Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
Claims 4, 5, and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4 and 5 each recite the limitation the wearable sensor. There is insufficient antecedent basis for this limitation in the claim. For the purpose of examination, the claims will be interpreted as if they had instead read the .
Claim 17 recites The system of Claim 16, The non-transitory machine-readable medium of Claim 11, wherein … It is indefinite as to which parent claim, Claim 17 is dependent upon. For the purpose of examination, the claim will be interpreted as if it depended solely upon Claim 16.
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 1, 2, 5, 10, 11, and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al., “Deep Transfer Learning for Cross-domain Activity Recognition.”
Regarding Claim 1, Wang teaches a method, comprising: collecting, by a system comprising at least one processor (Wang, pg. 6, 2nd column, 1st paragraph, “code” denotes they perform their experiments on a computer), from a sensor, sensor data associated with a gesture (Wang, pg. 5, Fig. 3, the smartphone, smartwatch, and wristband collect sensor data associated with the activity to be recognized) made in a first condition (Wang, pg. 2, Fig. 1, the sensors may be located on different body parts, i.e. in different conditions) resulting in collected sensor data (Wang, pg. 5, Fig. 3, “data”) wherein the first condition being present represents that the sensor is in a first known arrangement (Wang, pg. 2, Fig. 1, “If the labels of a certain part are missing (the red pentagon), how to leverage the well-labeled activity data on other body parts (the blue dots) to acquire its labels?” where sensors from known positions are in a first known arrangement, i.e. the “source domain,” see Abstract); based on the collected sensor data, using, by the system, one or more transformation functions to create a simulated unified data representation … wherein the one or more transformation functions implements a known relationship between the collected sensor data and the simulated unified data representation (Wang, pg. 5, Fig. 1, where the output of the “fc1” fully connected layer of the network is a simulated unified data representation since the source and target data should be similar, i.e. “Maximum Mean Discrepancy” will be low, so the layers from input to fc1 are a transformation function on the data) for recognizing the gesture (Wang, pg. 5, Fig. 3, the output is for activity recognition) made under a plurality of conditions comprising the first condition, wherein at least one condition of the plurality of conditions is different from the first condition (Wang, pg. 5, Fig. 3, “source” and “target” domains, also see pg. 2, Fig. 1, “The activity signals on different body parts are often different”); and training, by the system, using the simulated data representation, a machine learning model for recognizing the gesture made in any of the plurality of conditions (Wang, pg. 5, Fig. 3 & pg. 5, 1st column, 3rd paragraph, “We train the TNNAR …”).
Regarding Claim 2, Wang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wang further teaches wherein the collecting comprises collecting the sensor data from a wearable motion sensor (Wang, pg. 5, 2nd column, 1st paragraph, “accelerometer [and] gyroscope … are all used in three datasets”).
Regarding Claim 5, Wang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wang further teaches wherein the sensor is positioned on the left hand of a user of the sensor, and, in the at least one of the plurality of conditions, the sensor is positioned on the right hand of the user of the sensor (Wang, pg. 2, Fig. 1 & pg. 4, 1st column, 1st paragraph, “if the target domain is the Right Hand, we could probably give a larger weight to the source domain Left Hand”).
Regarding Claim 10, Wang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wang further teaches 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 (Wang, pg. 2, Fig. 1, any of the other blue positions); 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 (Wang, pg. 5, Fig. 3 & pg. 2, Fig. 1) wherein the one or more transformation functions implements a known relationship between the collected sensor data and the simulated unified data representation (Wang, pg. 5, Fig. 3, where the layers from input to fc1 remain a transformation function on the data); and training, by the system, using the simulated data representation, a machine learning model for recognizing the gesture made in any of the plurality of conditions (Wang, pg. 5, Fig. 3 & pg. 5, 1st column, 3rd paragraph, “We train the TNNAR …” & pg. 3, 2nd column, last paragraph, “if there are M source domains available, there will be M weights” denotes that both first and second conditions are used in training the model and generating the unified data representation).
