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 .
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. (PBA)
DETAILED ACTION
Preliminary Amendment
Preliminary Amendment filed on 06/03/2024 noted by the examiner, claims 1-15, 21-22, 28, 30, 32-33 and 35 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15, 21-22, 28, 30, 32-33 and 35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a system comprising: at least one sensing point, each sensing point comprising at least one sensor configured to provide sensor data about the food collected by the user and a reader configured to read the identifier associated with the food collecting session of the user; obtain the weighing result and the identifier from each food serving point; and associate weighing results having the same identifier with each other; a training system configured to
obtain sensor data originating from the at least one sensing point, the sensor data being associated with the identifier; obtain the weighing results associated with the identifier and the data relating to food served in the plurality of food serving points from the data system; and use the obtained sensor data associated with the identifier, the weighing results associated with the identifier and the data relating to food served in the plurality of food serving points as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of a plurality of food serving points configured to serve food, each food serving point being configured to serve a predetermined dish, each food serving point being associated with a weighing device configured to weigh the amount of the food collected from the food serving point to provide a weighing result, and a reader configured to read an identifier associated with a food collecting session of a user are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional elements of a data system configured to store data relating to food served in each of the plurality of food serving points appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 not eligible.
Claim 15, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a computer-implemented method comprising: obtaining sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier, obtaining weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point; obtaining data relating to food served in the plurality of food serving points appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of using at least the sensor data, the weighing results and the data relating to food served in the plurality of food serving points as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? there is no more additional elements.
Step 2B: No. claim 15 not eligible.
Claim 22, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to an apparatus comprising: obtain sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier; obtain weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point; obtain data relating to food served in the plurality of food serving points appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of use at least the sensor data, the weighing results and the data relating to food served in the plurality of food serving points as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional elements of at least one processor; and at least one memory including computer program code,
the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 22 not eligible.
Claim 28, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a computer-implemented method comprising: receiving sensor data associated with food collected by a user; and applying a trained machine learning model to classify the food collected by the user based on the sensor data, the trained machine learning model being obtained by obtaining sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier; obtaining weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point; obtaining data relating to food served in the plurality of food serving points appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of using at least the sensor data, the weighing results and the data as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the are no more additional elements.. Step 2B: No. claim 28 not eligible.
Claim 30, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to an apparatus comprising: receive sensor data associated with food collected by a user; and apply a trained machine learning model to classify the food collected by the user based on the sensor data, the trained machine learning model being obtained by obtaining sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier; obtaining weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point; obtaining data relating to food served in the plurality of food serving points appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of using at least the sensor data, the weighing results and the data as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional elements of at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 30 not eligible.
Claim 33, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a system comprising: a sensing point comprising at least one sensor configured to provide sensor data about food collected by a user;
a control unit configured to control the sensing point; and
an analyzing unit configured to configured apply a trained model to classify the food collected by the user based at least on the sensor data to provide an estimation and/or a classification of the food taken by the user appears
is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? there are no more additional elements.
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? there are no more additional elements
Step 2B: No. claim 33 not eligible.
Claim 35, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a system comprising: a sensing point comprising at least one sensor configured to provide sensor data about food left by a user; a control unit configured to control the sensing point; and an analyzing unit configured to configured apply a trained model to classify the food left by the user based at least on the sensor data to provide an estimation and/or a classification of the food left by the user appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? there are no more additional elements.
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? there are no more additional elements
Step 2B: No. claim 35 not eligible.
Claim 2 related to comprising a control unit configured to: receive a trigger event; and trigger storing of n sensor data of the food collected by the user with the at least one sensor, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 2 not eligible.
Claim 3 related to wherein the control unit is configured to receive the trigger event from the reader, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 3 not eligible.
Claim 4 related to a control unit and a sensing point at each of the plurality of food serving points, the control unit of a food serving point being configured to: receive a trigger event; and trigger storing of sensor data associated with the food collected by the user with the at least one sensor of the sensing point, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 4 not eligible.
Claim 5 related to wherein the control unit is configured to receive the trigger event from the reader associated with the food serving point, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 5 not eligible.
Claim 6 related to wherein the control unit is configured to receive the trigger event from the weighing device associated with the food serving point, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 6 not eligible.
