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 .
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) 1-5, 7, 8, 11-15, 17 and 18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tramoni (U.S. PG-PUB NO. 2021/0195404) in view of Buckley (U.S. PATENT NO. 10944436).
-Regarding claim 1, Tramoni discloses a method of controlling an electronic device executing near-field communication (NFC) transactions (see abstract), the method comprising: obtaining a plurality of context parameters related to an NFC transaction to be performed (step 201 (LOCATION) of determination of the geographic position of mobile terminal 100, FIG. 2, paragraph 53); identifying an optimal radio frequency (RF) configuration from a plurality of RF configurations for executing the NFC transaction by inputting the plurality of context parameters (step 202 (SELECT NFC SETTINGS) of selection of one or a plurality of parameters of configuration of the NFC device 102 of the mobile terminal, FIG. 2, paragraph 57) and executing the NFC transaction using the optimal RF configuration (step 203 (APPLY NFC SETTINGS) of application, to NFC device 102, of the configuration parameters selected at step 202, FIG. 2, paragraph 60), and wherein the plurality of context parameters comprises one or more of: an orientation of the electronic device, a tap angle with respect to an NFC reader, a key position of the electronic device, a type of the NFC transaction to be performed, NFC firmware information, and a geographic location of the NFC transaction to be performed (location 201, FIG. 2, paragraph 53).
Tramoni is silent to teaching that an artificial intelligence (AI) model; wherein the AI model is configured to establish correlations of the plurality of context parameters with the plurality of RF configurations. However, the claimed limitation is well known in the art as evidenced by Buckley.
In the same field of endeavor, Buckley teaches an artificial intelligence (AI) model; wherein the AI model is configured to establish correlations of the plurality of context parameters with the plurality of RF configurations (artificial intelligence model 32, col. 5 line 54-col. 6 line 22, FIG. 3).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Tramoni with the teaching of Buckley in order to provide decrease power consumption when the RF communication performance is less difficult.
-Regarding claim 2, the combination further discloses the electronic device is a wearable device (Tramoni, watches, paragraph 74), wherein each of the plurality of RF configurations comprises a corresponding NFC antenna configuration (Tramoni, antenna circuits of NFC device 102, paragraph 59), and wherein each of the corresponding NFC antenna configuration is associated with one or more of radio frequency parameters, impedance parameters, NFC firmware information, and hardware configuration parameters of the electronic device (Tramoni, setting frequency matching and/or impedance matching components of the antenna circuits of NFC device 102, paragraph 59).
-Regarding claim 3, the combination further discloses the plurality of RF configurations are pre-stored on the electronic device (Tramoni, stored in table 110, paragraph 59).
-Regarding claim 4, the combination further discloses identifying the optimal RF configuration comprises: identifying the optimal RF configuration from the plurality of RF configurations based on the correlations between the plurality of context parameters and the plurality of RF configurations, and wherein the method further comprises: loading the optimal RF configuration for executing the NFC transaction (Tramoni, setting frequency matching and/or impedance matching components of the antenna circuits of NFC device 102, paragraph 59).
-Regarding claim 5, the combination further discloses the obtaining the plurality of context parameters comprises: receiving, from one or more sensors at the electronic device, readings indicative of values of corresponding context parameters of the plurality of context parameters (Tramoni, identifier, paragraph 66); and obtaining the plurality of context parameters related to the NFC transaction to be performed based on the readings (Tramoni, step 302 (SELECT NFC SETTINGS) of selection of one or a plurality of parameters of configuration of the NFC device 102 of the mobile terminal according to the wireless local network identifier detected at step 301, paragraph 67).