Regarding Claim 11, Wang teaches a non-transitory machine-readable medium, comprising instructions that, when executed by at least one processor of a system, facilitate performance of operations (Wang, pg. 6, 2nd column, 1st paragraph, “code” denotes they perform their experiments on a computer), comprising: receiving, from a sensor, real time incoming sensor data associated with a gesture (Wang, pg. 5, Fig. 3, the smartphone, smartwatch, and wristband collect sensor data associated with the activity to be recognized), using one or more transformation functions to transform the received sensor data into a unified data representation; and inputting thee unified data representation into a machine learning model stored on the at least one processor and trained to recognize the gesture; and making a prediction by using the machine learning model; wherein the unified data representation is agnostic to a plurality of conditions in which the gesture is capable of being made (Wang, pg. 5, Fig. 3 & pg. 2, Fig. 1, where sensors from different positions are conditions and where the output of the “fc1” fully connected layer of the network is a simulated unified data representation that can take data from both source and target domains to make predictions with fc2, i.e. the output of the activity recognition are predictions of the gestures/activities).
Regarding Claim 13, Wang teaches the non-transitory machine-readable medium of Claim 11 (and thus the rejection of Claim 11 is incorporated). Wang further teaches wherein receiving the real time incoming sensor data from one of … a wearable motion sensor positioned on the left hand of the user, or a wearable motion sensor positioned on the right hand of the user (Wang, pg. 3, Fig. 3, the smartwatch is worn on either the left or right hand, also see pg. 2, Fig. 1).
Regarding Claim 14, Wang teaches the non-transitory machine-readable medium of Claim 11 (and thus the rejection of Claim 11 is incorporated). Wang further teaches wherein the machine learning model was generated using collected data sets associated with one or more conditions of the plurality of conditions (Wang, pg. 2, Fig. 1, “If the labels of a certain part are missing (the red pentagon), how to leverage the well-labeled activity data on other body parts (the blue dots) to acquire its labels?” & pg. 5, Fig. 3 & pg. 5, 1st column, 3rd paragraph, “We train the TNNAR …”).
Regarding Claim 15, Wang teaches the non-transitory machine-readable medium of Claim 11 (and thus the rejection of Claim 11 is incorporated). Wang further teaches wherein the transformation function is configured to one of determine an absolute value of data (Wang, pg. 4, 2nd column, final paragraph, “the ReLU units” where “ReLU” denotes rectified linear units, i.e. determining an absolute value of x when x is positive).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3, 4, 6, 7, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al., “Deep Transfer Learning for Cross-domain Activity Recognition,” in view of Li et al., “Enabling Voice-Accompanying Hand-to-Face Gesture Recognition with Cross-Device Sensing.”
Regarding Claim 3, Wang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wang does not teach, but Li, in the analogous art of gesture recognition using wearable sensors, teaches wherein the collecting comprises collecting the sensor data from a microphone (Li, pg. 9, Fig. 3, “Earbuds” & 1st column, last paragraph, “earbuds are most commonly used, we chose them as the primary device, which would work in different forms including … one-side “wearing one earbud)”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use earbuds/microphone to collect sensor data, as does Li, for the gesture recognition of Wang. The motivation to do so is that the “heterogeneous channels (vocal, ultrasound, IMU)” recognition method achieves high accuracy “providing the high applicability” (Li, Abstract), i.e. adding a microphone sensor provides high accuracy recognition.
Regarding Claim 4, Wang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wang does not teach, but Li, in the analogous art of gesture recognition using wearable sensors, teaches wherein in the first condition, the sensor is positioned in the left ear of a user of the sensor or on the left side of the head of a user of the 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 sensor or on the left side of the head of a user of the sensor (Li, pg. 9, Fig. 3, “Earbuds” & 1st column, last paragraph, “earbuds are most commonly used, we chose them as the primary device, which would work in different forms including … one-side “wearing one earbud)” in combination with Wang, pg. 2, Fig. 1, that shows it is important to compensate for sensors worn on the left or right side). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use earbuds/microphone to collect sensor data, as does Li, for the gesture recognition of Wang, which compensates for the different positions of the sensor. The motivation to do so is that the “heterogeneous channels (vocal, ultrasound, IMU)” recognition method achieves high accuracy “providing the high applicability” (Li, Abstract), i.e. adding a microphone sensor provides high accuracy recognition.
