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
The following is a non-final, first office action in response to the communication filed 03/22/2024. Claims 1-15 are currently pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Benefit is given to the priority document EP21198804.3 and the effective filing date of 09/24/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/22/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
On page 10, line 16, “more3” should read “more”.
On page 11, line 29, it appears that “tracker 455” should refer to “tracking function 460”. No item labeled “455” appears in the figures.
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
Regarding punctuation in claim 1, Examiner recommends removing the comma following the word “system” in line 1, removing the semi-colon following the word “comprising” in line 2, and changing the comma to a colon following the word “comprising” in line 4.
It appears that the words “the system” were mistakenly marked for deletion in line 4 of amended claim 1.
Claim 12 is objected to because of the following informalities: it appears that “radar range” in line 3 should read “a radar range”.
Claim 15 is objected to because of the following informalities: Examiner recommends replacing the semi-colon at the end of line 4 with a colon.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Examiner interprets claim limitations of claim 1 under 35 U.S.C. 112(f). Supporting structure from the specification for the limitations in claim 1 is detailed below:
Structure for “an environment perception system arranged to obtain environment data indicative of a surrounding traffic environment of the radar transceiver” can be found in page 2, lines 23-31 of the instant specification: “According to some aspects, the environment perception system comprises the radar transceiver…According to some aspects, the environment perception comprises any of a vision based sensor, a further radar sensor, and/or a lidar sensor.”
Structure for “a radar resource requirement prediction module configured to estimate a future time-frequency resource requirement for radar operation, based on the environment data” can be found in Fig. 5 of the instant specification, which give a flow chart showing the algorithm used by the radar resource requirement prediction module.
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.
Claims 1-5, 8-9 and 14-15 are rejected under 35 U.S.C. 102(a) as being anticipated by Chen et al. (US-20220260671-A1; hereinafter Chen).
Regarding claim 1, Chen discloses:
An automotive radar transceiver system (see at least Abs; “Methods, apparatus, systems and articles of manufacture to manage automotive radar coordination are disclosed.”), comprising a radar transceiver (see at least Fig. 3, transmitter 208, receiver 312 and duplexer 310) arranged to transmit radar signals (see at least [0024]; “The example radar unit 300 is equipped with a transmitter 308, a duplexer 310, and a receiver 312 to send and receive RF signals.”) in time-frequency resources dynamically allocated (see at least Fig. 9, step 908: “Master node assigns resource group to vehicle B”) by a remote scheduler function (see at least Fig. 9, master node 950), the system further comprising:
an environment perception system (see at least Fig. 3, image sensor 306, GPS antenna 304, antenna 302, and various processing modules) arranged to obtain environment data indicative of a surrounding traffic environment of the radar transceiver (see at least [0024]; “The example radar unit 300 also contains a GPS accessor 313, an image accessor 314, and an image analyzer 315… The example radar unit 300 illustrated herein also contains a resource multiplexer 321, a beacon detector 322, an interference detector 324, a resource assigner 326, a resource hopper 328, and a pedestrian probability calculator 330.”),
a radar resource requirement prediction module (see at least Fig. 3, Resource Manager 316) configured to estimate a future time-frequency resource requirement for radar operation (see at least [0032]; “The example resource manager 316 of the illustrated example of FIG. 3 performs an initial setup process of the example radar unit 300, including assigning a unit ID, saving the current time and position information of the example radar unit 300, and obtaining the radar unit requirements.” See also [0033], which discusses how past radar requirements for specific locations are stored in database 318. The use of location-specific historical usage data in assessing current radar requirements is one example of how manager 316 estimates future resource requirements.), based on the environment data (see again at least [0032]; “The example resource manager 316 of the illustrated example of FIG. 3 performs an initial setup process of the example radar unit 300, including assigning a unit ID, saving the current time and position information of the example radar unit 300, and obtaining the radar unit requirements.”),
wherein the radar transceiver is arranged to request time-frequency resources for radar operation from the remote scheduler function (see at least Fig. 9, step 902: “Vehicle B reports vehicle status” to master node 950) based on the estimated future time-frequency resource requirement (see at least [0058]; “…vehicle statuses (e.g., position, radar unit requirements, etc.)”).
