions; and
(i.e. the copending application recites that the first component is configured to at least collect sensor data, using transformation functions to transform the collected sensor data using transformation functions, and train the machine learning model; this is considered equivalent to the instant application’s recited training of the machine learning model using collected sensor data and simulated sensor data created by transforming the collected sensor data)
a second component discrete from the first component comprising a second integrated chip configured to receive real time incoming sensor data in a second condition,
use a mapping function to map the real time incoming sensor data in the second condition to the collected sensor data in the first condition,
use the mapped data as an input to the machine learning model, and
use the machine learning model to perform a prediction;
wherein the first condition being present represents that the sensor is in a first arrangement, and
wherein the second condition being present represents that the sensor is in a second arrangement that is different from the first arrangement.
19. The system of claim 16, wherein receiving real time incoming sensor data comprises receiving data from a wearable motion sensor.
20. The system of claim 16, wherein receiving real time incoming sensor data comprises receiving data from a microphone.
1. A method, comprising: collecting, by a system comprising at least one processor from a sensor, sensor data associated with a gesture made in a first condition, resulting in collected sensor data,
wherein the first condition being present represents that the sensor is in a first known arrangement;
based on the collected sensor data, using, by the system, one or more transformation functions to create
a simulated unified data representation for recognizing the gesture made under a plurality of conditions comprising the first condition,
(i.e. the copending application’s recitation of simulated unified data is considered analogous to simulated sensor data as recited in the instant application)
wherein at least one condition of the plurality of conditions is different from the first condition,
(i.e. copending application’s recitation of “at least one condition…is different from the first condition is considered equivalent to the instant application’s recitation of “a second condition that is different from the first condition”)
wherein the one or more transformation functions implements a known relationship between the collected sensor data and the simulated unified data representation; and
training, by the system, using the simulated unified data representation, a machine learning model for recognizing the gesture made in any of the plurality of conditions.
(i.e. since the simulated unified data representation of the copending application is associated with a plurality of conditions including the first condition under which the sensor data is collected, training the machine learning model using the simulated unified data representation (associated with both collected data and simulated data) in the copending application is considered equivalent to training the machine learning model using the collected sensor data and simulated data in the instant application)
(as cited earlier in the claim, the first condition being present represents the senor in a first known arrangement)
(as cited earlier in the copending claim recites at least one condition of the plurality of conditions is different from the first condition, which is analogous to a second condition which is different from the first condition; where the first condition is based on a known arrangement, the different condition may be associated with a different arrangement)
2. The method of claim 1, wherein the collecting comprises collecting the sensor data from a wearable motion sensor.
3. The method of claim 1, wherein the collecting comprises collecting the sensor data from a microphone.
4. The method of claim 1, wherein, in the first condition, the wearable sensor is positioned in the left ear of a user of the wearable sensor or on the left side of the head of a user of the wearable sensor, and,
in the at least one of the plurality of conditions, the wearable sensor is positioned in the right ear of the user of the wearable sensor or on the left side of the head of a user of the wearable sensor.
5. The method of claim 1, wherein, in the first condition, the wearable sensor is positioned on the left hand of a user of the wearable sensor, and, in the at least one of the plurality of conditions, the wearable sensor is positioned on the right hand of the user of the wearable sensor.
6. The method of claim 2, wherein the gesture is a head gesture of a user of the wearable sensor.
7. The method of claim 2, wherein the gesture is a hand gesture of a user of the wearable sensor.
10. The method of claim 1, further comprising:
collecting, by a system comprising at least one processor from a sensor, sensor data associated with a gesture made in a second condition, resulting in collected sensor data, wherein the second condition being present represents that the sensor is in a second known arrangement;
based on the collected sensor data, using, by the system, one or more transformation functions to create a simulated unified data representation for recognizing the gesture made under a plurality of conditions comprising the second condition,
wherein at the first condition of the plurality of conditions is different from the second condition,
wherein the one or more transformation functions implements a known relationship between the collected sensor data and the simulated unified data representation; and
training, by the system, using the simulated unified data representation, a machine learning model for recognizing the gesture made in any of the plurality of conditions.
