DETAILED ACTION
Claims 1-20 are presented for examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings received on 22 June 2022 are accepted.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Applicant is advised that should claim 5 be found allowable, claim 11 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim 5
Claim 11
5. The jaw movement analysis system according to claim 1, wherein
11. The jaw movement analysis system according to claim 1, further comprising:
the chewing information is acquired based on jaw movement information detected by
… wherein the chewing information is acquired based on the jaw movement information detected.
a sensor mounted on a lower jaw denture of the user.
comprising: a sensor mounted on a denture of the user to detect jaw movement information, ….
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.
Claims 1, 2, 4, 12-15, 18, and 19
Claims 1, 2, 4, 12-15, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Farooq, M., et al. “A Comparative Study of Food Intake Detection Using Artificial Neural Network and Support Vector Machine” IEEE 12th Int’l Conf. on Machine Learning & Applications, pp. 153-157 (2013) [herein “Farooq”] in view of Huang, Q., et al. “Your Glasses Know Your Diet: Dietary Monitoring Using Electromyography Sensors” IEEE Internet of Things J., vol. 4, no. 3 (2017) (cited in IDS dated 12 January 2024) [herein “Huang”].
Claim 1 recites “1. A jaw movement analysis system.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.”
Claim 1 further recites “comprising: circuitry configured to: acquire chewing information including time-series information that represents a jaw movement of a user chewing a bite of food.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” Farooq page 154 right column last paragraph discloses:
The jaw motion sensor signals were divided into non-overlapping segments of 30s called epochs. This epoch size helps to detect small food intake events such as snacking. Time and frequency domain features were computed for each epoch of the jaw motion sensor signal (Table 1).
Jaw motion sensor signals in 30 second segments correspond with acquired time-series chewing information representing jaw movement.
Claim 1 further recites “and determine an attribute of the food having been chewed by the user based on the chewing information acquired, and based on an analysis model.” The classifier taught by Farooq determines whether an epoch is “no food intake” or “food intake”. Farooq page 155 left column lines 1-2. However, this is a classification of the epoch and not an attribute of the food itself.
Farooq page 157 left column last paragraph discloses “Additional sensors such as a camera with computer vision algorithms can be used to estimate portion size and the contents of the meals.” Estimating a portion size is articulating a desire for a future capability to determine a size attribute of the food. Farooq does not explicitly disclose determine an attribute of the food chewed by the user; however, in analogous art of dietary monitoring, Huang page 709 section IV teaches:
Meal composition is an important dimension of dietary monitoring, …. The goal of this paper is to recognize the broad food category. ….
A decision tree-based classifier is used for classification, ….
…
For a chewing sequence, chews in the sequence may be classified with different labels. Since food content is unlikely to change within a bite, similar to [2], a majority vote is performed over a chewing sequence to decide on its food content.
Determining food content for a chewing sequence corresponds with determining an attribute of the food itself being chewed.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq and Huang. One having ordinary skill in the art would have found motivation to use determining food content into the system of food intake detection for the advantageous purpose of estimating portion size and contents of the meals. See Farooq page 157 left column last paragraph. Furthermore, Huang page 709 section IV recognizes “Meal composition is an important dimension of dietary monitoring, which is of great value for chronic disease prevention and diagnosis.”
Claim 1 further recites “wherein the analysis model is generated by machine learning based on training data including first information and second information” Farooq page 155 left column section C “Artificial Neural Network (ANN)” discloses:
A three layered (input layer, hidden layer and output layer) feed-forward neural network with back-propagation training algorithm was used for food intake classification. The proposed ANN architecture consisted of an input layer with 38 input neurons (one for each predictor), one hidden layer with N hidden neurons and one output layer with one output neuron. N ranged from 1 to 10 neurons to obtain the optimum ANN classifier.
An artificial neural network (ANN) is a machine learning analysis model. Back-propagation corresponds with training the ANN.
