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 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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: the communication unit configured to, a learning unit configured to, an acquisition unit configured to, a surrounding situation detection unit configured to, an evaluation unit configured to and a determination unit configured to with respect to claims 1-2. Figure 2 of the applicant’s specification includes hardware configuration of the in-vehicle device; figure 2 includes the hardware communication unit 120, vehicle inside-outside coordination unit 122; figure 6 is a detailed illustration of the hardware inside the inside-outside coordination unit 122. Figures 2 and 6 appear to show all the hardware units recited in claims 1-2.
Claim 7 recites a communication unit configured to, a learning unit configured to, an acquisition unit configured to, a surrounding situation detection unit configured to, an evaluation unit configured to, a determination unit configured to and a model configured to; figures 2 and 6 appear to show the hardware units recited in claim 7.
Claim 8 recited a communication unit configured to and a learning unit configured to; figures 2 and 6 appear to show the hardware units recited in claim 7
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 8, 12-14, 16, 18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sabella et al. (US Publication 2021/0112441 A1).
In regards to claims 1, 14 and 18 Sabella et al. (US Publication 2021/0112441 A1) teaches, an in-vehicle device installed in a vehicle, comprising: a communication unit configured to receive reception target data from a roadside device that is a device located outside the vehicle (see figure 2, MEC apps 206 and paragraph 86; the in-vehicle MEC apps 206 collect data and measurements from V2X, sensors, cameras, etc., as well as from local network of user devices within short range, and share the collected data with other MEC apps) a learning unit configured to learn a reception condition for the reception target data by evaluating an extent to which the reception target data received by the communication unit has been used by a function control device installed in the vehicle, wherein the learning unit specifies the reception condition, during a learning period in which the communication unit repeatedly receives the reception target data (see paragraph 98; The local PF 302 performs PQoS predictions based on journey-specific input and may be implemented via a machine-learning model, e.g., neural network, or a logistic regression model. To train the PQoS prediction model, the in-vehicle MEC app 206 may obtain, for training purposes, several kinds of information from different sources, such as radio measurements collected by the car, outputs from sensors, information from user devices in the car, and information from nearby vehicles and devices), and the communication unit receives the reception target data according to whether or not the reception condition is satisfied, after the reception condition has been specified by the learning unit (see figure 17 and paragraph 183; the method 1700 further comprises detecting, by the in-vehicle MEC application, that accuracy of the local QoS prediction model is below a predetermined threshold; and requesting, via the central MEC application, updated parameters of the global QoS prediction model).
In regards to claims 8, 16 and 20 Sabella teaches, an in-vehicle device installed in a vehicle, comprising: a communication unit configured to transmit transmission target data to a roadside device that is a device located outside the vehicle (see paragraph 82; the PQoS system is supported by an application-level communication protocol between in-vehicle MEC apps 206 and the central MEC app 702. This enables the in-vehicle construction of the city's predicted digital twin based on the communicated parameters of the global PQoS prediction model; see paragraph 39; The V2X applications 210 can use co-operative awareness to provide more intelligent services for end-users. This means that entities, such as vehicle stations or vehicle user equipment (vUEs) including such as CA/AD vehicles, roadside infrastructure or roadside units (RSUs)), and receive, from the roadside device (see figure 2, MEC apps 206 and paragraph 86; the in-vehicle MEC apps 206 collect data and measurements from V2X, sensors, cameras, etc., as well as from local network of user devices within short range, and share the collected data with other MEC apps), an evaluation index that evaluates a service provided by the roadside device (see paragraph 113; The accuracy of the PQoS model is monitored over time by analyzing quality metrics of results, such as user ratings of different routes contributed by various connected vehicles. When the quality metrics fall below a predetermined threshold, stage 1 (see FIGS. 8A and 8B) is performed to re-train the PQoS model by the central PF); and a learning unit configured to learn a transmission condition for the transmission target data by using the evaluation index, wherein the learning unit specifies the transmission condition, during a learning period in which the communication unit repeatedly transmits the transmission target data (see paragraph 98; The local PF 302 performs PQoS predictions based on journey-specific input and may be implemented via a machine-learning model, e.g., neural network, or a logistic regression model. To train the PQoS prediction model, the in-vehicle MEC app 206 may obtain, for training purposes, several kinds of information from different sources, such as radio measurements collected by the car, outputs from sensors, information from user devices in the car, and information from nearby vehicles and devices; collecting radio measurements implies transmissions and receptions), and the communication unit transmits the transmission target data according to whether or not the transmission condition is satisfied, after the transmission condition has been specified by the learning unit (see paragraph 83; the conventional embodiment transmits raw data such as radio signal quality measurements or MEc resource use telemetry measurements).