-Regarding claim 7, the combination further discloses training the AI model by performing, for each of the plurality of RF configurations, pre-determined NFC transactions for multiple predefined context parameter combinations (Buckley, training database 49 that may be built from spectral sensor data obtained from the RF spectral sensor 26 based on operational conditions, col. 5 line 54-col. 6 line 22); determining, for the multiple predefined context parameter combinations, corresponding success rates of each of the plurality of RF configurations, respectively (Buckley, RF spectral sensor 26 senses the dynamically changing RF spectral environment and observes spectrum conditions in real-time, col. 5 line 54-col. 6 line 22); generating a success matrix based on the multiple predefined context parameter combinations, and the corresponding success rates of each of the plurality of RF configurations for the multiple predefined context parameter combinations (Buckley, Data is generated and transferred to a classification section 56 of the AI model 32 for classification and action, where any hardware forming the RF circuitry 22 is configured versus the spectrum class 58, col. 5 line 54-col. 6 line 22); and storing the success matrix in a database (Buckley, feeds new data into the training database 49, col. 5 line 54-col. 6 line 22).
-Regarding claim 8, the combination further discloses the identifying the optimal RF configuration comprises: identifying, from the success matrix, a relevant context parameter combination from the multiple predefined context parameter combinations based on the plurality of context parameters, identifying the plurality of RF configurations (Buckley, RF spectral sensor 26 senses the dynamically changing RF spectral environment and observes spectrum conditions in real-time, col. 5 line 54-col. 6 line 22), and the corresponding success rates, for the relevant context parameter combination, determining, from the success matrix, for the relevant context parameter combination, one RF configuration of the plurality of RF configurations having a highest success rate, and determining the one RF configuration having the highest success rate to be the optimal RF configuration (Buckley, Data is generated and transferred to a classification section 56 of the AI model 32 for classification and action, where any hardware forming the RF circuitry 22 is configured versus the spectrum class 58, col. 5 line 54-col. 6 line 22).
-Regarding claim 11, Tramoni discloses an electronic device executing near-field communication (NFC) transactions (see abstract) comprising: at least one memory storing instructions (memory, paragraph 68); and at least one processor (it is inherent to have a processor in a smartphone) connected to the at least one memory and configured to execute the instructions to: obtain a plurality of context parameters related to an NFC transaction to be performed (step 201 (LOCATION) of determination of the geographic position of mobile terminal 100, FIG. 2, paragraph 53); identify an optimal radio frequency (RF) configuration from a plurality of RF configurations for executing the NFC transaction by inputting the plurality of context parameters (step 201 (LOCATION) of determination of the geographic position of mobile terminal 100, FIG. 2, paragraph 53); and execute the NFC transaction using the optimal RF configuration (step 203 (APPLY NFC SETTINGS) of application, to NFC device 102, of the configuration parameters selected at step 202, FIG. 2, paragraph 60), and wherein the plurality of context parameters comprises one or more of: an orientation of the electronic device, a tap angle with respect to an NFC reader, a key position of the electronic device, a type of the NFC transaction to be performed, NFC firmware information, and a geographic location of the NFC transaction to be performed (location 201, FIG. 2, paragraph 53).
Tramoni is silent to teaching that an artificial intelligence (AI) model; wherein the AI model is configured to establish correlations of the plurality of context parameters with the plurality of RF configurations. However, the claimed limitation is well known in the art as evidenced by Buckley.
In the same field of endeavor, Buckley teaches an artificial intelligence (AI) model; wherein the AI model is configured to establish correlations of the plurality of context parameters with the plurality of RF configurations (artificial intelligence model 32, col. 5 line 54-col. 6 line 22, FIG. 3).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Tramoni with the teaching of Buckley in order to provide decrease power consumption when the RF communication performance is less difficult.
-Regarding claim 12, the combination further discloses the electronic device is a wearable device (Tramoni, watches, paragraph 74), wherein each of the plurality of RF configurations comprises a corresponding NFC antenna configuration (Tramoni, antenna circuits of NFC device 102, paragraph 59), and wherein each of the corresponding NFC antenna configuration is associated with one or more of radio frequency parameters, impedance parameters, NFC firmware information, and hardware configuration parameters of the electronic device (Tramoni, setting frequency matching and/or impedance matching components of the antenna circuits of NFC device 102, paragraph 59).
-Regarding claim 13, the combination further discloses the plurality of RF configurations are pre-stored on the electronic device (Tramoni, stored in table 110, paragraph 59).