16. A system, comprising:
a first component comprising a first integrated circuit chip, wherein the first component is configured to collect sensor data in a first condition, use one or more transformation functions to transform the collected sensor data into a unified data representation, and input the unified data representation into a machine learning model stored on the first integrated circuit chip to train the machine learning model to recognize a gesture agnostic to a condition of a plurality of conditions under which the gesture was made; and
a second component discrete from the first component comprising a second integrated chip configured to receive real time incoming sensor data in one of the plurality of conditions same or different from the first condition,
use the one or more transformation functions to transform the real time incoming sensor data into a unified data representation,
input the transformed data as an input to the machine learning model that was trained by the first component and is stored on the second integrated circuit chip, and
use the machine learning model to perform a prediction;
wherein the first condition represents that the sensor is in a first arrangement, and
wherein the different condition represents that the sensor is in a second arrangement that is different from the first condition.
19. The system of claim 16, wherein the sensor comprises a wearable motion sensor.
20. The system of claim 16, wherein the sensor comprises a microphone.
Claim Rejections - 35 USC § 112
Claim 10 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 10 recites “the collected sensor data.” Prior to this, claim 10 also recites “collecting…sensor data associated with the second condition, resulting in collected sensor data.” However claim 1, which claim 10 depends upon (the claims of which are therefore inherited by claim 10) also recites “collecting…sensor data associated with a first condition, resulting in collected sensor data…” Therefore, there are two different instances of “collected sensor data” recited between claims 1 and 10 (collected sensor data associated with a first condition and collected sensor data associated with a second condition), and it is unclear which of these two instances “the collected sensor data” in claim 10 is intended to refer to. Therefore the limitation is indefinite. In the interest of providing full examination on the merits, the limitation is interpreted as referring to any collected sensor data.
Claim 10 also recites “first condition that is different from the first condition.” It is unclear how the first condition can be different from the first condition. Therefore, the limitation is indefinite. In the interest of providing full examination on the merits, the limitation is interpreted as referring to any condition.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 8-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Parvaneh et al. (US 20220319654 A1).
With respect to claim 1, Parvaneh teaches a method, comprising:
collecting, by a system comprising at least one processor from a sensor, sensor data associated with a first condition, resulting in collected sensor data (e.g. paragraph 0035, Fig. 2, collecting raw data from a sensor in block S212, indicating characteristics of the body of the subject and/or ambient conditions; physical location of sensor on body; paragraph 0036, physical location of sensor may be primary location on the body or secondary location on the body of the subject; paragraph 0038, physical location of sensor does not match the primary location; i.e. collecting sensor data, including where the sensor is located in a secondary location on the body of the subject);
based on the collected sensor data, using, by the system, a transformation function to create simulated sensor data associated with a second condition that is different from the first condition, wherein the transformation function implements a known relationship between the collected sensor data and the simulated sensor data (e.g. paragraph 0038, when it is determined that the physical location of the sensor does not match the primary location, the raw data is mapped from the actual physical location (which is a secondary location) of the sensor to the primary location in block S216 of Fig. 2 to provide mapped data; the mapping adjusts the raw data to account for differences in location between the secondary location and the primary location; mapping may be accomplished using ML algorithm; mapped data treated as if it were raw data collected by the sensor at the primary location; mapped data may be recorded in augmented database in block S218; i.e. the collected sensor data for the secondary location is mapped/transformed using a corresponding function implementing a known relationship/mapping to create the mapped data, which is analogous to simulated sensor data, where this mapped/simulated sensor data is associated with a placement of the sensor at a primary location different from the secondary location, analogous to a second condition which is different from the first condition); and
training a machine learning model using the combination of the collected sensor data and the simulated data (e.g. paragraph 0006, model previously trained using training data recorded from primary location; paragraph 0038, the selected model may be retrained using the mapped data recorded in the augmented database and the training data in block S219; paragraph 0045, training model on both wrist and chest datasets to be able to map currently available data from the wrist sensor to chest sensor data),
wherein the first condition being present represents that the sensor is in a first arrangement (e.g. paragraphs 0035-0038, sensor placed at secondary location on subject’s body), and
wherein the second condition being present represents that the sensor is in a second arrangement that is different from the first arrangement (e.g. paragraphs 0035-0038, sensor placed at primary location on subject’s body).
