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
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.
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, 4, 5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over US 20060195239 A1 (Teichner et al.) (hereinafter Teichner) in view of CN 109217885 A (CHENG et al.) (hereinafter CHENG).
In re claims 1 and 5, Teichner discloses a control method of a server (Fig. 3, [0039], “FIG. 3 illustrates a process to control operations carried out at the system controller 175”) and the server (Fig. 1:191) comprising: a communicator (Fig. 1:160, [0022], “The system 100 may receive inputs from a network server 191 or a broadcast station 192”. [0032], “The mobile communication unit 160 may communicate wirelessly within a cellular network system, including broadcast stations (called base stations) that may receive and/or send speech or data information to and from other wireless or wired communication units, such as a network server 191” (communication is both ways to send and receive signals); a storage configured to store radio station information including location information on a plurality of transmitting stations, radio frequencies for each of the plurality of transmitting stations, location information of a plurality of vehicles corresponding to each of the plurality of transmitting stations, and at least one radio station name corresponding to each of the radio frequencies (Fig. 1:140, [0030], “The data from the received broadcast stations data may be stored in local databases 140”. [0063], “As indicated in FIG. 10, at a location defined by GPS coordinates (X2, Y2), the field strength from a broadcast station transmitting at 94.1 kHz may be much lower than that broadcast from a different station of the same network at frequency 97.3 kHz. An entry into the database 140 may be made indicating that coordinates (X2, Y2) constitute a switch over point from one broadcast station (94.1 kHz) to another one (97.3 kHz). At a location defined by coordinates (X10, Y10), the tuning receiver 130 may determine that the selected program 903 will no longer be received with satisfactory quality. The receiver 130 may populate the database 140 with reception quality values and broadcast station data. Data for other routes may be entered into the database 140. When the user selects a route for which data has already been stored in the database 140...”. [0061], “The database 140, with the system controller 175, may build a broadcast station map indicating the channels that may be received and boundaries where a switchover operation to a different channel should be performed”. [0036], “...The data may include broadcast station information for radio or television reception such as program name, position such as geographical coordinates, broadcast transmission power, area of coverage, alternative frequencies, program identification code, program type code, listings of alternative programs, or other parameters related to broadcast station information. Position data and frequencies may be listed as one data block followed by data blocks for other programs”. [0060], “The respective frequencies are indicated in the diagram as 97.7 kHz, 102.5 kHz and 95.8 kHz. Route data defined by reference points is compared with the information of the local database 140. A high-capacity memory may be provided either locally in the tuner 130 or within the system control unit memory 177, to provide sufficient storage space for the data associated with route, reference points and broadcast station information” (discloses a local database 140 to store data on the broadcast stations, radio station names, routes and corresponding switching frequencies based on locations of the vehicle)); and a controller (Fig. 1:175) configured to: receive location information of a vehicle (Fig. 4:405, [0026], “The navigation unit 105 may receive position data, such as geographical coordinates from the GPS unit 110 and from a database 115 containing map data. The data provided by GPS unit 110 may include geographical coordinates in standardized form, which may be detected in real time according to the vehicle's location and movement using satellite communication” (vehicle collects location information according to the movement of the vehicle) and a radio frequency corresponding to the location information of the vehicle, from the vehicle through the communicator ([0029], “The receiver may search the frequency band for available broadcast stations, and may provide a list of available stations and tune the tuning receiver 130 to a selected station...The tuning receiver 130 may simultaneously search the frequency band during reproduction of a program for further available stations, or may continuously scan the available frequency band for broadcast stations with good reception properties through a background tuner”); run a machine learning algorithm which is pre-trained to predict, based on the received location information and the received radio frequency, a first transmitting station corresponding to the received location information among the plurality of transmitting stations ([0043], “The system 100 may correlate the position data, at block 410, obtained from the navigation unit in block 405, with the geographical location coordinates for the broadcast stations and their geographical coverage area. A matching algorithm may be implemented by using the method of minimal distances for the distance between the present location and the broadcast station. Other matching algorithms, which process parameters such as transmission power, shape and geographical extensions of the coverage area, may be implemented”); store the location information of the vehicle in the storage to correspond to the first transmitting station and update the radio station information (Fig. 4:415, [0038], “The information may be updated during maintenance operations by installing an updated version of the database 150. The database update may be performed remotely, such as over a network in communication with the system 100, through a network server 191”. [0041], “The system 100 may use the information obtained by blocks 305, 310, and 312 to build the local databases 140”. [0044], “The tuning receiver 130 may provide data for the local database 140, at block 415. The data may relate to the current location, such as a list of available broadcast stations for the present location and its reception characteristics related to the actual position data delivered from the navigation unit 105”. [0063], “The database 140 may be initially empty. If the user has selected program 903 as his preferred broadcast program, data may be entered into the reserved fields. Examples of data include the present GPS coordinates, the tuned frequency and the measured field strength of this program”. [0053], “Once the switching coordinates have been determined as satisfactory, they may be stored in the database 140” (update information in the local database)); and transmit the updated radio station information to the vehicle through the communicator ([0022], “The system 100 may receive inputs from a network server 191 or a broadcast station 192”. [0059], “When starting at point 901, the program is best received at reception frequency 94.1 kHz. At a certain distance from the start point, the reception quality at this frequency may decrease, while this program may be received from an alternative broadcast station at the alternative frequency of 97.3 kHz, with improved reception quality. When traveling further along the route, there will be a third broadcast station broadcasting the program SWR3 with a frequency of 96.8 kHz”).
