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 .
Response to Amendment
The following is a final office action in response to the communication filed on 06/16/2026. Claims 1, 2 and 15 have been amended. Claims 1-15 are currently pending and have been examined.
Response to Arguments
Applicant’s arguments and remarks filed on 06/16/2026 have been fully considered.
Applicant’s amendments overcome the objections to the specification.
Applicant’s amendments overcome the objections to the claims.
Applicant’s arguments provided for the U.S.C. §112(f) interpretation of claim 1 have been considered but are not persuasive.
(A) Applicant argues, “The following features of Claim 1 were indicated as being interpreted under 35 U.S.C. 112(f) as means-plus-function limitations: "an environment perception system arranged to obtain environment data indicative of a surrounding traffic environment of the radar transceiver;" and "a radar resource requirement prediction module configured to estimate a future time-frequency resource requirement for radar operation, based on the environment data." Applicant respectfully traverses.
“A person having ordinary skill in the art would understand each of the "environment perception system" and the "radar resource requirement prediction module" to describe structural components of the claimed automotive radar transceiver system. The "arranged to" and "configured to" specifying language does not transform either of the cited structural features into means-plus-function limitations.
“A device configured to perform a specified function or operation is structurally different from a device not so configured. See Ex Parte Nix, 2008 WL 4338020 / 2008- 1747, at 6 (BPAI Sep 22, 2008) (Fischetti, concurring). As explained in Nix, "the word 'configured' requires us to interpret the term as causing a non-transient condition in the [device] worthy of interpreting the function so configured as a positive recitation." Id.
“It is both common and permissible to define a structural element by reference to the function it performs rather than solely by structural language. See, e.g., section 2173.05(g) of the M.P.E.P.:
“A claim term is functional when it recites a feature "by what it does rather than by what it is" (e.g., as evidenced by its specific structure or specific ingredients). In re Swinehart, 439 F.2d 210, 212, 169 USPQ 226, 229 (CCPA 1971). There is nothing inherently wrong with defining some part of an invention in functional terms. Functional language does not, in and of itself, render a claim improper. Id. In fact, 35 U.S.C. 112(f) and pre-AIA 35 U.S.C. 112, sixth paragraph, expressly authorize a form of functional claiming (means- (or step-) plus- function claim limitations discussed in MPEP § 2181 et seq.). Functional language may also be employed to limit the claims without using the means-plus-function format. See, e.g., K-2 Corp. v. Salomon S.A., 191 F.3d 1356, 1363 (Fed. Cir. 1999).
“Unlike means-plus-function claim language that applies only to purely functional limitations,... functional claiming often involves the recitation of some structure followed by its function. For example, in In re Schreiber, the claims were directed to a conical spout (the structure) that "allow[ed] several kernels of popped popcorn to pass through at the same time" (the function). In re Schreiber, 128 F.3d 1473, 1478 (Fed. Cir. 1997). As noted by the court in Schreiber, "[a] patent applicant is free to recite features of an apparatus either structurally or functionally." Id. (Emphasis added)
“Accordingly, applicant respectfully requests reconsideration of the 35 U.S.C. 112(f) interpretation of the terms: "an environment perception system arranged to obtain environment data indicative of a surrounding traffic environment of the radar transceiver," and "a radar resource requirement prediction module configured to estimate a future time- frequency resource requirement for radar operation, based on the environment data,"” (from remarks pages 7-8).
As to point (A), Examiner respectfully disagrees. Applicant asserts that a person of ordinary skill would understand the “environment perception system” and the “radar resource requirement prediction module” to describe structural components, and that the functional language tied to these components itself provides them structure. In assessing whether a claim is to be interpreted under 35 U.S.C. 112(f), MPEP § 2181, subsection I gives a three-prong test:
(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.
The language of claim 1 fulfill all three requirements of the three-prong test. Both “system” and “module” are listed in MPEP § 2181, subsection I(A) as nonce terms (“The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for."”), thus fulfilling the first prong of the three-prong test. Both “system” and “module” are modified by functional language, as acknowledged by the Applicant, thus fulfilling the second prong of the three-prong test. Finally, Applicant arguments do not show that the claim limitations recite sufficient structure, material, or acts to entirely perform the recited functions “obtain environment data” and “estimate a future time-frequency resource requirement”. The functional language itself does not provide sufficient structure to perform the functional language. Therefore, the interpretation of claim 1 under 35 U.S.C. 112(f) is maintained.