With respect to claim 2, Parvaneh teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein the collecting comprises collecting the sensor data from a wearable motion sensor (e.g. paragraph 0001, motion sensors such as accelerometers and gyroscopes; paragraph 0018, wearable sensor including accelerometer, gyroscope, etc.; paragraph 0035, raw sensor data including acceleration, physical movement, etc.).
With respect to claim 3, Parvaneh teaches all of the limitations of claim 1 as previously discussed and further teaches wherein the collecting comprises collecting the sensor data from a microphone (e.g. paragraph 0018, wearable sensor including a microphone; paragraph 0030, microphone capturing audio data such as heart and lung sounds, etc.).
With respect to claim 8, Parvaneh teaches all of the limitations of claim 1 as previously discussed, and further teaches another system embedded in a consumer electronics device for receiving real time incoming sensor data, wherein the another system is different from the system for using the transformation function to create simulated data, wherein using the another system to make a prediction based on the real time incoming sensor data using the machine learning model (e.g. paragraph 0001, personal wearables such as Apple Watch and Fitbit having wearable sensors and collecting raw data; paragraph 0018, wearable sensor may be a commercial wearable device such as Apple Watch, Fitbit, Philips Lifeline, etc.; paragraph 0006, wearable sensor device includes database storing pretrained model and memory that stores executable modules including a physical activity and posture recognition module and a sensor data mapping module, where the determination of the physical activity and posture of the subject is performed using the model in accordance with the physical activity and posture recognition module and the mapping of the raw data from the predetermined location to the primary location is performed in accordance with the sensor data mapping module; paragraph 0030, mapping raw sensor data from secondary location to primary location while the subject continues to wear the sensor at the secondary location (i.e. in real time); paragraph 0053, system may be implemented using dedicated hardware implementations such as ASPICs, programmable logic arrays, etc.; i.e. a commercial/consumer electronics device may include embedded functional modules/systems including at least a first module/system for receiving real-time incoming sensor data and making predictions using the model, and a second module/system for performing the mapping/using the transformation function to create the simulated/mapped data).
With respect to claim 9, Parvaneh teaches all of the limitations of claim 1 as previously discussed, and further teaches based on the collected sensor data, using, by the system, a plurality of transformation functions to create simulated sensor data associated with a plurality of conditions that are different from the first condition, wherein the transformation functions implement known relationships between the collected sensor data and the simulated sensor data (e.g. paragraph 0029, detecting possible changes to location of wearable sensor, detecting new location with another model such as another classifier model, etc.; paragraph 0030, wearable sensor worn at secondary location such as wrist, ankle, etc.; mapping raw data from secondary location 112 (i.e. wrist as shown in Fig. 1) or other secondary location on the body to the primary location; corresponding axis between two different sensor locations changing due to differences including how the subject is wearing the wearable sensor; using methods such as correlation to find corresponding axis in the two sensor locations to improve performance; using kinematic body models to transfer coordinate frame of one sensor to another based on kinematic links and joins between body parts where the sensors are attached; paragraph 0033, sensor worn at various locations on the body; paragraph 0036, any other location on subject’s body at which sensor located considered a secondary location; paragraph 0038, when it is determined that the physical location of the sensor does not match the primary location, the raw data is mapped from the actual physical location (which is a secondary location) of the sensor to the primary location in block S216 of Fig. 2 to provide mapped data; the mapping adjusts the raw data to account for differences in location between the secondary location and the primary location; mapping may be accomplished using ML algorithm; mapped data treated as if it were raw data collected by the sensor at the primary location; mapped data may be recorded in augmented database in block S218; i.e. the sensor may be positioned at a plurality of different secondary locations and for each given secondary location, this location will be detected, and a corresponding mapping/transformation will be applied in order to map the corresponding sensor data at the primary location, such that a plurality of transformation/mapping functions associated with different secondary locations on the body may be used to create simulated/mapped sensor data based on corresponding relationships between the plurality of different secondary locations and the primary location).