Regarding claim 2, Chen discloses the automotive radar transceiver system according to claim 1. Chen further teaches:
wherein the environment perception system comprises the radar transceiver (see at least [0049]; “The example receiver 312 scans before transmitting a RF signal. (Block 406). The scanning process 406 includes detecting interferences and performing resource hops when interferences are detected.”).
Regarding claim 3, Chen discloses the automotive radar transceiver system according to claim 1. Chen further teaches:
wherein the environment perception system comprises one or more of a vision based sensor (see at least Fig. 3, image sensor 306, and associated description in [0031] – [0032]), a further radar sensor, or a lidar sensor.
Regarding claim 4, Chen discloses the automotive radar transceiver system according to claim 1. Chen further teaches:
wherein the environment perception system comprises a vehicle telematics system arranged to obtain information related to the environment surrounding the radar transceiver via a wireless link from one or more external entities (see at least [0058]; “In the illustrated example of FIG. 8, the example vehicle A 800 and the example vehicle B 850 exchange their respective vehicle statuses (e.g., position, radar unit requirements, etc.). (Message 802). In some examples, the example vehicle A 800 and the example vehicle B 850 communicate via beacons using the example antenna 302 and the example beacon detector 322 of FIG. 3.”).
Regarding claim 5, Chen discloses the automotive radar transceiver system according to claim 4. Chen further teaches:
wherein the vehicle telematics system is arranged to obtain information related to time-frequency resources dynamically allocated for radar operation by one or more vehicles in a vicinity of the radar transceiver (see again [0058]; “FIG. 8 is an illustration of example vehicle-to-vehicle (V2V) communication to manage automotive radar coordination. In some examples, this process occurs when block 702 of FIG. 7 returns a result of YES. In the illustrated example of FIG. 8, the example vehicle A 800 and the example vehicle B 850 exchange their respective vehicle statuses (e.g., position, radar unit requirements, etc.). (Message 802). In some examples, the example vehicle A 800 and the example vehicle B 850 communicate via beacons using the example antenna 302 and the example beacon detector 322 of FIG. 3. The example vehicle A 800 proposes a multiplexing scheme (e.g., time-domain multiplexing, frequency-domain multiplexing, code-domain multiplexing, etc.) to the example vehicle B 850. (Message 804). The proposed multiplexing scheme can include any number and/or combination of multiplexing schemes. The example vehicle B 850 down-selects or confirms the proposed multiplexing scheme from the example vehicle A 800. (Message 806). The example vehicle A 800 then proposes and assigns a radar resource group for the radar units of the example vehicle A 800 using the agreed upon multiplexing scheme. (Message 808). The example vehicle B 850 then assigns a non-conflicting radar resource group based on the radar resource group selected by the example vehicle A 800. (Message 810).”).
Regarding claim 8, Chen discloses the automotive radar transceiver system according to claim 1. Chen further teaches:
wherein the radar resource requirement prediction module (see at least [0032]; “The example resource manager 316 accesses both the example radar unit database 318…”) comprises a database of previous environment perception data and corresponding radar resource requirements (see at least [0033]; “The example radar unit database 318 of the illustrated example of FIG. 3 stores the unique radar unit ID and requirements of the example radar unit 300 (e.g., latency requirements, frequency requirements, etc.), the time and position information from the example GPS antenna 304, the vehicle information (e.g., vehicle velocity) of the example vehicle 100, and the radar resource map created by the example resource multiplexer 321.”), wherein the radar resource requirement prediction module is arranged to estimate the future time-frequency resource requirement based on the database (see at least [0032]; “In some examples, the example resource manager 316 implements means for retrieving radar unit requirements (e.g., a unit ID, current time information, vehicle position information, and radar resource requirements).”) and on a current environment perception (note the references to vehicle position and time information in both [0032] and [0033] which show that the manager assesses current time and position, and the database has radar requirements stored with time and position information).