-Regarding claim 14, the combination further discloses the at least one processor is further configured to execute the instructions to: identify the optimal RF configuration from the plurality of RF configurations based on the correlations between the plurality of context parameters and the plurality of RF configurations, and load the optimal RF configuration for executing the NFC transaction (Tramoni, setting frequency matching and/or impedance matching components of the antenna circuits of NFC device 102, paragraph 59).
-Regarding claim 15, the combination further discloses the at least one processor is further configured to execute the instructions to: receive, from one or more sensors at the electronic device, readings indicative of values of corresponding context parameters of the plurality of context parameters (Tramoni, identifier, paragraph 66); and obtain the plurality of context parameters related to the NFC transaction to be performed based on the readings (Tramoni, step 302 (SELECT NFC SETTINGS) of selection of one or a plurality of parameters of configuration of the NFC device 102 of the mobile terminal according to the wireless local network identifier detected at step 301, paragraph 67).
-Regarding claim 17, the combination further discloses the at least one processor is further configured to execute the instructions to: train the AI model by performing, for each of the plurality of RF configurations, pre-determined NFC transactions for multiple predefined context parameter combinations (Buckley, training database 49 that may be built from spectral sensor data obtained from the RF spectral sensor 26 based on operational conditions, col. 5 line 54-col. 6 line 22); determine, for the multiple predefined context parameter combinations, corresponding success rates of each of the plurality of RF configurations, respectively (Buckley, RF spectral sensor 26 senses the dynamically changing RF spectral environment and observes spectrum conditions in real-time, col. 5 line 54-col. 6 line 22); generate a success matrix based on the multiple predefined context parameter combinations, and the corresponding success rates of each of the plurality of RF configurations for the multiple predefined context parameter combinations (Buckley, Data is generated and transferred to a classification section 56 of the AI model 32 for classification and action, where any hardware forming the RF circuitry 22 is configured versus the spectrum class 58, col. 5 line 54-col. 6 line 22); and store the success matrix in a database in the at least one memory (Buckley, feeds new data into the training database 49, col. 5 line 54-col. 6 line 22).
-Regarding claim 18, the combination further discloses the at least one processor is further configured to execute the instructions to identify the optimal RF configuration by: identifying, from the success matrix, a relevant context parameter combination from the multiple predefined context parameter combinations based on the plurality of context parameters, identifying the plurality of RF configurations (Buckley, RF spectral sensor 26 senses the dynamically changing RF spectral environment and observes spectrum conditions in real-time, col. 5 line 54-col. 6 line 22), and the corresponding success rates, for the relevant context parameter combination, determining, from the success matrix, for the relevant context parameter combination, one RF configuration of the plurality of RF configurations having a highest success rate, and determining the one RF configuration having the highest success rate to be the optimal RF configuration (Buckley, Data is generated and transferred to a classification section 56 of the AI model 32 for classification and action, where any hardware forming the RF circuitry 22 is configured versus the spectrum class 58, col. 5 line 54-col. 6 line 22).
Claim(s) 6, 9, 10, 16, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tramoni (U.S. PG-PUB NO. 2021/0195404) in view of Buckley (U.S. PATENT NO. 10944436) and further in view of Stahl (U.S. PG-PUB NO. 2024/0137070).
-Regarding claim 6, the combination is silent to teaching that prior to the identifying the optimal RF configuration: initiating execution of the NFC transaction based on a default RF configuration of the electronic device; determining one or more failures of the NFC transaction executed based on the default RF configuration; and determining that a different RF configuration for executing the NFC transaction should be used. However, the claimed limitation is well known in the art as evidenced by Stahl.
In the same field of endeavor, Stahl teaches prior to the identifying the optimal RF configuration: initiating execution of the NFC transaction based on a default RF configuration of the electronic device (initiate a transaction, are issued by a PCD device dependent on a specific standard, paragraph 47); determining one or more failures of the NFC transaction executed based on the default RF configuration (the TX phase of the communications device 100 causes the failed communications, paragraph 47); and determining that a different RF configuration for executing the NFC transaction should be used (The next CED response can be transmitted by the communications device 100 using an updated TX phase, paragraph 47).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of the combination with the teaching of Stahl in order to provide cures to the weakness of fixed parameterization of phase control mechanisms.