Claim 1 further recites “wherein the first information includes time-series information indicating a past jaw movement during a chewing of a bite of food.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” The time domain and frequency domain features correspond with first information time-series information.
Claim 1 further recites “and wherein the second information indicates an attribute of the food chewed during the past jaw movement associated with the first information.” Farooq page 155 left column first line discloses “Each feature vector fi was associated with a class label ci.” The class label corresponds with second information indicating an attribute of the food associated with the first information. The training data set with included class labels is past jaw movement data.
Farooq page 157 left column last paragraph discloses “Additional sensors such as a camera with computer vision algorithms can be used to estimate portion size and the contents of the meals.” Estimating a portion size is articulating a desire for a future capability to determine a size attribute of the food. Farooq does not explicitly disclose determine an attribute of the food chewed by the user; however, in analogous art of dietary monitoring, Huang page 709 section IV teaches:
Meal composition is an important dimension of dietary monitoring, …. The goal of this paper is to recognize the broad food category. ….
A decision tree-based classifier is used for classification, ….
…
For a chewing sequence, chews in the sequence may be classified with different labels. Since food content is unlikely to change within a bite, similar to [2], a majority vote is performed over a chewing sequence to decide on its food content.
Determining food content for a chewing sequence corresponds with determining an attribute of the food itself being chewed. The decision tree classification of food content is the second information corresponding with a class label as combined with Farooq.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq and Huang. One having ordinary skill in the art would have found motivation to use determining food content into the system of food intake detection for the advantageous purpose of estimating portion size and contents of the meals. See Farooq page 157 left column last paragraph. Furthermore, Huang page 709 section IV recognizes “Meal composition is an important dimension of dietary monitoring, which is of great value for chronic disease prevention and diagnosis.”
Claim 2 further recites “2. The jaw movement analysis system according to claim 1, wherein the past jaw movement is a past jaw movement performed by the user.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” The time domain and frequency domain features correspond with first information time-series information.
Farooq page 154 left column section II(A) disclose “Training data were collected from a total of 12 subjects.” Without loss of generality, the subjects from which data is collected correspond with the user(s).
Claim 4 further recites “4. The jaw movement analysis system according to claim 1, wherein the first information of the training data includes first chewing information and second chewing information, wherein the first chewing information includes time-series information indicating a first chewing motion in the first information, and the second chewing information includes time-series information indicating a second chewing motion in the first information.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” Farooq page 154 right column last paragraph discloses:
The jaw motion sensor signals were divided into non-overlapping segments of 30s called epochs. This epoch size helps to detect small food intake events such as snacking. Time and frequency domain features were computed for each epoch of the jaw motion sensor signal (Table 1).
Jaw motion sensor signals in 30 second segments correspond with acquired time-series chewing information representing jaw movement. The respective features are each a respective information of the chewing information.
The classifier taught by Farooq determines whether an epoch is “no food intake” or “food intake”. Farooq page 155 left column lines 1-2.
Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” Using the captures signals to train indicates this is training data.
Claim 4 further recites “wherein the chewing information acquired includes first chewing information and second chewing information associated with the bite of food chewed by the user.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.”
Claim 4 further recites “and wherein the circuitry is further configured to extract data corresponding to the first chewing information and data corresponding to the second chewing information from the chewing information acquired, to determine the attribute of the food having been chewed by the user.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” Farooq page 154 right column last paragraph discloses:
The jaw motion sensor signals were divided into non-overlapping segments of 30s called epochs. This epoch size helps to detect small food intake events such as snacking. Time and frequency domain features were computed for each epoch of the jaw motion sensor signal (Table 1).
The computer features are information extracted from the raw chewing information acquired.
As discussed above, Vleugels is cited regarding the determined attribute of the food.