Claim 7 related to wherein the data relating to food served in the plurality of food serving points comprises at least one of the food served by each of the plurality of food serving points; and nutrient content information associated with each food served by each of the plurality of food serving points, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 7 not eligible.
Claim 8 related to wherein the data system is configured to: generate a session having a session identifier when obtaining the identifier for the first time and determining that there does not exist an active session; associate a session start time with the session; and link the identifier with the session having the session identifier, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 8 not eligible.
Claim 9 related to user identifier reader, and the data system is configured to: obtain a user identifier from the user identifier reader; and associate the user identifier with the session, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 9 not eligible.
Claim 10 related to wherein the identifier comprises a radio frequency identifier, a near filed communication identifier, a bar code, a QR code or a visually recognizable identifier associated with a tray used by the user, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 10 not eligible.
Claim 11 related to wherein the identifier comprises an identifier, a radio frequency identifier, a near filed communication identifier, a smart wearable identifier, a smart ring identifier, a fingerprint, a biometric identifier and a visually recognizable identifier associated with the user, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 11 not eligible.
Claim 12 related to comprising a waste collecting point comprising:a weighing device configured to weigh the amount of biowaste left by the user to provide a waste weighing result; a reader configured to read the food collecting session identifier associated with the food collecting session of the user; and at least one sensor configured to provide sensor data about the food left by the user, wherein the training system is configured to: obtain the waste weighing result, the food collecting session identifier associated with the food collecting session of the user and the sensor data about the food left by the user; and provide based on the obtained waste weighing result, the food collecting session identifier associated with the food collecting session of the user and the sensor data about the food left by the user additional information about the food left by the user, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 12 not eligible.
Claim 13 related to wherein the at least one sensor comprise at least one of a camera, a stereo camera, a depth camera, a multispectral camera, a hyperspectral camera, an infrared camera, a RGB camera, an ultraviolet camera a spectroscopy sensor, a near infrared sensor, a spectroscopy sensor, a photogrammetry sensor, a lidar sensor, a three-dimensional scanner and a photodetector, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 13 not eligible.
Claim 14 related to wherein the training system is configured to: obtain additional sensor data and manually labeled data associated with the additional sensor data; and use the obtained additional sensor data and manually labeled data as training data for the machine learning algorithm to complement the model, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 14 not eligible.
Claim 21 related to computer program comprising instructions for causing an apparatus to perform the method, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 21 not eligible.
Claim 32 related to computer program comprising instructions for causing an apparatus to perform the method, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 32 not eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-15, 21-22, 28, 30, 32-33 and 35 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Divakaran et al. (US Patent Application Publication 2016/0063734 A1, Date Published: 2016-03-03)
Regarding claim 1:
Divakaran described a system comprising:
a plurality of food serving points configured to serve food, each food serving point being configured to serve a predetermined dish, each food serving point being associated with a weighing device configured to weigh the amount of the food collected from the food serving point to provide a weighing result, and a reader configured to read an identifier associated with a food collecting session of a user (0019, many users, 0029, plate of food taken by a mobile device, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant);
at least one sensing point, each sensing point comprising at least one sensor configured to provide sensor data about the food collected by the user and a reader configured to read the identifier associated with the food collecting session of the user (0029, food identification models);
a data system configured to
store data relating to food served in each of the plurality of food serving points;
obtain the weighing result and the identifier from each food serving point; and
associate weighing results having the same identifier with each other (0029, food identification models, fig. 1, 104); a training system configured to
obtain sensor data originating from the at least one sensing point, the sensor data being associated with the identifier; obtain the weighing results associated with the identifier and the data relating to food served in the plurality of food serving points from the data system (0039, training set of food images); and
use the obtained sensor data associated with the identifier, the weighing results associated with the identifier and the data relating to food served in the plurality of food serving points as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food (0043, through machine learning. Also, due to the many different variations of food around the world, further food identification accuracy gains can be realized by organizing customized food databases locally).