Regarding claim 9, Chen discloses the automotive radar transceiver system according to claim 1. Chen further teaches:
wherein the radar resource requirement prediction module (see Fig. 3, resource manager 316) comprises a machine learning structure (see at least [0084]; “FIG. 15 is a block diagram of an example processor platform 1500 structured to execute the instructions of FIGS. 4-5, 7, and 11-14 to implement the example radar unit 300 of FIG. 3. The processor platform 1500 can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network)…”) configured with an input port connected to the environment perception system (see at least [0050]; “FIG. 5 describes example machine readable instructions which may be executed to implement the example resource manager 316 of FIG. 3 to setup the example radar unit 300 of FIG. 3. The example process of FIG. 5 begins when the example resource manager 316 stores a unique unit ID in the example radar unit database 318. (Block 502). The example GPS accessor 313 accesses the example GPS antenna 304. (Block 504). Although the example radar unit 300 is illustrated with an example GPS antenna 304, other methods of implementing the example radar unit 300 may be additionally or alternatively used. For example, a geographic information system (GIS) may be used in place of the example GPS antenna 304. The GPS accessor 313 obtains the current time from the example GPS antenna 304 and the example resource manager 316 saves the current time to the example radar unit database 318. (Block 506). The GPS accessor 313 also obtains the current position information of the vehicle from the example GPS antenna 304 and the example resource manager 316 saves the current position information to the example radar unit database 318. (Block 508).”) and an output port arranged to generate the estimated future time-frequency resource requirement (see at least [0051]; “The example resource manager 316 obtains the radar unit requirements from the radar unit database 318. (Block 510). In some examples, radar unit requirements include but are not limited to key performance indicators (e.g., speed, range, etc.) and sets of resources (e.g., bandwidth, time duration, etc.). In the example disclosed herein, the example resource multiplexer 321 performs time and frequency domain multiplexing to form and store a radar resource map in the example radar unit database 318. (Block 512).”).
Regarding claim 14, Chen discloses the automotive radar transceiver system according to claim 1. Chen further teaches:
A vehicle (see at least Fig. 1, vehicle 100) comprising an automotive radar transceiver system (see at least Fig. 1, radar units 110 and 112) according to claim 1.
Regarding claim 15, Chen discloses:
A method performed by an automotive radar transceiver system (see at least Abs; “Methods, apparatus, systems and articles of manufacture to manage automotive radar coordination are disclosed.”), comprising a radar transceiver (see at least Fig. 3, transmitter 208, receiver 312 and duplexer 310) arranged to transmit radar signals (see at least [0024]; “The example radar unit 300 is equipped with a transmitter 308, a duplexer 310, and a receiver 312 to send and receive RF signals.”) in time-frequency resources dynamically allocated (see at least Fig. 9, step 908: “Master node assigns resource group to vehicle B”) by a remote scheduler function (see at least Fig. 9, master node 950), the method comprising the steps of:
obtaining environment data indicative of a surrounding traffic environment of the radar transceiver (see at least [0024]; “The example radar unit 300 also contains a GPS accessor 313, an image accessor 314, and an image analyzer 315… The example radar unit 300 illustrated herein also contains a resource multiplexer 321, a beacon detector 322, an interference detector 324, a resource assigner 326, a resource hopper 328, and a pedestrian probability calculator 330.”),
estimating a future time-frequency resource requirement for radar operation (see at least [0032]; “The example resource manager 316 of the illustrated example of FIG. 3 performs an initial setup process of the example radar unit 300, including assigning a unit ID, saving the current time and position information of the example radar unit 300, and obtaining the radar unit requirements.” See also [0033], which discusses how past radar requirements for specific locations are stored in database 318. The use of location-specific historical usage data in assessing current radar requirements is one example of how manager 316 estimates future resource requirements.), based on the environment data (see again at least [0032]; “The example resource manager 316 of the illustrated example of FIG. 3 performs an initial setup process of the example radar unit 300, including assigning a unit ID, saving the current time and position information of the example radar unit 300, and obtaining the radar unit requirements.”), and
requesting time-frequency resources for radar operation from the remote scheduler function (see at least Fig. 9, step 902: “Vehicle B reports vehicle status” to master node 950) based on the estimated future time-frequency resource requirement (see at least [0058]; “…vehicle statuses (e.g., position, radar unit requirements, etc.)”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Suzuki et al. (US-5694130-A; hereinafter Suzuki).
Regarding claim 6, Chen discloses the automotive radar transceiver system according to claim 1. However, Chen does not explicitly teach:
wherein the radar resource requirement prediction module is configured to estimate the future time-frequency resource requirement as a pre-determined bandwidth in case an object is detected for a first time in the environment.