Teichner does not explicitly disclose run a machine learning algorithm which is pre-trained to predict, based on the received location information and the received radio frequency, a first transmitting station corresponding to the received location information among the plurality of transmitting stations.
CHENG discloses run a machine learning algorithm which is pre-trained to predict, based on the received location information and the received radio frequency, a first transmitting station corresponding to the received location information among the plurality of transmitting stations (Page 2, lines 9-15, “the present invention provides a vehicle-mounted radio device, the device comprising: a radio input module, the parameter forecasting module and the radio output module, wherein, the station input module used for obtaining the current radio station of correlative technical parameter”. Page 2, lines 16-19, “the parameter predicting module is further used for according to the correlative technical parameter of the current station and the radio location information, determining whether the current station is effective radio by including two classification algorithm model...”). Page 2, lines 32-35, “The embodiment of the invention further claims a vehicle-mounted radio searching method, the method comprises the following steps: obtaining related technical parameters of the current station according to the correlative technical parameter of the current station, through the classification algorithm model to determine whether the current station is effectively broadcasting station; and storing the active station” (using machine learning model to find the valid radio station for a certain frequency based on location of the vehicle)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Teichner with CHENG to provide a system for utilizing machine learning model to find the best radio station to tune into when the vehicle is moving to different areas and the signal becomes weak. The advantage of doing so is convenience to the driver to not manually search a similar channel while driving, less distraction and safety.
In re claims 4 and 8, the combination discloses the server of claim 1, wherein Teichner discloses wherein the machine learning algorithm includes a K-nearest neighbors’ algorithm ([0043], “The system 100 may correlate the position data, at block 410, obtained from the navigation unit in block 405, with the geographical location coordinates for the broadcast stations and their geographical coverage area. A matching algorithm may be implemented by using the method of minimal distances for the distance between the present location and the broadcast station” (It is implicit that the machine learning algorithm utilizes K nearest neighbor algorithm to find the closest transmitting station among the set of M transmitting stations for a certain location point q in the coverage region)).
Claim(s) 2 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over US 20060195239 A1 (Teichner et al.) (hereinafter Teichner) in view of CN 109217885 A (CHENG et al.) (hereinafter CHENG) and in further view of US 11190289 B2 (Aggarwal et al.) (hereinafter Aggarwal).
In re claims 2 and 6, the combination discloses the server of claim 1, but does not explicitly disclose wherein the controller is configured to run the machine learning algorithm to identify at least one transmitting station corresponding to the received radio frequency among the plurality of transmitting stations, and to predict that the first transmitting station corresponds to the received location information, in response to the received location information being included in a predetermined first radio wave detection area of the first transmitting station among the at least one transmitting station.