Regarding claim 15:
Divakaran described a computer-implemented method comprising (abstract):
obtaining sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier (0019, many users, 0029, plate of food taken by a mobile device, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant);
obtaining weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point (0029, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant);
obtaining data relating to food served in the plurality of food serving points; and
using at least the sensor data, the weighing results and the data relating to food served in the plurality of food serving points as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food (0043, through machine learning. Also, due to the many different variations of food around the world, food served by a particular ethnic restaurant,further food identification accuracy gains can be realized by organizing customized food databases locally).
Regarding claim 22:
Divakaran described an apparatus comprising (fig. 1):
at least one processor (0017, computer vision); and
at least one memory including computer program code,
the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to (0017, computer system vision-enabled, 0063, computerized programs, routines, logic and/or instructions executed by the computing system):
obtain sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier;
obtain weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point (0019, many users, 0029, plate of food taken by a mobile device, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant);
obtain data relating to food served in the plurality of food serving points; and
use at least the sensor data, the weighing results and the data relating to food served in the plurality of food serving points as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food (0043, through machine learning. Also, due to the many different variations of food around the world, further food identification accuracy gains can be realized by organizing customized food databases locally).
Regarding claim 28:
Divakaran described a computer-implemented method comprising (fig.1 computer, 0017, computer vision):
receiving sensor data associated with food collected by a user; and
applying a trained machine learning model to classify the food collected by the user based on the sensor data, the trained machine learning model being obtained by obtaining sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier (0019, many users, 0029, plate of food taken by a mobile device, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant, 0029, food identification models);
obtaining weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point; obtaining data relating to food served in the plurality of food serving points (0019, many users, 0029, plate of food taken by a mobile device, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant); and
using at least the sensor data, the weighing results and the data as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food (0043, through machine learning. Also, due to the many different variations of food around the world, food served by a particular ethnic restaurant, further food identification accuracy gains can be realized by organizing customized food databases locally).
Regarding claim 30:
Divakaran described an apparatus comprising:
at least one processor (fig. 1, computer, 0017, computer vision); and
at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to (0063, computerized programs, routines, logic and/or instructions executed by the computing system):
receive sensor data associated with food collected by a user; and
apply a trained machine learning model to classify the food collected by the user based on the sensor data, the trained machine learning model being obtained by obtaining sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier (0019, many users, 0029, plate of food taken by a mobile device, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant, 0029, food identification models);
obtaining weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point; obtaining data relating to food served in the plurality of food serving points (0019, many users, 0029, plate of food taken by a mobile device, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant, 0029, food identification models); and
using at least the sensor data, the weighing results and the data as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food (0043, through machine learning. Also, due to the many different variations of food around the world, food served by a particular ethnic restaurant, further food identification accuracy gains can be realized by organizing customized food databases locally).
Regarding claim 33:
Divakaran described a system comprising (abstract):
a sensing point comprising at least one sensor configured to provide sensor data about food collected by a user (0019, many users, 0029, plate of food taken by a mobile device, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant, 0029, food identification models);
a control unit configured to control the sensing point; and
an analyzing unit configured to configured apply a trained model to classify the food collected by the user based at least on the sensor data to provide an estimation and/or a classification of the food taken by the user (0043, through machine learning. Also, due to the many different variations of food around the world, food served by a particular ethnic restaurant, further food identification accuracy gains can be realized by organizing customized food databases locally).
Regarding claim 35:
Divakaran described a sensing point comprising at least one sensor configured to provide sensor data about food left by a user; a control unit configured to control the sensing point (0019, many users, 0029, plate of food taken by a mobile device, food identification models, 0043, foods that are specific to particular geographic locations food served by a particular ethnic restaurant, 0029, food identification models); and
an analyzing unit configured to configured apply a trained model to classify the food left by the user based at least on the sensor data to provide an estimation and/or a classification of the food left by the user (0043, through machine learning. Also, due to the many different variations of food around the world, food served by a particular ethnic restaurant, further food identification accuracy gains can be realized by organizing customized food databases locally).
.
. Regarding claim 2, Divakaran further described receive a trigger event; and trigger storing of n sensor data of the food collected by the user with the at least one sensor (0043, food classification, food recognition system).
Regarding claim 3, Divakaran further described the control unit is configured to receive the trigger event from the reader (fig.1, 104, 0040, training a supervised learning model such as a multiple class support vector machine).