With respect to claim 10, Parvaneh teaches all of the limitations of claim 1 as previously discussed, and further teaches
collecting, by the system comprising at least one processor from a sensor, sensor data associated with the second condition, resulting in collected sensor data (e.g. paragraph 0025, indicating that training data may be acquired from training location other than the chest, in which case the primary location would correspond to the location from which the training data is acquired; paragraph 0034, training data collected from body of training subject at location that corresponds to the primary location; raw data acquired at the chest; i.e. training sensor data is collected from the sensor location corresponding to the primary location);
based on the collected sensor data, using, by the system, a transformation function to create simulated sensor data associated with first condition that is different from the first condition, wherein the transformation function implements a known relationship between the collected sensor data and the simulated sensor data (e.g. paragraph 0033, indicating that the sensor may be worn at various different locations on the body and indicating a plurality of possible secondary locations; paragraph 0038, when it is determined that the physical location of the sensor does not match the primary location, the raw data is mapped from the actual physical location (which is a secondary location) of the sensor to the primary location in block S216 of Fig. 2 to provide mapped data; the mapping adjusts the raw data to account for differences in location between the secondary location and the primary location; mapping may be accomplished using ML algorithm; mapped data treated as if it were raw data collected by the sensor at the primary location; mapped data may be recorded in augmented database in block S218; i.e. there may be a plurality of different secondary sensor locations (analogous to at least one first condition that is different from the first condition, i.e. a secondary location which is different from the original secondary location, such as a different wrist, an ankle, etc.), and the transformation/mapping function may be performed based on both the collected sensor data corresponding to the primary location (i.e. training data) and to the other/different secondary location to create simulated/mapped sensor data which is associated with the other/different secondary location based on a known relationship/mapping between the collected sensor data and simulated/mapped sensor data); and
training a machine learning model using the combination of the collected sensor data for the first and second conditions and the simulated data for the first and the second conditions (e.g. paragraph 0006, model stored in memory and previously trained using training data recorded from primary location; paragraph 0038, the selected model may be retrained using the mapped data recorded in the augmented database and the training data in block S219; paragraph 0045, training model on both wrist and chest datasets to be able to map currently available data from the wrist sensor to chest sensor data).
With respect to claim 11, Parvaneh teaches a non-transitory machine-readable medium, comprising executable instructions that, when executed by at least one processor, facilitate performance of operations (e.g. paragraph 0006, memory storing instructions for executing modules implementing described invention), comprising:
in response to receiving real time incoming sensor data associated with a second condition (e.g. paragraph 0030, indicating that the determining of the physical location of the sensor and mapping from the secondary location to primary location is performed while the subject continues to wear the wearable sensor at the secondary location (i.e. indicating the system performing in real-time/as the user performs activities waring the sensors); paragraph 0035, Fig. 2, collecting raw data from a sensor in block S212, indicating characteristics of the body of the subject and/or ambient conditions; physical location of sensor on body; paragraph 0036, physical location of sensor may be primary location on the body or secondary location on the body of the subject; paragraph 0038, physical location of sensor does not match the primary location; i.e. collecting sensor data, including where the sensor is located in a secondary location on the body of the subject),
using a mapping function to map the sensor data associated with the second condition to data associated with a first condition (e.g. paragraph 0038, when it is determined that the physical location of the sensor does not match the primary location, the raw data is mapped from the actual physical location (which is a secondary location) of the sensor to the primary location in block S216 of Fig. 2 to provide mapped data; the mapping adjusts the raw data to account for differences in location between the secondary location and the primary location; mapping may be accomplished using ML algorithm; mapped data treated as if it were raw data collected by the sensor at the primary location; mapped data may be recorded in augmented database in block S218; i.e. the collected sensor data for the secondary location is mapped/transformed using a corresponding function implementing a known relationship/mapping to create the mapped data, which is analogous to simulated sensor data, where this mapped/simulated sensor data is associated with a placement of the sensor at a primary location different from the secondary location, analogous to a second condition which is different from the first condition),