Claim 12 further recites “12. The jaw movement analysis system according to claim 1, further comprising: one or more processors including the circuitry, wherein the circuitry includes a storage storing the training data, and wherein the one or more processors are further configured to generate the analysis model from the training data.” Farooq abstract discloses “Machine Learning applications.” Machine learning requires a computer including a processor and memory. Farooq page 154 left column seventh paragraph discloses “Android phone which worked as a data logger.” Logging data is storing respective training data.
Claim 13 further recites “13. The jaw movement analysis system according to claim 1, wherein the attribute determined includes at least one attribute selected from the group consisting of: a size, a hardness, and a type of the food having been chewed.” From the above list of alternatives the Examiner is selecting “a type.”
Farooq page 157 left column last paragraph discloses “Additional sensors such as a camera with computer vision algorithms can be used to estimate portion size and the contents of the meals.” Estimating a portion size is articulating a desire for a future capability to determine a size attribute of the food. Farooq does not explicitly disclose determine an attribute of the food chewed by the user; however, in analogous art of dietary monitoring, Huang page 709 section IV teaches:
Meal composition is an important dimension of dietary monitoring, …. The goal of this paper is to recognize the broad food category. ….
A decision tree-based classifier is used for classification, ….
…
For a chewing sequence, chews in the sequence may be classified with different labels. Since food content is unlikely to change within a bite, similar to [2], a majority vote is performed over a chewing sequence to decide on its food content.
Determining food content for a chewing sequence corresponds with determining an attribute of the food itself being chewed. The decision tree classification of food content is the second information corresponding with a class label as combined with Farooq. The food content corresponds with identifying a type of food.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq and Huang. One having ordinary skill in the art would have found motivation to use determining food content into the system of food intake detection for the advantageous purpose of estimating portion size and contents of the meals. See Farooq page 157 left column last paragraph. Furthermore, Huang page 709 section IV recognizes “Meal composition is an important dimension of dietary monitoring, which is of great value for chronic disease prevention and diagnosis.”
Claim 14 recites “14. A non-transitory storage for jaw movement analysis, the non-transitory storage comprising processor-readable data and instructions.” Farooq abstract discloses “Machine Learning applications.” Machine learning requires a computer including a processor and memory.
Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.”
Claim 14 further recites “to: acquire chewing information including time-series information that represents a jaw movement of a user chewing a bite of food.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” Farooq page 154 right column last paragraph discloses:
The jaw motion sensor signals were divided into non-overlapping segments of 30s called epochs. This epoch size helps to detect small food intake events such as snacking. Time and frequency domain features were computed for each epoch of the jaw motion sensor signal (Table 1).
Jaw motion sensor signals in 30 second segments correspond with acquired time-series chewing information representing jaw movement.
Claim 14 further recites “and determine an attribute of a food having been chewed by the user based on the chewing information acquired and based on an analysis model.” The classifier taught by Farooq determines whether an epoch is “no food intake” or “food intake”. Farooq page 155 left column lines 1-2. However, this is a classification of the epoch and not an attribute of the food itself.
Farooq page 157 left column last paragraph discloses “Additional sensors such as a camera with computer vision algorithms can be used to estimate portion size and the contents of the meals.” Estimating a portion size is articulating a desire for a future capability to determine a size attribute of the food. Farooq does not explicitly disclose determine an attribute of the food chewed by the user; however, in analogous art of dietary monitoring, Huang page 709 section IV teaches:
Meal composition is an important dimension of dietary monitoring, …. The goal of this paper is to recognize the broad food category. ….
A decision tree-based classifier is used for classification, ….
…
For a chewing sequence, chews in the sequence may be classified with different labels. Since food content is unlikely to change within a bite, similar to [2], a majority vote is performed over a chewing sequence to decide on its food content.
Determining food content for a chewing sequence corresponds with determining an attribute of the food itself being chewed.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq and Huang. One having ordinary skill in the art would have found motivation to use determining food content into the system of food intake detection for the advantageous purpose of estimating portion size and contents of the meals. See Farooq page 157 left column last paragraph. Furthermore, Huang page 709 section IV recognizes “Meal composition is an important dimension of dietary monitoring, which is of great value for chronic disease prevention and diagnosis.”
