DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending of which claims 1, 11 and 16 are in independent form.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claim(s) 1, 2, 10, 11, 12, and 16-18 are rejected under 35 U.S.C. 102(a)(2).
Claim(s) 3-9, 13-15, 19 and 20 is/are rejected under 35 U.S.C. 103.
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 1-20 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.
The claim(s) recite(s) capturing images and performing image analysis using artificial intelligence models.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter.
Independent Claims 1 and 16 is directed to an apparatus, including one or more processors and memory, which is a machine, and directed to one of the 4 categories of patent eligible subject matter.
Independent claim 11 is directed to a method, which is a process. All other claims depend on claims 1, 11 and 16. As such, claims 1-20 are directed to a statutory category.
With respect to step 2A, Prong One, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claim recites the following limitations directed to an abstract idea:
Obtaining an image using a camera,
Inputting the image into a trained AI model,
Generating identification information and accuracy information,
Obtaining a second accuracy value,
Comparing the difference between the two accuracy values,
Updating the AI model based on the calculated difference.
These limitation amount to:
Mathematical concept/algorithm (accuracy values, difference calculation values), and
Mental process/information analysis (identifying objects, correctness evaluation, error determination).
Data analysis/mental process and mathematical algorithms/concepts, have been consistently held to be abstract ideas. See Electric Power Group (collecting, analyzing and displaying information is abstract) and Digitech (gathering and manipulating data is abstract).
Accordingly, independent claims are considered abstract.
With respect to step 2A, Prong Two, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
The claims recite:
A camera,
A processor,
An AI model, and
Generic memory/communication components.
These components perform their well understood, routine, and conventions functions, such as:
Capturing an image,
Executing a model,
Producing confidence value, and
Updating model parameters.
The claims do not:
improve function of the camera,
improve computing hardware operation,
provide a specific improvement to the structure or training of the AI model,
recite new data structure or algorithm, or
effect a transformation of an article.
The update step recited is merely functional/result oriented terms (update the AI model based on the difference), without providing any technical details as to how the update is performed or how it improved the computer operation. Providing generic computer implementation is simply insufficient and claims reciting results without technical solutions remain abstract.
Therefore, the claims do NOT integrate the abstract idea into a practical application.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to apparatus, with memory, and processor, and method at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition, the published instant specification describe generic off‐the‐shelf computer‐based elements for implementing the claimed invention, which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Regarding claims 2, 12 and 18,
The claim recites:
the second accuracy information indicates a reference degree of recognition used in training.
This merely defines what the second accuracy information represents, this is still a mathematical algorithm (calculating confidence value) used as a reference parameter in AI model training. There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 3, 13 and 19,
The claim recites:
memory storing the AI model,
a communication interface,
sending signals to the server based on the threshold analysis,
updating the AI model based on the server information.
All additions are generic computer components (memory, comm. Interface, server) performing routine functions of data storing, sending signals, and receiving data. These are well understood, routine and conventional activities. There are no improvements to networking, transmission protocol or data structure.
Generic data communication does not integrate the abstract idea into a practical application. The claims are still considered abstract.
Regarding claims 4 and 14,
The claim recites:
a display,
a UI guiding the user to trigger updates,
display control.
Adding a UI, display, or using input is considered extra-solution activity and insufficient for integration. The user interface that merely display results or transmit a user instruction do not add any technological improvement.
UI elements are insignificantly extra-solution activity. The claims are still considered abstract.
Regarding claims 5 and 15,
The claim recites:
specifies UI components: ID info, accuracy, difference value, update inquiry.
These are data presentations, which is insignificant application and thus abstract idea. Displaying data does not constitute a technological improvement.
Merely specifying what is shown in the UI does not provide a practical application. The claims are still considered abstract.
Regarding claim 6,
The claim recites:
transmitting first accuracy and ID info to server,
receiving a second accuracy info,
storing second accuracy info in memory.