wherein the mapping function is usable by an integrated circuit chip of a consumer electronics device (e.g. paragraph 0006, processor executing instructions such as instructions for mapping raw data in accordance with sensor data mapping module; paragraph 0021, processor including application-specific integrated circuits (ASICs));
using the mapped data as an input to a machine learning model stored in memory of the integrated circuit chip (e.g. paragraph 0006, model stored in memory and previously trained using training data recorded from primary location; paragraph 0038, the selected model may be retrained using the mapped data recorded in the augmented database and the training data in block S219; paragraph 0045, training model on both wrist and chest datasets to be able to map currently available data from the wrist sensor to chest sensor data); and
making a prediction by using the machine learning model (e.g. paragraph 0029, classifier model that identifies changes in location of the wearable sensor; classifier model receiving input data and providing class (wrist, ankle, chest) in the output; paragraph 0038, mapping using the machine learning based algorithm; paragraph 0040, mapping model trained to map raw data from one location on subject’s body to primary location on subject’s body; pre-trained models for determining physical activity or posture trained on primary location; i.e. the retrained machine learning model may subsequently be used to make a corresponding classification/prediction, such as regarding an activity of a user);
wherein the first condition represents that the sensor is in a first arrangement (e.g. paragraphs 0035-0038, sensor placed at primary location on subject’s body), and
wherein the second condition represents that the sensor is in a second arrangement that is different from the first arrangement (e.g. paragraphs 0035-0038, sensor placed at secondary location on subject’s body).
With respect to claim 12, Parvaneh teaches all of the limitations of claim 11 as previously discussed, and further teaches wherein making the prediction comprises performing one of a classification analysis or a regression analysis (e.g. paragraph 0029, classifier model that identifies changes in location of the wearable sensor; classifier model receiving input data and providing class (wrist, ankle, chest) in the output; paragraph 0044, LSTM regression model to map left wrist raw sensor data to chest sensor data).
With respect to claim 13, Parvaneh teaches all of the limitations of claim 11 as previously discussed, and further teaches wherein receiving the real time incoming sensor data comprises receiving the sensor data from a wearable motion sensor or a wearable microphone (e.g. paragraph 0001, motion sensors such as accelerometers and gyroscopes; paragraph 0018, wearable sensor including accelerometer, gyroscope, microphone, etc.; paragraph 0030, microphone capturing audio data such as heart and lung sounds, etc.; paragraph 0035, raw sensor data including acceleration, physical movement, etc.).
With respect to claim 14, Parvaneh teaches all of the limitations of claim 11 as previously discussed, and further teaches wherein the operations further comprise: determining that the real time incoming sensor data is associated with the second condition (e.g. paragraph 0030, indicating that the determining of the physical location of the sensor and mapping from the secondary location to primary location is performed while the subject continues to wear the wearable sensor at the secondary location (i.e. indicating the system performing in real-time/as the user performs activities waring the sensors); paragraph 0038, physical location of sensor does not match the primary location, actual physical location is secondary location).
With respect to claim 15, Parvaneh teaches all of the limitations of claim 11 as previously discussed, and further teaches
wherein the integrated circuit chip is part of a first computing system (e.g. paragraph 0020, Fig. 1, any combination of processor 120, memory 130, etc., may be incorporated into the wearable sensor 110 itself, worn on the body of the subject; processor, memory, user interface, and communications interface located in the wearable sensor, enabling localized processing of the raw data collected by the wearable sensor; paragraph 0021, ASIC), and
wherein the operations further comprise: receiving the machine learning model and the mapping function from a second computing system that trained the machine learning model based on the first sensor data associated with the first condition (e.g. paragraph 0020, models database and augmented database located in remote server/cloud accessible to the wearable sensor over network/connection; functionalities divided between local and remote locations; paragraph 0021, processor 120 (in wearable device) capable of executing instructions stored in models database and augmented database and otherwise processing raw data, and execute instructions to implemented described methods; i.e. the models database may be located in a system separate from the sensor, such that the wearable device including the sensor receives the models and corresponding mappings from the remote/separate system).