Regarding Claim 6, Wang teaches the method of Claim 2 (and thus the rejection of Claim 2 is incorporated). Wang does not teach, but Li, in the analogous art of gesture recognition using wearable sensors, teaches wherein the gesture is a head gesture of a user of the wearable sensor (Li, pg. 1, Fig. 1(c), “The user performs a ‘phone call’ gesture” where putting your hands up to your head is a head gesture). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the Wang method to recognize gestures such as “the phone call gesture,” as does Li. The motivation to do so is to recognize gestures a user might want to make, where the sensors (i.e. a smart watch) may be on different body parts.
Regarding Claim 7, Wang teaches the method of Claim 2 (and thus the rejection of Claim 2 is incorporated). Wang does not teach, but Li, in the analogous art of gesture recognition using wearable sensors, teaches wherein the gesture is a hand gesture of a user of the wearable sensor (Li, pg. 1, Fig. 1(c), “The user performs a ‘phone call’ gesture” where putting your hands up to your head is a hand gesture). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the Wang method to recognize gestures such as “the phone call gesture,” as does Li. The motivation to do so is to recognize gestures a user might want to make, where the sensors (i.e. a smart watch) may be on different body parts.
Regarding Claim 12, Wang teaches the non-transitory machine-readable medium of Claim 11 (and thus the rejection of Claim 11 is incorporated). Wang does not teach, but Li, in the analogous art of gesture recognition using wearable sensors, teaches wherein the gesture is … a hand gesture (Li, pg. 1, Fig. 1(c), “The user performs a ‘phone call’ gesture” where putting your hands up to your head is a hand gesture). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the Wang method to recognize gestures such as “the phone call gesture,” as does Li. The motivation to do so is to recognize gestures a user might want to make, where the sensors (i.e. a smart watch) may be on different body parts.
Claims 8, 9, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang, in view of Near, US PG Pub 2021/0319337.
Regarding Claim 8, Wang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wang is silent regarding wherein the system, which performs the training of the machine learning model, is discrete from another system that uses the machine learning model to recognize gestures; however, Near teaches this limitation (Near, Fig. 2, where training and detection are performed on different devices). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to train and deploy the model of Wang on different devices, as does Near. The motivation to do so is to “offload processing load, power consumption and demand” (Near, [0075]), i.e. to keep the compute-intensive training on a laptop and perform recognition on the lower-capability wearable device.
Regarding Claim 9, Wang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Wang further teaches after the training, facilitating, by the system, the machine learning model … to use the machine learning model to agnostically recognize the gesture when made under any one of the plurality of conditions (Wang, pg. 5, Fig. 3 & pg. 2, Fig. 1, where sensors from different positions are the conditions and where the model can take data from both source and target domains agnostically to make predictions with fc2, i.e. the output of the activity recognition are predictions of the gestures/activities).
Wang does not teach the machine learning model being stored on an integrated circuit chip embedded in a consumer electronics device to enable the consumer electrics device to use the machine learning model, but Near, also in the art of wearable gesture recognition, does teach this limitation (Near, Figs. 2 & 4A & [0075], “a smart wearable or Sports Detection Device 400 with an embedded AI sports recognition engine”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to deploy the trained model of Wang on an embedded wearable device, such as that of Near. The motivation to do so is to allow real-time local detection of the activity or gesture.