This is merely data transmission, which is routine and conventional. There is no specific protocol, no specific technological improvement such as improved memory structure presented.
Merely moving abstract info around is generic computer environment does not incorporate abstract idea into a practical application. The claim is still considered abstract.
Regarding claim 7,
The claim recites:
if difference is greater or equal to a threshold, then transmitting a request to the server to train the AI model.
Threshold based triggers are abstract idea decision rules. Transmitting a training request is a routine network messaging. There is no technological improvement.
Conditional control does not incorporate abstract idea into a practical application. The claim is still considered abstract.
Regarding claims 8 and 20,
The claim recites:
triggering update based on multiple images being taken more than a first threshold number of times,
requesting a first update information,
updating the AI model based on that information.
Threshold based training triggers a multi-image recognition counts are mathematical data processing/algorithm and generic camera use. This is merely managing when training occurs-a mental process. There is no technological improvement to camera operation or AI architecture.
The claims are still considered abstract.
Regarding claim 9,
The claim recites:
similar to claim 8 but using a second threshold;
requesting a second update information,
updating the AI model based on that information.
Threshold based triggers are abstract idea decision rules. Transmitting a training request is a routine network messaging. There is no technological improvement.
Conditional control does not incorporate abstract idea into a practical application. The claim is still considered abstract.
Regarding claim 10,
The claim recites:
chamber in which the object is disposed,
cameras capture first and second images from different viewpoints,
receiving first and second accuracy values,
calculate and average value.
Chamber is generic, nothing improves its data structure or operation. Multi-view image capture is routine and does not constitute a technological improvement. Computing an average is mathematical concept/algorithm. There is no technological improvement to camera hardware, chamber, optics, or AI model architecture.
The claim merely recites environmental context and mathematical averaging. The claim is still considered abstract.
Regarding claim 17,
The claim recites:
the first accuracy info indicates a likelihood that the object is a plurality of types of food.
This claims merely specifies that content of the accuracy info (type of food).
It adds nothing more than a classification label and a mathematical likelihood, which is data analysis and probability calculation, which are abstract.
There is no technological improvement to camera hardware, AI model, model training, or any computer function.
The claim merely recites classification and mathematical calculation. The claim is still considered abstract.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 10, 11, 12, and 16-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by MAENG; Ji Chan (US 20210182667 A1) [Maeng].
Regarding claims 1, 11 and 16, Maeng discloses, an electronic apparatus comprising: a camera (a camera configured to photograph a cooking material being cooked in the main body ¶ [0015], [0016], [0142]-[0147]);
and at least one processor configured to: obtain, using the camera, an image captured by the camera (a camera configured to photograph a cooking material being cooked in the main body ¶ [0015], [0016], [0142]-[0147]),
input the obtained image to an artificial intelligence model that is trained (The learning processor 160 may train a model configured with an artificial neural network using an image of a cooking material ¶ [0171]. Also see ¶ [0016]-[0019], [0127], [0144], [0147]),
obtain, using the artificial intelligence model, identification information of an object in the image and first accuracy information indicating a degree of recognition of the identification information (Learning rate and accuracy of an artificial neural network may include not only the structure and learning optimization algorithms of the artificial neural network but also the hyperparameters thereof ¶ [0123]-[0124], [0127]. In detail, the camera 120 determines the type of cooking material by applying an object classifier to the image of the surface of the cooking material captured by the RGB camera 121 ¶ [0145]. The object classifier may be trained to predict the cooked state of the cooking material through the captured image of the surface of the cooking material, and may set a recipe for cooking the cooking material differently in accordance with the characteristics of the change in the surface of the cooking material based on the trained model ¶ [0147]), and
update the artificial intelligence model based on a difference value between the obtained first accuracy information and second accuracy information corresponding to the identification information (When a specific operation is performed, the processors analyze history information indicating execution of the specific operation through the data analysis and the machine learning algorithm and technology, and update the information which is previously learned based on the analyzed information ¶ [0194]-[0195]. The memory 230 may store therein a model (or an artificial neural network) that is being trained or has been trained through the learning processor 240. When the model is updated through the learning, the memory 230 may store the updated model therein ¶ [0207]. Also see ¶ [0211]-[0216]),
wherein the second accuracy information indicates a reference degree of recognition associated with the identification information (In addition, the server 200 may evaluate the artificial intelligence model and update the artificial intelligence model for better performance even after the evaluation, and provide the updated artificial intelligence model to the cooking apparatus 100. Here, the cooking apparatus 100 may also perform a series of operations, which are performed by the server 200, alone in a local area or through communication with the server 200. For example, the cooking apparatus 100 may train the AI model to learn a personal pattern of the user through training with the user's personal data, and thereby may update the AI model downloaded from the server 200 ¶ [0215]. Thus, the processors may increase the accuracy of the future performance of data analysis and a machine learning algorithm and scheme based on the updated information along with the learning processor 160 ¶ [0195]. These paragraphs indicate increase in accuracy and updating for improved performance).