With respect to claim 16, Parvaneh teaches a system, comprising:
a first component comprising a first integrated circuit chip (e.g. paragraph 0006, processor executing modules; paragraph 0020, models database and augmented database located in remote server/cloud accessible to the wearable sensor over network/connection; functionalities divided between local and remote locations; paragraph 0021, processor 120 (in wearable device) capable of executing instructions stored in models database and augmented database and otherwise processing raw data, and execute instructions to implemented described methods; paragraph 0026-0028, model training performed in computer using Windows, Mac, or Linux; training data collected from subject from location corresponding to primary location; training models and verifying performance), wherein the first component is configured to train a machine learning model by using collected sensor data in a first condition and simulated sensor data created by transforming the collected sensor data by using a plurality of transfer functions (e.g. paragraph 0006, model stored in memory and previously trained using training data recorded from primary location; paragraph 0038, the selected model may be retrained using the mapped data recorded in the augmented database and the training data in block S219; paragraph 0045, training model on both wrist and chest datasets to be able to map currently available data from the wrist sensor to chest sensor data); and
a second component discrete from the first component comprising a second integrated chip (e.g. paragraph 0006, processor executing modules; paragraph 0020, Fig. 1, any combination of processor 120, memory 130, etc., may be incorporated into the wearable sensor 110 itself, worn on the body of the subject; processor, memory, user interface, and communications interface located in the wearable sensor, enabling localized processing of the raw data collected by the wearable sensor; paragraph 0021, ASIC) configured to
receive real time incoming sensor data in a second condition (e.g. paragraph 0030, indicating that the determining of the physical location of the sensor and mapping from the secondary location to primary location is performed while the subject continues to wear the wearable sensor at the secondary location (i.e. indicating the system performing in real-time/as the user performs activities wearing the sensors); paragraph 0035, Fig. 2, collecting raw data from a sensor in block S212, indicating characteristics of the body of the subject and/or ambient conditions; physical location of sensor on body; paragraph 0036, physical location of sensor may be primary location on the body or secondary location on the body of the subject; paragraph 0038, physical location of sensor does not match the primary location; i.e. collecting sensor data, including where the sensor is located in a secondary location on the body of the subject),
use a mapping function to map the real time incoming sensor data in the second condition to the collected sensor data in the first condition (e.g. paragraph 0038, when it is determined that the physical location of the sensor does not match the primary location, the raw data is mapped from the actual physical location (which is a secondary location) of the sensor to the primary location in block S216 of Fig. 2 to provide mapped data; the mapping adjusts the raw data to account for differences in location between the secondary location and the primary location; mapping may be accomplished using ML algorithm; mapped data treated as if it were raw data collected by the sensor at the primary location; mapped data may be recorded in augmented database in block S218; i.e. the collected sensor data for the secondary location is mapped/transformed using a corresponding function implementing a known relationship/mapping to create the mapped data, which is analogous to simulated sensor data, where this mapped/simulated sensor data is associated with a placement of the sensor at a primary location different from the secondary location, analogous to a second condition which is different from the first condition),
use the mapped data as an input to the machine learning model (e.g. paragraph 0006, model stored in memory and previously trained using training data recorded from primary location; paragraph 0038, the selected model may be retrained using the mapped data recorded in the augmented database and the training data in block S219; paragraph 0045, training model on both wrist and chest datasets to be able to map currently available data from the wrist sensor to chest sensor data), and
use the machine learning model to perform a prediction (e.g. paragraph 0029, classifier model that identifies changes in location of the wearable sensor; classifier model receiving input data and providing class (wrist, ankle, chest) in the output; paragraph 0038, mapping using the machine learning based algorithm; paragraph 0040, mapping model trained to map raw data from one location on subject’s body to primary location on subject’s body; pre-trained models for determining physical activity or posture trained on primary location; i.e. the retrained machine learning model may subsequently be used to make a corresponding classification/prediction, such as regarding an activity of a user);
wherein the first condition being present represents that the sensor is in a first arrangement (e.g. paragraphs 0035-0038, sensor placed at primary location on subject’s body), and
wherein the second condition being present represents that the sensor is in a second arrangement that is different from the first arrangement (e.g. paragraphs 0035-0038, sensor placed at secondary location on subject’s body).