Chen discloses coordination among automotive radars, and Suzuki is directed to methods for a vehicle-mounted radar system. Suzuki teaches:
wherein the radar resource requirement prediction module (see at least Fig. 1, modulation frequency setting device 52) is configured to estimate the future time-frequency resource requirement as a pre-determined bandwidth in case an object is detected for a first time in the environment (see at least Fig. 3 and associated description in col. 4, lines 16-48; “In an initial state in which range data 42a has not yet been obtained, the modulation frequency setting circuit 52 first outputs the modulation frequency data 52a which designates such modulation frequency as to make the detecting range the maximum.
“FIG. 3 is a flow chart showing an operation in the vehicle-mounted radar system according to the invention. The modulation frequency setting circuit 52 determines, in step 1, if the vehicle speed is a high vehicle speed, e.g., 80 km/h or more on the basis of the vehicle speed data 6a supplied from the vehicle speed detector 6. If the detected range measured in a longest range detection mode (e.g., 150 m) at a high vehicle speed is 20 m or more (step 2), then a low modulation frequency (e.g., 1 MHz) is set to keep the longest range detection mode (for example, a detectable range of 150 m) in step 3. If the vehicle speed is not a high vehicle speed, then, in step 4, the modulation frequency setting circuit 52 determines if the vehicle speed is a middle speed (e.g., 40 km/h or more). If so, and if the detected range is 20 m or more (step 5), then a modulation frequency of, e.g., 1.5 MHz is set to make the detection mode a long range detection mode (e.g., a detectable range of 100 m) in step 6. If the vehicle speed is not a middle speed, it is determined in step 7 whether the vehicle speed is a low vehicle speed (e.g., less than 40 km/h) and the detected range is 20 m or more. If the detected range is 20 m, then a modulation frequency of, e.g., 3 MHz is set to make the detection mode a middle range detection mode (e.g., a detectable range of 50 m) in step 8. And, if the detected range is less than 20 m, a modulation frequency of, e.g., 7.5 MHz is set to make the detection mode a short range detection mode (e.g., a detectable range of 20 m) in step 9.”).
Chen uses a database and information from environmental sensors to determine which radar resources to request from a scheduling device. Suzuki teaches a pre-set relationship between vehicle speed, detected object range, and radar resource used. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the resource manager used in Chen to employ a logic-based process for estimating required radar resources based on the range of detection and vehicle speed, with the process being employed as soon as an object is detected, as taught by Suzuki. Such a modification would have a reasonable expectation of success because Chen already considers speed and range when estimating required radar resources (see [0051]). One of ordinary skill would be motivated to include such a process for mapping radar resources to speed and range in order to enhance range resolution, as recognized by Suzuki (see Suzuki at least Abs; “If an object in a short distance is detected, the modulation frequency is set high to limit the detection range to a short range, thereby enhancing the range resolution.”).
Regarding claim 12, Chen discloses the automotive radar transceiver system according to claim 1. However, Chen does not explicitly teach:
further comprising a target tracking function arranged to generate a request for radar range or a radar range resolution, wherein the radar resource requirement prediction module is arranged to predict a future request by the target tracking function.
Suzuki teaches:
further comprising a target tracking function arranged to generate a request for radar range or a radar range resolution (see at least flow chart of Fig. 3), wherein the radar resource requirement prediction module (see at least Fig. 1, modulation frequency setting device 52) is arranged to predict a future request by the target tracking function (see at least col. 4, lines 16-20; “In an initial state in which range data 42a has not yet been obtained, the modulation frequency setting circuit 52 first outputs the modulation frequency data 52a which designates such modulation frequency as to make the detecting range the maximum.”).
Chen uses a database and information from environmental sensors to determine which radar resources to request from a scheduling device. Suzuki teaches a pre-set relationship between vehicle speed, detected object range, and radar resource used. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the resource manager used in Chen to employ a logic-based process for estimating required radar resources based on the range of detection and vehicle speed, as taught by Suzuki. Such a modification would have a reasonable expectation of success because Chen already considers speed and range when estimating required radar resources (see [0051]). One of ordinary skill would be motivated to include such a process for mapping radar resources to speed and range in order to enhance range resolution, as recognized by Suzuki (see Suzuki at least Abs; “If an object in a short distance is detected, the modulation frequency is set high to limit the detection range to a short range, thereby enhancing the range resolution.”).