Regarding claim 4, Divakaran further described to a control unit and a sensing point at each of the plurality of food serving points, the control unit of a food serving point being configured to: receive a trigger event; and trigger storing of sensor data associated with the food collected by the user with the at least one sensor of the sensing point user (0043, through machine learning. Also, due to the many different variations of food around the world, food served by a particular ethnic restaurant, further food identification accuracy gains can be realized by organizing customized food databases locally).
Regarding claim 5, Divakaran further described wherein the control unit is configured to receive the trigger event from the reader associated with the food serving point (0043, through machine learning. Also, due to the many different variations of food around the world, food served by a particular ethnic restaurant, further food identification accuracy gains can be realized by organizing customized food databases locally).
Regarding claim 6, Divakaran further described wherein the control unit is configured to receive the trigger event from the weighing device associated with the food serving point (0043, through machine learning. Also, due to the many different variations of food around the world, food served by a particular ethnic restaurant, further food identification accuracy gains can be realized by organizing customized food databases locally).
Regarding claim 7, Divakaran further described wherein the data relating to food served in the plurality of food serving points comprises at least one of the food served by each of the plurality of food serving points; and nutrient content information associated with each food served by each of the plurality of food serving points (0057, can estimate the calories and the nutritional value of the foods).
Regarding claim 8, Divakaran further described generate a session having a session identifier when obtaining the identifier for the first time and determining that there does not exist an active session; associate a session start time with the session; and link the identifier with the session having the session identifier (0045, time stamp associated with the image, 0076, can be real-time analysis)
Regarding claim 9, Divakaran further described identifier reader, and the data system is configured to: obtain a user identifier from the user identifier reader; and associate the user identifier with the session (0045, time stamp associated with the image, 0076, can be real-time analysis).
Regarding claim 10, Divakaran further described a radio frequency identifier, a near filed communication identifier, a bar code, a QR code or a visually recognizable identifier associated with a tray used by the user (0040, visually different food items may all be classified as “sandwiches.” ).
Regarding claim 11, Divakaran further described comprises an identifier, a radio frequency identifier, a near filed communication identifier, a smart wearable identifier, a smart ring identifier, a fingerprint, a biometric identifier and a visually recognizable identifier associated with the user (0040, visually different food items may all be classified as “sandwiches.” ).
Regarding claim 12, Divakaran further described a weighing device configured to weigh the amount of biowaste left by the user to provide a waste weighing result; a reader configured to read the food collecting session identifier associated with the food collecting session of the user; and at least one sensor configured to provide sensor data about the food left by the user, wherein the training system is configured to: obtain the waste weighing result, the food collecting session identifier associated with the food collecting session of the user and the sensor data about the food left by the user; and provide based on the obtained waste weighing result, the food collecting session identifier associated with the food collecting session of the user and the sensor data about the food left by the user additional information about the food left by the user (0043, through machine learning. Also, due to the many different variations of food around the world, food served by a particular ethnic restaurant, further food identification accuracy gains can be realized by organizing customized food databases locally, 0076, real-time manner).
Regarding claim 13, Divakaran further described at least one of a camera (0076, a wearable video camera), a stereo camera, a depth camera, a multispectral camera, a hyperspectral camera, an infrared camera, a RGB camera, an ultraviolet camera a spectroscopy sensor, a near infrared sensor, a spectroscopy sensor, a photogrammetry sensor, a lidar sensor, a three-dimensional scanner and a photodetector
Regarding claim 14, Divakaran further described obtain additional sensor data and manually labeled data associated with the additional sensor data; and use the obtained additional sensor data and manually labeled data as training data for the machine learning algorithm to complement the model (0019, updating a user's food log, or to tag the image to facilitate searching, retrieval, or for establishing links between the image 102 and other electronic content.)
Regarding claim 21, Divakaran further described obtain computer program comprising instructions for causing an apparatus to perform the method (0063, computerized programs, routines, logic and/or instructions executed by the computing system ).
Regarding claim 32, Divakaran further described to computer program comprising instructions for causing an apparatus to perform the method (0063, computerized programs, routines, logic and/or instructions executed by the computing system ).
Contact information
4. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TURNER SHELBY, can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUNG S LAU/Primary Examiner, Art Unit 2857
Technology Center 2800
July 15, 2026
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