Claim 14 further recites “wherein the analysis model is generated by machine learning from training data including first information and second information.” Farooq page 155 left column section C “Artificial Neural Network (ANN)” discloses:
A three layered (input layer, hidden layer and output layer) feed-forward neural network with back-propagation training algorithm was used for food intake classification. The proposed ANN architecture consisted of an input layer with 38 input neurons (one for each predictor), one hidden layer with N hidden neurons and one output layer with one output neuron. N ranged from 1 to 10 neurons to obtain the optimum ANN classifier.
An artificial neural network (ANN) is a machine learning analysis model. Back-propagation corresponds with training the ANN.
Claim 14 further recites “wherein the first information includes time-series information indicating a past jaw movement during a chewing of a bite of food.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” The time domain and frequency domain features correspond with first information time-series information.
Claim 14 further recites “and wherein the second information indicates an attribute of the food chewed during the past jaw movement associated with the first information.” Farooq page 155 left column first line discloses “Each feature vector fi was associated with a class label ci.” The class label corresponds with second information indicating an attribute of the food associated with the first information. The training data set with included class labels is past jaw movement data.
Farooq page 157 left column last paragraph discloses “Additional sensors such as a camera with computer vision algorithms can be used to estimate portion size and the contents of the meals.” Estimating a portion size is articulating a desire for a future capability to determine a size attribute of the food. Farooq does not explicitly disclose determine an attribute of the food chewed by the user; however, in analogous art of dietary monitoring, Huang page 709 section IV teaches:
Meal composition is an important dimension of dietary monitoring, …. The goal of this paper is to recognize the broad food category. ….
A decision tree-based classifier is used for classification, ….
…
For a chewing sequence, chews in the sequence may be classified with different labels. Since food content is unlikely to change within a bite, similar to [2], a majority vote is performed over a chewing sequence to decide on its food content.
Determining food content for a chewing sequence corresponds with determining an attribute of the food itself being chewed. The decision tree classification of food content is the second information corresponding with a class label as combined with Farooq.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq and Huang. One having ordinary skill in the art would have found motivation to use determining food content into the system of food intake detection for the advantageous purpose of estimating portion size and contents of the meals. See Farooq page 157 left column last paragraph. Furthermore, Huang page 709 section IV recognizes “Meal composition is an important dimension of dietary monitoring, which is of great value for chronic disease prevention and diagnosis.”
Claim 15 further recites “15. The non-transitory storage according to claim 14, wherein the chewing information acquired includes first chewing information associated with a first occlusal movement of the jaw movement of the user, and second chewing information associated with a second occlusal movement of the jaw movement.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” Farooq page 154 right column last paragraph discloses:
The jaw motion sensor signals were divided into non-overlapping segments of 30s called epochs. This epoch size helps to detect small food intake events such as snacking. Time and frequency domain features were computed for each epoch of the jaw motion sensor signal (Table 1).
Jaw motion sensor signals in 30 second segments correspond with acquired time-series chewing information representing jaw movement. Each segment of collected jaw motion corresponds with chewing information of respective jaw movements.
Claim 15 further recites “and wherein the processor-readable data and instructions are further configured to extract the first chewing information and the second chewing information from the chewing information acquired, to determine the attribute of the food having been chewed by the user.” Farooq abstract discloses “A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers.” Farooq page 154 right column last paragraph discloses:
The jaw motion sensor signals were divided into non-overlapping segments of 30s called epochs. This epoch size helps to detect small food intake events such as snacking. Time and frequency domain features were computed for each epoch of the jaw motion sensor signal (Table 1).
The computer features are information extracted from the raw chewing information acquired.
As discussed above, Vleugels is cited regarding the determined attribute of the food.
Dependent claim 18 is substantially similar to claim 13 above and is rejected for the same reasons.
Dependent claim 19 is substantially similar to claim 2 above and is rejected for the same reasons.