Applicant’s arguments provided for the U.S.C. §102 and §103 rejections of claims 1-15 have been considered but are not persuasive.
(A) Applicant argues, “As applied to independent claim 1, the Office Action relies on Chen's resource manager 316 and radar unit database 318 as allegedly estimating a future time-frequency resource requirement, and relies on Chen's vehicle status reporting to a master node as allegedly requesting resources based on that estimate. See Office Action 13-14. As applied to independent method claim 15, the Office Action relies on substantially the same disclosures. See Office Action 22. These cited disclosures do not disclose the claimed prediction-and-request arrangement.
“Anticipation requires that a single reference disclose each and every limitation of the claimed invention, arranged as in the claim. See MPEP § 2131. Claim 1 requires a radar resource requirement prediction module configured to "estimate a future time- frequency resource requirement for radar operation, based on the environment data," and further requires that the radar transceiver "request time-frequency resources for radar operation from the remote scheduler function based on the estimated future time- frequency resource requirement." Claim 15 recites the corresponding method steps of estimating the future time-frequency resource requirement and requesting time-frequency resources based on that estimate. These limitations require more than obtaining or storing present radar unit requirements. The Specification confirms this forward-looking arrangement: the request is made sooner, "before the need for additional radar transmission resources even arises," by predicting what radar resources the vehicle "will require in the future," so that the resources can be available before they are actually required. See Specification 0053, 0061-0062.
“Chen's cited passages do not disclose that arrangement. Chen 0032 states that resource manager 316 performs an initial setup process by assigning a unit ID, saving current time and position information, and obtaining radar unit requirements. Chen 0033 describes database 318 as storing the unit ID and requirements, current time and position information, vehicle information, and a radar resource map. Similarly, Chen 0050-0051 describe setup operations in which current time and current position are saved, radar unit requirements are obtained from the database, and a radar resource map is formed and stored. These disclosures concern retrieval and storage of current or existing requirements for setup and multiplexing; they do not expressly or inherently disclose estimating a future time-frequency resource requirement based on environment data.
“Nor does Chen's vehicle-status exchange disclose a request based on such a predicted future requirement. Chen 0058 describes vehicles exchanging respective vehicle statuses, such as position and radar unit requirements, proposing or confirming multiplexing schemes, and assigning non-conflicting radar resource groups. Chen 0063, 0065, and 0069 further describe interference detection and resource hopping in response to detected interference. These are coordination and interference-mitigation operations, not a preemptive request to a remote scheduler based on a predicted future time-frequency resource need. The Office Action's reliance on Chen's reporting of vehicle status therefore incorrectly collapses two distinct claim requirements—prediction of a future requirement and requesting resources based on that prediction—into generic status communication and current resource coordination,” (from remarks pages 9-10).
As to point (A), Examiner respectfully disagrees. Applicant asserts that Chen concerns current or existing requirements for setup and multiplexing rather than estimation future time-frequency resource requirements. However, the claims do not specify at what point in the future the estimate of future requirements is directed to. Because this time for which future requirements are estimated is not limited, this future time could be 1 minute, 1 second, or an even shorter time in the future. The vehicle of Chen requests time-frequency resources from a master node and subsequently receives assigned resources from the master node (see flow chart of Fig. 9). Thus, the estimate for the required resources of Chen is at least for the point in time in the future when resources are assigned by the master node, and it may be reasonably described as an estimate for future time-frequency resources.
In response to applicant's argument that the references fail to show certain features of the invention (i.e., the request being made “before the need for additional transmission resources even arises"), it is noted that the features upon which applicant relies are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
(B) Applicant argues, “For at least the reasons set forth above, Chen does not disclose every limitation of the independent claims 1 and 15, as arranged therein. Applicant respectfully requests reconsideration and withdrawal of the rejection. Because Chen does not disclose at least the claimed estimation of a future time-frequency resource requirement based on environment data and the claimed resource request based on that estimated future requirement, Chen does not anticipate either of the independent claims 1 or 15. Dependent claims 2-5, 8-9, and 14 are patentable for at least the same reasons by virtue of their dependence from claim 1, and for their additional recited features,” (from remarks page 10).
As to point (B), see point (A).
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], where vehicle status is shown to include radar unit requirements: “…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 has estimated 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], where vehicle status is shown to comprise radar unit requirements: “…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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ASHLEY BROWN RAYNAL/Examiner, Art Unit 3648
/OLUMIDE AJIBADE AKONAI/Primary Examiner, Art Unit 3648
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