Aggarwal discloses to identify at least one transmitting station corresponding to the received radio frequency among the plurality of transmitting stations, and to predict that the first transmitting station corresponds to the received location information, in response to the received location information being included in a predetermined first radio wave detection area of the first transmitting station among the at least one transmitting station (Fig. 1, Fig. 2, Fig. 6:635, Col 6, lines 11-17, “The method further includes receiving, in response to the providing, a list of radio stations, where the list of radio stations is tailored to the user based on the at least one location, where the list of radio stations includes a mapping for each of the radio stations, and where each of the mappings includes a radio station frequency mapped to a coverage area”. Col 11, lines 25-28, “A coverage area may include a broadcast area for a particular radio station tower and/or the extent to which a radio station may be received by a terrestrial radio station antenna”. Col 11, lines 49-67; Col 12, lines 1-8, “In some implementations, the radio station database 115 may be utilized to provide radio station information to a client device 105 based on one or more locations associated with the client device 105. Station selection module 130 identifies mappings in the radio station database 115 that are associated with the provided location (e.g., mappings that include the exact location, mappings that include coverage areas that include the provided location, mappings that include locations within a threshold distance to the provided location)... Station selection module 130 can then identify mappings in the radio station database 115 that include the vehicle 205 location as part of the mapping, such as the stations associated with tower 215 (i.e., the coverage area 215a or 215b) and the station associated with tower 220 (i.e., coverage area 220a). Station selection module 130 can determine a subset of stations that includes the frequencies of the stations associated with tower 215 and tower 220 and provide the subset to client device 105”. Col 6, lines 22-26, “identifying a current location of the user; identifying, from the mappings, one or more proximate radio stations with mapped coverage areas that are proximate to the current location based on the current location and the determined radio selection criteria” (discloses that the received location of the vehicle is within the coverage of the transmitting station selected corresponding to the frequency and location of the vehicle)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Teichner and CHENG with Aggarwal to provide a system for utilizing machine learning model to find the transmitting station to tune into which is in the coverage of the vehicle. The advantage of doing so is convenience to the driver to not manually search a similar channel while driving, less distraction and safety.
Claim(s) 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over US 20060195239 A1 (Teichner et al.) (hereinafter Teichner) in view of CN 109217885 A (CHENG et al.) (hereinafter CHENG) in view of US 11190289 B2 (Aggarwal et al.) (hereinafter Aggarwal) and in further view of US 20220399936 A1 (ARKSEY et al.) (hereinafter ARKSEY).
In re claims 3 and 7, the combination discloses the server of claim 2, wherein Aggarwal discloses wherein the controller is configured to run the machine learning algorithm to identify a predetermined number of vehicles located adjacent to the received location information among vehicles corresponding to each of the at least one transmitting station, in response to the received location information not being included in a predetermined radio wave detection area of each of the at least one transmitting stations (Fig. 2, Col 11, lines 22-25, “In some implementations, the stored location of a mapping can be a coverage area and the coverage area may be updated when subsequent locations for a radio station frequency are provided by client devices”. Col 11, lines 32-43, “For example, referring again to Fig. 2, based on the location provided by vehicle 205, a coverage area of 215a may be stored with the radio frequency in the database 115. Subsequently, when vehicle 230 provides a location along with the radio station information and frequency from tower 215, the mapping can be updated to include a mapped coverage area 215b that includes both locations 205 and 230. Thus, a coverage area stored with a radio frequency includes known locations where the radio station was received by one or more client devices” (utilizing data from two or more vehicles for creating a mapping for radio station coverage), but does not explicitly disclose to predict that the first transmitting station in which a largest number of vehicles are included among the identified vehicles corresponds to the received location information.
ARKSEY discloses to predict that the first transmitting station in which a largest number of vehicles are included among the identified vehicles corresponds to the received location information ([0026], “receiving, from a plurality of drones of a drone swarm, data comprising radio frequency signal characteristics detected by the plurality of drones; generating a model of a radio frequency environment for the drone swarm based on the data received from the plurality of drones; and controlling at least one wireless communication system to improve wireless communication for the drone swarm based on the model of the radio frequency environment”. [0047], “The intelligent Base Stations can use that information along with a machine-learned model based on the past flights, similar flights and current weather and other conditions to predict which specific 3D locations will have RF performance at what frequency”. [0251], “In any of these embodiments, generating a model of a radio frequency environment for the drone swarm can include predicting portions of the radio frequency environment using a machine learning model”). [0119], “Machine learning and artificial intelligence techniques may include, but are not limited to, neural networks, visual transformers, back propagation, convolutional neural networks (CNN), and deep learning” (this is analogous to a swarm of vehicles in a region, collecting radio frequency signal characteristics from the group of vehicles and creating a machine learning model to predict the radio frequency environment in a certain location so as to provide the users with the best frequency to tune into based on their location. Swarm of drones is interpreted as large number of vehicles and it is implicit that large sample size provides more accuracy for prediction)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Teichner and CHENG with Aggarwal and ARKSEY to provide a system and a method utilizing machine learning model for predicting a broadcast station to tune into when a vehicle moves to different areas and the signal becomes weak. The advantage of doing so is convenience to the driver to not manually search a similar channel while driving, less distraction and safety.
Contact
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/SWATI JAIN/Examiner, Art Unit 2649
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