Regarding claims 2, 12 and 18, Maeng discloses, wherein the second accuracy information indicates the reference degree of recognition used in a training process of the updated artificial intelligence model (In addition, the server 200 may evaluate the artificial intelligence model and update the artificial intelligence model for better performance even after the evaluation, and provide the updated artificial intelligence model to the cooking apparatus 100. Here, the cooking apparatus 100 may also perform a series of operations, which are performed by the server 200, alone in a local area or through communication with the server 200. For example, the cooking apparatus 100 may train the AI model to learn a personal pattern of the user through training with the user's personal data, and thereby may update the AI model downloaded from the server 200 ¶ [0215]. Thus, the processors may increase the accuracy of the future performance of data analysis and a machine learning algorithm and scheme based on the updated information along with the learning processor 160 ¶ [0195]. These paragraphs indicate increase in accuracy and updating for improved performance).
Regarding claim 10, Maeng discloses, a chamber in which the object is disposed, wherein the at least one processor is further configured to: obtain, using the camera, a first image capturing an inside of the chamber from a first viewpoint, obtain, using the camera, a second image capturing the inside of the chamber from a second viewpoint that is different from the first viewpoint, and obtain, as the first accuracy information, an average value of a first accuracy value of the object corresponding to the first image and a second accuracy value of the object corresponding to the second image (RGB and thermal camera ¶ [0143]. When a specific operation is performed, the processors analyze history information indicating execution of the specific operation through the data analysis and the machine learning algorithm and technology, and update the information which is previously learned based on the analyzed information ¶ [0194]-[0195]. The memory 230 may store therein a model (or an artificial neural network) that is being trained or has been trained through the learning processor 240. When the model is updated through the learning, the memory 230 may store the updated model therein ¶ [0207]. Also see ¶ [0211]-[0216]).
Regarding claim 17, Maeng discloses, wherein the first accuracy information indicates a likelihood that the object is a plurality of types of food (a food type, a cooking method, and setting information corresponding to a recipe ¶ [0004]. The cooking apparatus 100 may cook a cooking material according to a recipe that is directly inputted by a user who uses the cooking apparatus 100, or alternatively, may be an embedded-system-type apparatus that cooks a cooking material using a wireless communication function ¶ [0046]. Also see ¶ [0145], [0147]).
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.
Claim(s) 3-9, 13-15, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Maeng in view of Shah; Meelap et al. (US 11157723 B1) [Shah].