Dependent Claims 3 and 20
Claims 3 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Farooq and Huang as applied to claim 1 above, and further in view of US patent 10,373,716 B2 Vleugels, et al. [herein “Vleugels”].
Claim 3 further recites “3. The jaw movement analysis system according to claim 1, wherein the past jaw movement is a past jaw movement performed by an unspecified user.” Farooq abstract discloses “Data collected from an independent subject in a separate study were used to evaluate the performance of these classifiers.” An independent subject corresponds with an unspecified user.
Claim 3 further recites “wherein the training data further includes profile information of the unspecified user.” Farooq does not explicitly disclose profile information; however, in analogous art of tracking food intake, Vleugels column 33 lines 17-23 teaches:
feedback may include, but are not limited to, food content and nutritional information, historical data summaries, over views of one or more tracked parameters over an extended period of time, progress of one or more tracked parameters, personalized dietary coaching and advice, benchmarking of one or more tracked parameters against peers or other users with similar profile.
Using benchmarking of parameters against other users with similar profile corresponds with using profile information of unspecified users.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq, Huang, and Vleugels. One having ordinary skill in the art would have found motivation to use comparison against peers into the system of food intake detection for the advantageous purpose “to make specific recommendations to user.” See Huang column 19 lines 57-64.
Claim 3 further recites “wherein the circuitry is further configured to acquire the profile information indicating an attribute of the user, and wherein the attribute of the food is determined additionally based on the profile information acquired.” Vleugels column 33 lines 17-23 teaches:
feedback may include, but are not limited to, food content and nutritional information, historical data summaries, over views of one or more tracked parameters over an extended period of time, progress of one or more tracked parameters, personalized dietary coaching and advice, benchmarking of one or more tracked parameters against peers or other users with similar profile.
Using benchmarking of parameters against other users with similar profile corresponds with using profile information of unspecified users. The tracked parameters correspond with attributes of the user(s). Feedback on the food content and nutritional information and/or benchmarks corresponds with attributes of the food determined at least in part on the similar profile of the respective user. Vleugels column 36 lines 62-64 teach “tracking at least one parameter that is directly related to food intake and/or eating behavior.” Accordingly, at least one tracked parameter is directly related to the food.
Dependent claim 20 is substantially similar to claim 3 above and is rejected for the same reasons.
Dependent Claims 5-9, 11, 16, and 17
Claims 5-9, 11, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Farooq and Huang as applied to claims 1 and 14 above, and further in view of US patent 11,234,644 B2 Kochura, et al. [herein “Kochura”].
Claim 5 further recites “5. The jaw movement analysis system according to claim 1, wherein the chewing information is acquired based on jaw movement information detected by a sensor mounted on a lower jaw denture of the user.” Farooq does not explicitly disclose a denture; however, in analogous art of health monitoring, Kochura column 4 lines 6-12 teach:
Other embodiments of the present invention utilize one or more sensors embedded within a dental device, such as dentures or replacement teeth attached to dental implants. In addition, sensors embedded within a dental device can acquire more direct monitoring information about the temperature and consistency of items consumed by a user
Dentures with embedded sensors correspond with a sensor mounted on a lower jaw denture of a user.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq, Huang, and Kochura. One having ordinary skill in the art would have found motivation to use sensors embedded within a denture into the system of food intake detection for the advantageous purpose of collecting direct monitoring information from a user. See Kochura column 4 lines 6-12.
Claim 6 further recites “6. The jaw movement analysis system according to claim 5, wherein the jaw movement information includes information indicating a temporal change in at least one detected property selected from the group consisting of: an acceleration in triaxial directions and an angular velocity in the triaxial directions.” From the above list of alternatives the Examiner is selecting “an acceleration in triaxial directions.”
Farooq page 154 left column section II(A) item (c) discloses “c) A tri-axial accelerometer located in the wireless module to detect body acceleration.” A tri-axial accelerometer corresponds with detecting acceleration in triaxial directions. Farooq page 154 figure 2 shows the accelerometer signals are over time.