Regarding Claim 16, Wang teaches a system comprising: a first component comprising a first integrated circuit chip (Wang, pg. 6, 2nd column, 1st paragraph, “code” denotes they perform their experiments on a computer) wherein the first component is configured to collect sensor data (Wang, pg. 5, Fig. 3, the smartphone, smartwatch, and wristband collect sensor data associated with the activity to be recognized) in a first condition (Wang, pg. 2, Fig. 1, the sensors may be located on different body parts, i.e. in different conditions), use one or more transformation functions to transform the collected sensor data into a unified data representation (Wang, pg. 5, Fig. 1, where the output of the “fc1” fully connected layer of the network is a simulated unified data representation since the source and target data should be similar, i.e. “Maximum Mean Discrepancy” will be low, thus the layers from input to fc1 are a transformation function on the collected data), 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 (Wang, pg. 5, Fig. 3 & pg. 2, Fig. 1, where sensors from different positions are the conditions and where the model can take data from both source and target domains agnostically to make predictions with fc2, i.e. the output of the activity recognition are predictions of the gestures/activities & pg. 5, 1st column, 3rd paragraph, “We train the TNNAR …”); and … to receive real time incoming sensor data in one of the plurality of conditions same of 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 input into the machine learning model that was trained by the first component … 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 a different condition represents that the sensor is in a second arrangement that is different form the first condition (Wang, pg. 5, Fig. 3, predictions from both “source” and “target” domains, also see pg. 2, Fig. 1, “The activity signals on different body parts are often different”).
Wang does not teach a second component discrete from the first component comprising a second integrated chip nor the machine learning model that was trained by the first component being stored on the second integrated chip, but Near, also in the art of wearable gesture recognition, does teach this limitation (Near, Figs. 2 & 4A & [0075], “a smart wearable or Sports Detection Device 400 with an embedded AI sports recognition engine”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to deploy the trained model of Wang on an embedded wearable device, such as that of Near. The motivation to do so is to allow real-time local detection of the activity or gesture.
Regarding Claim 17, the Wang/Near combination of Claim 16 teaches the system of Claim 16 (and thus the rejection of Claim 16 is incorporated). Wang does not teach, but Near does teach wherein the gesture is one of … a hand gesture (Near, [0078], “When the ADM is appropriately embedding in the AI recognition of device 500 it will be able to determine when a slapshot occurred”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the Wang method to recognize gestures such as a slapshot as does Near The motivation to do so is to recognize gestures a user might be making, where the sensors (i.e. an IMU) may be on different body parts.
Regarding Claim 18, the Wang/Near combination of Claim 16 teaches the system of Claim 16 (and thus the rejection of Claim 16 is incorporated). Wang further teaches wherein the transformation function is configured to one of determine an absolute value of data (Wang, pg. 4, 2nd column, final paragraph, “the ReLU units” where “ReLU” denotes rectified linear units, i.e. determining an absolute value of x when x is positive).
Regarding Claim 19, the Wang/Near combination of Claim 16 teaches the system of Claim 16 (and thus the rejection of Claim 16 is incorporated). Wang further teaches wherein the sensor comprises a wearable motion sensor (Wang, pg. 5, 2nd column, 1st paragraph, “accelerometer [and] gyroscope … are all used in three datasets”).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Wang, in view of Near, and further in view of Li.
Regarding Claim 20, the Wang/Near combination of Claim 16 teaches the system of Claim 16 (and thus the rejection of Claim 16 is incorporated). Wang does not teach, but Li, in the analogous art of gesture recognition using wearable sensors, teaches wherein the sensor comprises a microphone (Li, pg. 9, Fig. 3, “Earbuds” & 1st column, last paragraph, “earbuds are most commonly used, we chose them as the primary device, which would work in different forms including … one-side “wearing one earbud)”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use earbuds/microphone to collect sensor data, as does Li, for the gesture recognition of Wang/Near. The motivation to do so is that the “heterogeneous channels (vocal, ultrasound, IMU)” recognition method achieves high accuracy “providing the high applicability” (Li, Abstract), i.e. adding a microphone sensor provides high accuracy recognition.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20, respectively, of copending Application No. 18/634,510 reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the reference application anticipate the corresponding claims of the instant application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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