Regarding claims 3, 13 and 19, Maeng discloses, at least one memory configured to store the artificial intelligence model (In addition, the memory 150 may store therein a model trained by the learning processor 160, to be described below. If necessary, the memory 150 may store the trained model by dividing the model into a plurality of versions depending on a training timing or a training progress ¶ [0167]-[0169], [0207]-[0209]); and
a communication interface configured to communicate with a server (The cooking apparatus 100 according to an embodiment of the present disclosure includes a transceiver 110 so as to enable the server 200, the electronic device 300, and the network 400 to communicate with each other ¶ [0136]. See Fig. 3),
control the communication interface to transmit a signal requesting update information of the artificial intelligence model to the server (the information collection may include sensing information by a sensor, extracting of information stored in the memory 150, or receiving information from other electronic devices, an entity, or an external storage device through a transceiver ¶ [0190]. The cooking apparatus 100 according to an embodiment of the present disclosure includes a transceiver 110 so as to enable the server 200, the electronic device 300, and the network 400 to communicate with each other ¶ [0136]. See Fig. 3), and
based on the update information being received from the server through the communication interface, update the artificial intelligence model based on the received update information (update the artificial intelligence model ¶ [0195], [0215]).
However, Maeng does not explicitly facilitate wherein the at least one processor is further configured to: based on the difference value between the first accuracy information and the second accuracy information being greater than or equal to a threshold value.
Shah discloses, wherein the at least one processor is further configured to: based on the difference value between the first accuracy information and the second accuracy information being greater than or equal to a threshold value (At operation 506, based on an identification of an inaccuracy of the lightweight model (e.g., based on a determination that the accuracy of the facial recognition by the lightweight is below a configurable accuracy threshold), the lightweight model may be retrained on the server using training data selected to improve the accuracy of the lightweight model. In example embodiments, for images that have been pre-processed by the client device, the server may request the original images from the client for performing the retraining [col. 9, ll. 54-63]).
It would have been obvious to one ordinary skilled in the art at the time of the filing of the present invention to combine the teachings of the cited references because Shah’s system would have allowed Maeng to facilitates wherein the at least one processor is further configured to: based on the difference value between the first accuracy information and the second accuracy information being greater than or equal to a threshold value. The motivation to combine is apparent in the Maeng's reference, because there is a need to improve architecture for systems and methods for using image recognition technology to identify changes and updates.
Regarding claims 4 and 14, the combination of Maeng and Shah discloses, a display (Maeng: The main body 101 of the cooking apparatus further includes a display 104. The display 104 may display a cooking procedure of a cooking material through any of various methods, such as an image, a picture, or the actual cooked state ¶ [0169] and [0221]),
wherein the at least one processor is further configured to: based on the difference value being greater than or equal to the threshold value (Shah: At operation 506, based on an identification of an inaccuracy of the lightweight model (e.g., based on a determination that the accuracy of the facial recognition by the lightweight is below a configurable accuracy threshold), the lightweight model may be retrained on the server using training data selected to improve the accuracy of the lightweight model. In example embodiments, for images that have been pre-processed by the client device, the server may request the original images from the client for performing the retraining [col. 9, ll. 54-63]),
control the display to display a user interface (UI) that guides an update of the artificial intelligence model (Maeng: FIG. 8 is a diagram illustrating an example in which the cooked state of a cooking material is displayed on the cooking apparatus according to another embodiment of the present disclosure while the cooking material is being cooked ¶ [0246], [0196]. The input interface 220 may be a component corresponding to the input interface 130 of the cooking apparatus 100. In detail, the input interface 220 may receive and obtain data on a recipe for a cooking material input to the cooking apparatus 100 by the user ¶ [0202]. Also see [0207]-[0208] and Figs. 4 and 6), and
based on a user input being received through the displayed Ul, control the communication interface to transmit a signal requesting an update of the artificial intelligence model to the server (Maeng: see Figs. 4-6. The database 205 may store input data obtained from the input interface 220, learning data (or training data) used to train a model, a learning history of the model, and so forth ¶ [0208]).
Regarding claims 5 and 15, the combination of Maeng and Shah discloses, wherein the UI comprises at least one from among the identification information, the first accuracy information, the second accuracy information, the difference value, or information inquiring of an update of the artificial intelligence model (Maeng: user feedback through UI ¶ [0234]-[0235]. Also see Fig. 6).