Claim 7 further recites “7. The jaw movement analysis system according to claim 6, wherein the jaw movement information further includes additional information indicating a temporal change in at least one property selected from an acceleration in the triaxial directions and the angular velocity in the triaxial directions.” From the above list of alternatives the Examiner is selecting “an acceleration in the triaxial directions.”
Farooq page 154 left column section II(A) item (c) discloses “c) A tri-axial accelerometer located in the wireless module to detect body acceleration.” A tri-axial accelerometer corresponds with detecting acceleration in triaxial directions. Farooq page 154 figure 2 shows the accelerometer signals are over time.
Claim 7 further recites “detected by a sensor mounted on an upper jaw denture of the user.” Farooq does not explicitly disclose a denture; however, in analogous art of health monitoring, Kochura column 4 lines 6-12 teach:
Other embodiments of the present invention utilize one or more sensors embedded within a dental device, such as dentures or replacement teeth attached to dental implants. In addition, sensors embedded within a dental device can acquire more direct monitoring information about the temperature and consistency of items consumed by a user
Dentures with embedded sensors correspond with a sensor mounted on a lower jaw denture of a user.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq, Huang, and Kochura. One having ordinary skill in the art would have found motivation to use sensors embedded within a denture into the system of food intake detection for the advantageous purpose of collecting direct monitoring information from a user. See Kochura column 4 lines 6-12.
Claim 8 further recites “8. The jaw movement analysis system according to claim 5, further comprising: a base device configured to store the denture, wherein the base device is further configured to acquire the jaw movement information from the sensor.” Farooq page 154 left column section II(A) sixth paragraph discloses “Sensor signals were captured by the wireless module at a sampling frequency of 1 kHz and were transmitted via Bluetooth in near real time to an Android phone which worked as a data logger.” Transmitting the sensor signals wirelessly to the logger corresponds with acquiring the jaw movement information from the sensors with the logger. Without loss of generality, the data logger or receiver corresponds with a base station device for the wireless module.
Farooq does not explicitly disclose a denture; however, in analogous art of health monitoring, Kochura column 4 lines 6-12 teach:
Other embodiments of the present invention utilize one or more sensors embedded within a dental device, such as dentures or replacement teeth attached to dental implants. In addition, sensors embedded within a dental device can acquire more direct monitoring information about the temperature and consistency of items consumed by a user
Dentures with embedded sensors correspond with a sensor mounted on a lower jaw denture of a user. Any container sized to store the denture is a base device capable of storing the denture. Furthermore, Kochura column 20 line 61 teaches “a charger” for the medical monitoring device.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq, Huang, and Kochura. One having ordinary skill in the art would have found motivation to use sensors embedded within a denture into the system of food intake detection for the advantageous purpose of collecting direct monitoring information from a user. See Kochura column 4 lines 6-12.
Claim 9 further recites “9. The jaw movement analysis system according to claim 8, wherein the base device further includes a charger configured to charge the sensor.” Farooq does not explicitly disclose a denture; however, in analogous art of health monitoring, Kochura column 4 lines 6-12 teach:
Other embodiments of the present invention utilize one or more sensors embedded within a dental device, such as dentures or replacement teeth attached to dental implants. In addition, sensors embedded within a dental device can acquire more direct monitoring information about the temperature and consistency of items consumed by a user
Dentures with embedded sensors correspond with a sensor mounted on a lower jaw denture of a user. Any container sized to store the denture is a base device capable of storing the denture. Furthermore, Kochura column 20 line 61 teaches “a charger” for the medical monitoring device.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq, Huang, and Kochura. One having ordinary skill in the art would have found motivation to use sensors embedded within a denture into the system of food intake detection for the advantageous purpose of collecting direct monitoring information from a user. See Kochura column 4 lines 6-12.