With respect to claim 17, Parvaneh teaches all of the limitations of claim 16 as previously discussed, and further teaches wherein the second component comprises one of a mobile phone, an earbud, or a wristwatch (e.g. paragraph 0018, wearable sensor such as Apple Watch, etc.).
With respect to claim 18, Parvaneh teaches all of the limitations of claim 16 as previously discussed, and further teaches wherein performing a prediction comprises performing one of a classification analysis or a regression analysis (e.g. paragraph 0029, classifier model that identifies changes in location of the wearable sensor; classifier model receiving input data and providing class (wrist, ankle, chest) in the output; paragraph 0044, LSTM regression model to map left wrist raw sensor data to chest sensor data).
With respect to claim 19, Parvaneh teaches all of the limitations of claim 16 as previously discussed, and further teaches wherein receiving real time incoming sensor data comprises receiving data from a wearable motion sensor (e.g. paragraph 0001, motion sensors such as accelerometers and gyroscopes; paragraph 0018, wearable sensor including accelerometer, gyroscope, etc.; paragraph 0035, raw sensor data including acceleration, physical movement, etc.).
With respect to claim 20, Parvaneh teaches all of the limitations of claim 16 as previously discussed, and further teaches wherein receiving real time incoming sensor data comprises receiving data from a microphone (e.g. paragraph 0018, wearable sensor including microphone, etc.; paragraph 0030, microphone capturing audio data such as heart and lung sounds, etc.).
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102€, (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Parvaneh in view of Tachibana et al. (US 20140086438 A1).
With respect to claim 4, Parvaneh teaches all of the limitations of claim 1 as previously discussed. Parvaneh does not explicitly disclose wherein, in the first condition, the wearable sensor is situated in the left ear of a user of the wearable sensor, and, in the second condition, the wearable sensor is situated in a right ear of the user of the wearable sensor.
However, Tachibana teaches wherein, in the first condition, the wearable sensor is situated in the left ear of a user of the wearable sensor, and, in the second condition, the wearable sensor is situated in a right ear of the user of the wearable sensor (e.g. paragraph 0051, Fig. 2A, headphones including sensor devices housed inside the housing of the earphones; paragraph 0056, potential wearing states of left and right earphones include states in which only the left or right earphone is being worn; paragraph 0069, detecting relationship between left and right output from sensors and determining headphones are in state in which only the left or right earphone is being worn; paragraph 0114, describing differences in sensor output when only left or right earphone is being worn as shown in Fig. 12).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Parvaneh and Tachibana in front of him to have modified the teachings of Parvaneh (directed to evaluating a subject using a wearable sensor), to incorporate the teachings of Tachibana (directed to control methods for mobile terminals including wearable left and right side sensors) to include the capability to situate the wearable sensor in a left ear of a user in a first condition and in a right ear of a user in a second condition. One of ordinary skill would have been motivated to perform such a modification in order to diversify mobile device control by ascertaining wearing states of earphones as described in Tachibana (paragraph 0008).
With respect to claim 8, Parvaneh teaches all of the limitations of claim 1 as previously discussed. Parvaneh does not explicitly disclose wherein the sensor data associated with the first condition refers to the sensor data generated by a head gesture of a user of the wearable sensor.
However, Tachibana teaches wherein the sensor data associated with the first condition refers to the sensor data generated by a head gesture of a user of the wearable sensor (e.g. paragraph 0072, user performing nodding gesture via head rotation in order to check on basis of sensor output during the gesture, whether or not the user is wearing each earphone and whether the earphones are being correctly worn on the left and right).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Parvaneh and Tachibana in front of him to have modified the teachings of Parvaneh (directed to evaluating a subject using a wearable sensor), to incorporate the teachings of Tachibana (directed to control methods for mobile terminals including wearable left and right side sensors) to include the capability to detect, as the sensor data associated with the first condition, sensor data generated by a head gesture of the user of the wearable sensor. One of ordinary skill would have been motivated to perform such a modification in order to diversify mobile device control by ascertaining wearing states of earphones as described in Tachibana (paragraph 0008).
Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Parvaneh in view of Poncot et al. (US 20240201793 A1).