Regarding claim 6, the combination of Maeng and Shah discloses, based on the identification information of the object and the first accuracy information being obtained, control the communication interface to transmit a signal requesting the second accuracy information corresponding to the identification information to the server, and based on the second accuracy information corresponding to the identification information being received from the server through the communication interface, store the received second accuracy information in the at least one memory (Maeng: receiving and storing input information, using the information to update the model, wherein the updated model improves the performance ¶ [0202]-[0208]. Also see ¶ [0194]-[0195]).
Regarding claim 7, the combination of Maeng and Shah discloses, based on the difference value being greater than or equal to the threshold value (Shah: At operation 506, based on an identification of an inaccuracy of the lightweight model (e.g., based on a determination that the accuracy of the facial recognition by the lightweight is below a configurable accuracy threshold), the lightweight model may be retrained on the server using training data selected to improve the accuracy of the lightweight model. In example embodiments, for images that have been pre-processed by the client device, the server may request the original images from the client for performing the retraining [col. 9, ll. 54-63]), control the communication interface to transmit the obtained image to the server to train the artificial intelligence model stored in the server (Maeng: collecting information and updating model ¶ [0190] and [0194]-[0195]).
Regarding claims 8 and 20, the combination of Maeng and Shah discloses, based on the identification information of the object, which is obtained based on a plurality of captured images obtained from the camera, being counted greater than or equal to a first threshold number of times, control the communication interface to transmit a signal requesting first update information of the artificial intelligence model corresponding to the identification information to the server (Shah: Based on a determination that the set of images matches one or more reference images stored in a database with a confidence level that is equal to or greater than a confidence threshold, a person corresponding to the one or more reference images is associated as a driver of the vehicle during a time period in which the set of images was captured [col. 2, ll. 45-51]. At operation 506, based on an identification of an inaccuracy of the lightweight model (e.g., based on a determination that the accuracy of the facial recognition by the lightweight is below a configurable accuracy threshold), the lightweight model may be retrained on the server using training data selected to improve the accuracy of the lightweight model. In example embodiments, for images that have been pre-processed by the client device, the server may request the original images from the client for performing the retraining [col. 9, ll. 54-63]. Comparing the identification count to a threshold is vague and unclear), and based on the first update information corresponding to the identification information being received from the server through the communication interface, update the artificial intelligence model based on the received first update information (Maeng: collecting information and updating model ¶ [0190] and [0194]-[0195]).
Regarding claim 9, the combination of Maeng and Shah discloses, based on the identification information of the object, which is obtained based on at least one captured image obtained from the camera, being counted less than a second threshold number of times, control the communication interface to transmit a signal requesting second update information of the artificial intelligence model corresponding to the identification information to the server (Shah: Based on a determination that the set of images matches one or more reference images stored in a database with a confidence level that is equal to or greater than a confidence threshold, a person corresponding to the one or more reference images is associated as a driver of the vehicle during a time period in which the set of images was captured [col. 2, ll. 45-51]. At operation 506, based on an identification of an inaccuracy of the lightweight model (e.g., based on a determination that the accuracy of the facial recognition by the lightweight is below a configurable accuracy threshold), the lightweight model may be retrained on the server using training data selected to improve the accuracy of the lightweight model. In example embodiments, for images that have been pre-processed by the client device, the server may request the original images from the client for performing the retraining [col. 9, ll. 54-63]. Comparing the identification count to a threshold is vague and unclear), and based on the second update information corresponding to the identification information being received from the server through the communication interface, update the artificial intelligence model based on the received second update information (Maeng: collecting information and updating model ¶ [0190] and [0194]-[0195]).
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571)270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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12/2/2025
/MOHAMMAD S ROSTAMI/ Primary Examiner, Art Unit 2154
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