Regarding claim 13, Chen discloses the automotive radar transceiver system according to claim 1. However, Chen does not explicitly teach:
wherein the radar transceiver is arranged to maintain a base level request for radar time-frequency resources, and to adjust the base request based on the estimated future time-frequency resource requirement.
Suzuki teaches:
wherein the radar transceiver is arranged to maintain a base level request for radar time-frequency resources (see at least col. 4, lines 16-20; “In an initial state in which range data 42a has not yet been obtained, the modulation frequency setting circuit 52 first outputs the modulation frequency data 52a which designates such modulation frequency as to make the detecting range the maximum.”), and to adjust the base request based on the estimated future time-frequency resource requirement (see at least Fig. 3 and col. 4, lines 49-53; “As described above, the arrangement is constructed such that the modulation frequency is so shifted as to extend the detectable range in response to the vehicle speed and limit the detection range to a short range if the target is in a short range.”).
Chen uses a database and information from environmental sensors to determine which radar resources to request from a scheduling device. Suzuki teaches a pre-set relationship between vehicle speed, detected object range, and radar resource used, with a default setting used in the absence of detected objects. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the resource manager used in Chen to employ the logic-based process for estimating required radar resources based on the range of detection and vehicle speed, as taught by Suzuki. Such a modification would have a reasonable expectation of success because Chen already considers speed and range when estimating required radar resources (see [0051]). One of ordinary skill would be motivated to include such a process for mapping radar resources to speed and range in order to enhance range resolution, as recognized by Suzuki (see Suzuki at least Abs; “If an object in a short distance is detected, the modulation frequency is set high to limit the detection range to a short range, thereby enhancing the range resolution.”).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Leong et al. (US-11714183-B2; hereinafter Leong).
Regarding claim 7, Chen discloses the automotive radar transceiver system according to claim 1. However, Chen does not explicitly teach:
wherein the radar resource requirement prediction module is configured to estimate the future time-frequency resource requirement based on a pre-determined mapping between a radial distance to a target and a pre-determined energy.
Chen discloses coordination among automotive radars, and Leong is directed to an impulse radar with modes for short-range and long-range targets. Leong teaches:
wherein the radar uses resources based on a pre-determined mapping between a radial distance to a target and a pre-determined energy (see at least claim 7; “…the gated mode comprises transmitting pulses with a first amplitude and first frequency to detect long range targets and the non-gated mode comprises transmitting pulses with a second amplitude and second frequency to detect short range targets, wherein the first amplitude is higher than the second amplitude and the first frequency is less than the second frequency.” Examiner maps amplitude to energy.).
Chen teaches requesting radar resources from a scheduling device based on determined needs. Leong teaches reducing the amplitude and adjusting the frequency of the radar transmission when detecting short range targets compared to long range targets. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the resource manager used in Chen to include in its determination of radar requirements a mapping between the range of the target and the amplitude and frequency of the radar transmissions, as taught by Leong. One of ordinary skill would be motivated to include different modes for different ranges in order to offer both high SNR and the ability to detect at short and long ranges, as recognized by Leong (see Leong at least col. 1, line 58 – col. 2, line 3; “Embodiments described herein provide for a pulsed radar system alternatively operating in both a long-range (e.g., gated mode), and a short-range (e.g., non-gated mode). In the long-range mode, receiver blindness is supported for higher available Signal to Noise Ratio (SNR). In the short-range mode, the receiver detects target reflections or retransmissions overlapping with a direct feed-through signal from the mono-static transmitter to the receiver. Accordingly, the pulsed (e.g., impulse), radar systems described herein offer cost competitive detection of both short-range and long-range targets, as well as a dynamic means to tune operating modes of the radar system based on a combination of target characteristics.”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Markel et al. (US-12164054-B2; hereinafter Markel).
Regarding claim 10, Chen discloses the automotive radar transceiver system according to claim 9. However, Chen does not explicitly teach:
wherein the machine learning structure is arranged to be trained based on the environment data and corresponding time-frequency resource requests for radar operation at one or more vehicles.