With respect to claim 5, Parvaneh teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein, in the at least the first condition, or the second condition, the wearable sensor is situated on the hand of a user of the wearable sensor (e.g. paragraph 0019, indicating that a primary location may be the chest of the subject and the secondary location may be the wrist of the subject; further indicating other extremities, such as ankles are considered; paragraph 0025, indicating that training data may be acquired from training location other than the chest, in which case the primary location would correspond to the location from which the training data is acquired; paragraph 0030, primary location such as chest and secondary location such as chest, ankle, etc.; paragraph 0044, indicating mapping of left wrist raw sensor data to chest sensor data).
Parvaneh does not explicitly disclose wherein, in the first condition, the wearable sensor is situated on the left hand of a user of the wearable sensor, and, in the second condition, the wearable sensor is situated on the right hand of the user of the wearable sensor. However, Poncot teaches wherein, in the first condition, the wearable sensor is situated on the left hand of a user of the wearable sensor, and, in the second condition, the wearable sensor is situated on the right hand of the user of the wearable sensor (e.g. paragraph 0022, system for training gesture recognition model detecting side of user on which device is worn and transforming the gesture data if the device is worn on the off hand for which the gesture recognition model is trained; paragraph 0037, gesture recognition model trained for use in smart watch worn on left side of user; during training, sensor data received from smart watches worn on the right side of the user, and gesture recognition model only receives sensor data that is representative of sensor data for smart watches worn on the left wrist (directly or through transformation); during deployment of the model, side detection operation is performed and if the user is wearing the device on their right side (the off side), the sensor data is transformed to generate primary side data for use of gesture recognition model; i.e. in a first condition the wearable sensor may be situated on either the left or right hand, and in a second condition the wearable sensor may be situated on the other of the left or right hand).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Parvaneh and Poncot in front of him to have modified the teachings of Parvaneh (directed to evaluating a subject using a wearable sensor), to incorporate the teachings of Poncot (directed to training a gesture recognition model) to include the capability to situate the wearable sensor on a left hand of a user in a first condition and in a right hand of a user in a second condition. One of ordinary skill would have been motivated to perform such a modification in order to provide for a gesture recognition model trained using data from one side of user that is capable of detecting gestures on either side of the user, providing significant memory and processing saving over conventionally trained models as described in Poncot (paragraph 0023).
With respect to claim 7, Parvaneh teaches all of the limitations of claim 1 as previously discussed. Parvaneh does not explicitly disclose wherein the sensor data associated with the first condition refers to the sensor data generated by a hand gesture of a user of the wearable sensor.
However, Poncot teaches wherein the sensor data associated with the first condition refers to the sensor data generated by a hand gesture of a user of the wearable sensor (e.g. paragraph 0022, system for training gesture recognition model detecting side of user on which device is worn and transforming the gesture data if the device is worn on the off hand for which the gesture recognition model is trained; paragraph 0035, gesture recognition model trained using sensor data collected from devices worn on primary side of users and transformed off side sensor data; training gesture recognition model using sensor data and transformed off side sensor data such that gesture recognition model is trained using sensor data from primary side of user; paragraph 0037, gesture recognition model trained for use in smart watch worn on left side of user; during training, sensor data received from smart watches worn on the right side of the user, and gesture recognition model only receives sensor data that is representative of sensor data for smart watches worn on the left wrist (directly or through transformation); paragraph 0041, collected gesture data).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Parvaneh and Poncot in front of him to have modified the teachings of Parvaneh (directed to evaluating a subject using a wearable sensor), to incorporate the teachings of Poncot (directed to training a gesture recognition model) to include the capability to detect, as the sensor data associated with the first condition, sensor data generated by a hand gesture of the user of the wearable sensor. One of ordinary skill would have been motivated to perform such a modification in order to provide for a gesture recognition model trained using data from one side of user that is capable of detecting gestures on either side of the user, providing significant memory and processing saving over conventionally trained models as described in Poncot (paragraph 0023).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain,” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting in re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (GCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co, v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F,3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir, 2005): Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
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/JEREMY L STANLEY/
Primary Examiner, Art Unit 2127
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