In regards to claim 12, Sabella teaches, A roadside device that communicates with the in- vehicle device according to claim 8 (see paragraph 60; roadside units (RSUs); the RANs 502-503 are RSUs), and receives the transmission target data (see paragraph 112; Stage 0 may be repeated periodically (e.g., daily, outside rush hours) once a sufficient number of time-stamped measurements is collected by the RAN and the MEC host), the roadside device comprising: a service execution unit configured to execute a predetermined service (see paragraph 113; When the quality metrics fall below a predetermined threshold, stage 1 (see FIGS. 8A and 8B) is performed to re-train the PQoS model by the central PF); an evaluation unit configured to generate an evaluation index indicating an extent to which the transmission target data has been used by the service execution unit (see paragraphs 117-118; at stage 3, the local models stored in the in-vehicle MEC hosts are updated by each vehicle's local PF based on the downloaded parameters of the global model and the local sensory data coming from RADAR, LiDAR, cameras, and other sensors installed both in the same vehicle (including passenger devices such as smartphones) and in nearby vehicles and VRU devices within short range (V2V, I2V signals); Further, at stage 4A within the loop 801, the parameters of the updated local models for joint radio/edge cloud QoS predictions are forwarded (utilizing in-vehicle VIS, VIS, and via the in-vehicle and central MEC apps) from the local PF inside the in-vehicle MEC app 206 to the central PF in the MEC host 808 hosting the global model), and a communication unit configured to transmit the evaluation index to the in-vehicle device (see paragraphs 123; At stage 6 (outside the loop 801) the PQoS global model is forwarded by the central PF (utilizing VIS, in-vehicle VIS and the central and in-vehicle MEC Apps) to the local PFs together with a notification informing of training completion. The recipients of the converged global model parameters may be model contributors or entirely new devices with processing capabilities which are under coverage).
In regards to claim 13, Sabella teaches, A roadside device (see paragraph 60; roadside units (RSUs); the RANs 502-503 are RSUs) comprising: a communication unit configured to communicate with an in-vehicle device installed in a vehicle, and receive transmission target data (see paragraph 112; Stage 0 may be repeated periodically (e.g., daily, outside rush hours) once a sufficient number of time-stamped measurements is collected by the RAN and the MEC host); a service execution unit configured to execute a predetermined service (see paragraph 113; When the quality metrics fall below a predetermined threshold, stage 1 (see FIGS. 8A and 8B) is performed to re-train the PQoS model by the central PF); and a learning unit configured to learn a transmission condition for the transmission target data by the in-vehicle device, by evaluating an extent to which the transmission target data received by the communication unit has been used by the service execution unit, wherein the learning unit specifies the transmission condition, during a learning period in which the communication unit receives the transmission target data that is repeatedly transmitted (see paragraphs 117-118; at stage 3, the local models stored in the in-vehicle MEC hosts are updated by each vehicle's local PF based on the downloaded parameters of the global model and the local sensory data coming from RADAR, LiDAR, cameras, and other sensors installed both in the same vehicle (including passenger devices such as smartphones) and in nearby vehicles and VRU devices within short range (V2V, I2V signals); Further, at stage 4A within the loop 801, the parameters of the updated local models for joint radio/edge cloud QoS predictions are forwarded (utilizing in-vehicle VIS, VIS, and via the in-vehicle and central MEC apps) from the local PF inside the in-vehicle MEC app 206 to the central PF in the MEC host 808 hosting the global model), and the communication unit transmits the transmission condition specified by the learning unit to the in-vehicle device (see paragraphs 123; At stage 6 (outside the loop 801) the PQoS global model is forwarded by the central PF (utilizing VIS, in-vehicle VIS and the central and in-vehicle MEC Apps) to the local PFs together with a notification informing of training completion. The recipients of the converged global model parameters may be model contributors or entirely new devices with processing capabilities which are under coverage).
Allowable Subject Matter
Claims 7, 11, 15, 17, 19 and 21 are allowed.
In regards to the claim 7, the cited prior art fails to particularly teach, the evaluation and determination including the comparing the evaluation index obtained with the reception target data that has been received with the evaluation index obtained when the reception target data has not been received during the predetermined period as claimed.
In regards to the claim 11, the cited prior art fails to particularly teach, the evaluation and determination including the comparing the evaluation index obtained with the transmission target data that has been received with the evaluation index obtained when the transmission target data has not been received during the predetermined period as claimed.
Claims 2-6 and 9-10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
In regards to the claims, the cited prior art fails to particularly teach, the evaluation and determination including the comparing the evaluation index obtained with the reception target data that has been received with the evaluation index obtained when the reception target data has not been received during the predetermined period as claimed.
In regards to the claims, the cited prior art fails to particularly teach, the evaluation and determination including the comparing the evaluation index obtained with the transmission target data that has been received with the evaluation index obtained when the transmission target data has not been received during the predetermined period as claimed.
Prior art Higuchi et al. (US Publication 2023/0085360 A1) teaches, identifying parking spots based on congestion-dependent parking navigation preferences using machine learning modules.
Prior art Haga et al. (US Publication 2023/0247038 A1) teaches, an example of cooperative operations between the anomaly detection server and a vehicle (see figure 10); figure 11 shows an example of an anomaly detection process in the anomaly detection server; figure 13 shows a sequence diagram illustrating exemplary operations of the delivery of fraud detection information (such as rules) by the anomaly detection server.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY P PATEL whose telephone number is (571)272-3086. The examiner can normally be reached M-F 9:30-6.
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, Faruk Hamza can be reached at 571-272-8786. 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.
/JAY P PATEL/ Primary Examiner, Art Unit 2466
Prosecution Insights