Claim 11 further recites “11. The jaw movement analysis system according to claim 1, further comprising: a sensor mounted on a denture of the user to detect jaw movement information, wherein the chewing information is acquired based on the jaw movement information detected.” Farooq does not explicitly disclose a denture; however, in analogous art of health monitoring, Kochura column 4 lines 6-12 teach:
Other embodiments of the present invention utilize one or more sensors embedded within a dental device, such as dentures or replacement teeth attached to dental implants. In addition, sensors embedded within a dental device can acquire more direct monitoring information about the temperature and consistency of items consumed by a user
Dentures with embedded sensors correspond with a sensor mounted on a lower jaw denture of a user.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq, Huang, and Kochura. One having ordinary skill in the art would have found motivation to use sensors embedded within a denture into the system of food intake detection for the advantageous purpose of collecting direct monitoring information from a user. See Kochura column 4 lines 6-12.
Claim 16 further recites “16. The non-transitory storage according to claim 14, wherein the chewing information is acquired based on jaw movement information detected by a sensor mounted on a denture of the user.” Farooq does not explicitly disclose a denture; however, in analogous art of health monitoring, Kochura column 4 lines 6-12 teach:
Other embodiments of the present invention utilize one or more sensors embedded within a dental device, such as dentures or replacement teeth attached to dental implants. In addition, sensors embedded within a dental device can acquire more direct monitoring information about the temperature and consistency of items consumed by a user
Dentures with embedded sensors correspond with a sensor mounted on a lower jaw denture of a user.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq, Huang, and Kochura. One having ordinary skill in the art would have found motivation to use sensors embedded within a denture into the system of food intake detection for the advantageous purpose of collecting direct monitoring information from a user. See Kochura column 4 lines 6-12.
Claim 17 further recites “17. The non-transitory storage according to claim 16, wherein the jaw movement information includes information indicating a temporal change in at least one detected property selected from the group consisting of: an acceleration in triaxial directions and an angular velocity in the triaxial directions between an upper jaw and a lower jaw of the user.” From the above list of alternatives the Examiner is selecting “an acceleration in triaxial directions.”
Farooq page 154 left column section II(A) item (c) discloses “c) A tri-axial accelerometer located in the wireless module to detect body acceleration.” A tri-axial accelerometer corresponds with detecting acceleration in triaxial directions. Farooq page 154 figure 2 shows the accelerometer signals are over time.
Dependent Claim 10
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Farooq, Huang, and Kochura as applied to claim 8 above, and further in view of Duyck, J., et al. “Impact of Denture Cleaning Method and Overnight Storage Condition on Denture Biofilm Mass and Composition: A Cross-Over Randomized Clinical Trial” PLoS ONE, vol. 11, issue 1, e0145837 (2016) [herein “Duyck”].
Claim 10 further recites “10. The jaw movement analysis system according to claim 8, wherein the base device further includes a cleaner configured to clean the denture.” Farooq does not explicitly disclose denture cleaning; however, in analogous art of denture cleaning methods, Duyck abstract background teaches “to compare the role of denture cleaning methods in combination with overnight storage.”
Duyck page 1 abstract results teach “Overnight denture storage in water with a cleansing tablet significantly reduced the total bacterial count.” Denture storage with a cleansing tablet corresponds with storing the denture in a device which includes a cleaner. The cleansing tablet is a cleaner to clean the denture.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Farooq, Huang, Kochura, and Duyck. One having ordinary skill in the art would have found motivation to use overnight storage with a cleansing tablet into the system of denture with embedded sensors for the advantageous purpose of “a decrease of total bacterial load and of specific bacteria.” See Duyck page 13 conclusion.
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Farooq, M. & Sazonov, E. “A Novel Wearable Device for Food Intake and Physical Activity Recognition” Sensors, vol. 16, 1067 (2016)
teaches
Abstract: a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses
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/Jay Hann/Primary Examiner, Art Unit 2186 21 January 2026