Chen discloses coordination among automotive radars to reduce interference, and Markel is directed to radar interference reduction among autonomous vehicles. Markel teaches:
wherein the machine learning structure is arranged to be trained based on the environment data and corresponding time-frequency resource trends for radar operation at one or more vehicles (see at least col. 17, lines 8-18; “In addition, remote computing device 404 may be configured to perform processing on sensor data obtained by the vehicle radar system and/or other sensors of vehicle 402. For instance, remote computing device 404 may use deep learning (e.g., an artificial neural network) to detect trends within sensor data captured by vehicle sensors from multiple vehicles navigating different environments. The trends may be used to associate certain bandwidths (e.g., spectral regions) and/or other parameters with particular vehicles and/or emitters.”).
Chen teaches using a neural network in selecting radar resources to request from a scheduling device based on sensed environmental data, including detection of interference. Markel teaches using a neural network to analyze sensor data and detect trends relevant to interference, such as bandwidths likely to be occupied. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network used in Chen to include analysis of sensor data and its association with occupied bandwidths, as taught by Markel. One of ordinary skill would be motivated to include training the neural network on trends from the environmental data in order to identify sources of interference, as recognized by Markel (see Markel at least col. 17, lines 8-18).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Li et al. (US-11079497-B2; hereinafter Li).
Regarding claim 11, Chen discloses the automotive radar transceiver system according to claim 4. Chen further teaches [note, what Chen fails to teach is strikethrough]:
wherein the vehicle telematics system includes an at least partly trained machine learning structure (see at least [0084]; “FIG. 15 is a block diagram of an example processor platform 1500 structured to execute the instructions of FIGS. 4-5, 7, and 11-14 to implement the example radar unit 300 of FIG. 3. The processor platform 1500 can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network)…”) configured for predicting future radar resource requirements at a given geographic location or time of day (see at least [0032] – [0033]; “The example resource manager 316 of the illustrated example of FIG. 3 performs an initial setup process of the example radar unit 300, including assigning a unit ID, saving the current time and position information of the example radar unit 300, and obtaining the radar unit requirements. The example resource manager 316 accesses both the example radar unit database 318 and the example frequency blacklist database 320. In some examples, the example resource manager 316 implements means for retrieving radar unit requirements (e.g., a unit ID, current time information, vehicle position information, and radar resource requirements)… The example radar unit database 318 of the illustrated example of FIG. 3 stores the unique radar unit ID and requirements of the example radar unit 300 (e.g., latency requirements, frequency requirements, etc.), the time and position information from the example GPS antenna 304, the vehicle information (e.g., vehicle velocity) of the example vehicle 100, and the radar resource map created by the example resource multiplexer 321.”).
However, Chen does not explicitly teach receiving the at least partially trained machine learning structure.
Chen discloses coordination among automotive radars, and Li is directed to determining the location of a vehicle using vehicle sensor data, including radar data. Li teaches:
receiving an at least partially trained machine learning structure (see at least claim 1; “…the method including: receiving a current set of vehicle system data associated with the vehicle, the current set of vehicle system data including GPS location data, vehicle dynamics and image data generated by sensors or cameras mounted to the vehicle; receiving a trained neural network model, the trained neural network model being part of the trained neural network and being based upon a training set of vehicle data comprising GPS location data, vehicle dynamics and sensor data associated with one or more vehicles during a training phase, the trained neural network model comprising weights and biases associated with the training set of vehicle data…”).
The neural network of Chen uses current vehicle system data including GPS location data to determine radar resource requirements. The neural network of Li uses current vehicle system data including GPS location data to determine vehicle location. Li teaches that trained neural network model may be received by the vehicle. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network used in Chen to include the capability of being received by the vehicle in a trained form, as taught by Li. One of ordinary skill would be motivated to receive trained neural network models in order to use networks trained on data from other vehicles, as recognized by Li (see Li at least Abs; “The method includes receiving, at data processing hardware, a first set of vehicle system data from one or more vehicles. The method also includes determining, at the data processing hardware, a data model based on the first set of vehicle system data. Additionally, the method includes receiving, at the data processing hardware, a second set of vehicle system data associated with the vehicle. The vehicle being different than the one or more vehicles.”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley B. Raynal whose telephone number is (703)756-4546. The examiner can normally be reached Monday - Friday, 8 AM - 4 PM.
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, Vladimir Magloire can be reached at (571) 270-5144. 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.
/ASHLEY BROWN RAYNAL/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648