-Regarding claim 9, the combination further discloses obtaining an updated AI model by updating the AI model based on feedback information, the feedback information comprising inferences related to success and failure information associated with the NFC transaction (Buckley, Data is generated and transferred to a classification section 56 of the AI model 32 for classification and action, where any hardware forming the RF circuitry 22 is configured versus the spectrum class 58, col. 5 line 54-col. 6 line 22); and updating the default RF configuration based on the feedback information and the updated AI model, thereby personalizing the plurality of RF configurations for a user associated with the electronic device (Buckley, feeds new data into the training database 49, col. 5 line 54-col. 6 line 22).
-Regarding claim 10, the combination further discloses the AI model includes a plurality of neural network layers, wherein each of the plurality of neural network layers includes a plurality of weight values, and wherein the method further comprising performing neural network computation by computation based on a computation result of a previous layer and a plurality of weight values of a current layer (Stahl, the POM 404 is an artificial neural network (ANN), which includes an input layer 410 with input nodes 412-1, . . . , 412-N (N is a positive integer), two hidden layers 420, 430 with neurons 422-1, . . . , 422-N, and neurons 432-1, . . . , 432-N, respectively, and an output layer 440 with an output node 442, paragraph 56).
-Regarding claim 16, the combination is silent to teaching that the at least one processor is further configured to execute the instructions to, prior to the identifying the optimal RF configuration: initiate execution of the NFC transaction based on a default RF configuration of the electronic device; determine one or more failures of the NFC transaction executed based on the default RF configuration; and determine that a different RF configuration for executing the NFC transaction should be used. However, the claimed limitation is well known in the art as evidenced by Stahl.
In the same field of endeavor, Stahl teaches the at least one processor is further configured to execute the instructions to, prior to the identifying the optimal RF configuration: initiating execution of the NFC transaction based on a default RF configuration of the electronic device (initiate a transaction, are issued by a PCD device dependent on a specific standard, paragraph 47); determining one or more failures of the NFC transaction executed based on the default RF configuration (the TX phase of the communications device 100 causes the failed communications, paragraph 47); and determining that a different RF configuration for executing the NFC transaction should be used (The next CED response can be transmitted by the communications device 100 using an updated TX phase, paragraph 47).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of the combination with the teaching of Stahl in order to provide cures to the weakness of fixed parameterization of phase control mechanisms.
-Regarding claim 19, the combination further discloses the at least one processor is further configured to execute the instructions to: obtain an updated AI model by updating the AI model based on feedback information, the feedback information comprising inferences related to success and failure information associated with the NFC transaction (Buckley, Data is generated and transferred to a classification section 56 of the AI model 32 for classification and action, where any hardware forming the RF circuitry 22 is configured versus the spectrum class 58, col. 5 line 54-col. 6 line 22); and update the default RF configuration based on the feedback information and the updated AI model, thereby personalizing the plurality of RF configurations for a user associated with the electronic device (Buckley, feeds new data into the training database 49, col. 5 line 54-col. 6 line 22).
-Regarding claim 20, the combination further discloses the AI model includes a plurality of neural network layers, wherein each of the plurality of neural network layers includes a plurality of weight values, and wherein the at least one processor is further configured to execute the instructions to: perform neural network computation by computation based on a computation result of a previous layer and a plurality of weight values of a current layer (Stahl, the POM 404 is an artificial neural network (ANN), which includes an input layer 410 with input nodes 412-1, . . . , 412-N (N is a positive integer), two hidden layers 420, 430 with neurons 422-1, . . . , 422-N, and neurons 432-1, . . . , 432-N, respectively, and an output layer 440 with an output node 442, paragraph 56).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PING Y HSIEH/ Primary Examiner, Art Unit 2664