DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in Instant Application.
Priority
Examiner acknowledges Applicant’s claim to priority benefits of U.S. Pat. App. Ser. No. 18/227,548, filed July 28, 2023, now U.S. Patent No. 12,260,746, issued March 25, 2025, which is a continuation of U.S. Pat. App. Ser. No. 17/840,249, filed June 14, 2022, now U.S. Pat. No. 11,735,035, issued August 22, 2023, which is a continuation of U.S. Pat. App. Ser. No. 17/741,903, filed May 11, 2022, now U.S. Pat. No. 11,881,101, issued January 23, 2024, which is a continuation of U.S. Pat. App. Ser. No. 16/776,846, filed January 30, 2020, now U.S. Pat. No.11,430,328, issued August 30, 2022, which is a continuation of U.S. Pat. App. Ser. No. 16/135,916, filed September 19, 2018, now U.S. Pat. No. 10,692,365, issued June 23, 2020, which claims the benefit of U.S. Provisional Pat. App. Ser. No. 62/627,005, filed February 6, 2018 and is a continuation-in-part of and claims priority to U.S. Pat. App. Ser. No. 15/628,331, filed June 20, 2017, now U.S. Pat. No. 10,380,886, issued August 13, 2019, which claims the benefit of U.S. Provisional Pat. App. Ser. No. 62/507,453, filed May 17, 2017.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 02/11/2026 and 06/04/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
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.
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:
data collection module that collects in claims 1, 11, 19
computation resources module that performs in claims 1, 11, 19
data allocation module that allocates in claims 1, 11, 19
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. The following are the interpreted corresponding structures found within the specifications for some the above limitations:
data collection module
computation resources module
data allocation module
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.
Double Patenting
A rejection based on double patenting of the "same invention" type finds its support in the language of 35 U.S.C. 101 which states that "whoever invents or discovers any new and useful process ... may obtain a patent therefor ..." (Emphasis added). Thus, the term "same invention," in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957); and In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the conflicting claims so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are provisionally rejected on the ground of non-statutory non-obviousness-type double patenting as being unpatentable over claims 1-20 of Ran et al., co-pending Application 18/960,202. Although the claims at issue are not identical, they are not patentably distant from each other because they are drawn to obvious variations.
In view of the above, since the subject matters recited in the claims 1-20 of the instant application were fully disclosed in and covered by the claims 1-20 of US co-pending application 18/860,202, allowing the claims to result in an unjustified or improper timewise extension of the "right to exclude" granted by a patent.
Claims 1-2, 11-12, and 19-20 are provisionally rejected on the ground of non-statutory non-obviousness-type double patenting as being unpatentable over claims 1-2, 5, and 11-12 of Ran et al., co-pending Application 19/090,663. Although the claims at issue are not identical, they are not patentably distant from each other because they are drawn to obvious variations.
In view of the above, since the subject matters recited in the claims 1-2, 11-12, and 19-20 of the instant application were fully disclosed in and covered by the claims 1-2, 5, and 11-12 of US co-pending application 19/090,663, allowing the claims to result in an unjustified or improper timewise extension of the "right to exclude" granted by a patent.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims’ subject matter eligibility will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
With respect to claims 1, 11, and 19.
Claims 1, 11, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claims 1, 11, and 19 are directed to one of the statutory categories.
Step 2A Prong One Analysis: the claim recites, inter alia:
“provides customized and vehicle-specific information and computing services for an autonomous vehicle": A person of ordinary skill in the art can mentally and/or physically provide information and services. Thus, this limitation is construed to be directed to the abstract idea of mental processes.
“performs data processing”: A person of ordinary skill in the art can mentally and/or physically perform processing of data. Thus, this limitation is construed to be directed to the abstract idea of mental processes.
as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes concepts performed in the human mind (including an observation, evaluation, judgment, opinion) but for the recitation of generic computer components. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application. The only limitations not treated above, “collects data from one or more of: (a) a roadside unit (RSU) network, (b) a cloud platform, (c) an onboard unit (OBU) network, (d) a traffic control center/traffic control unit (TCC/TCU), (e) a traffic operations center (TOC)” and “allocates the data to computation resources that process the data”, involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g). In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of the “cloud-based learning system” is recited at a high level of generality, and comprises only a processor to simply perform the generic computer functions. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the communication step is considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites that the communication module is conventional transmitter/receiver mounted on the vehicle, and the specification does not provide any indication that this is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer. Thus, the claim is ineligible.
Dependent claims: Dependent claims(s) 2-10, 12-18, and claim 20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-10, 12-18, and claim 20 are not patent eligible under the same rationale as provided for in the rejection of claims 1, 11, and 19. Therefore, claim(s) 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claim 1, 11, and 19, claim element “data collection module that collects…", “computation resources module that performs…”, and “data allocation module that allocates…” are limitations that invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for the claimed function. However the specification does not show how these units are implemented in terms of hardware and detailed algorithm. The specification does not clearly associate circuits and corresponding detailed algorithm with corresponding hardware for claim element reciting element “data collection module that collects…", “computation resources module that performs…”, and “data allocation module that allocates…” in claims 1, 11, and 19.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph; or
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the claimed function, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The above cited rejections are merely exemplary.
The Applicant(s) are respectfully requested to correct all similar errors.
Claims not specifically mentioned are rejected by virtue of their dependency.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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 1-9, 11-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ricci (USPGPub 2021/0280055) in view of Kasuga (USPGPub 2019/0042863). As per claim 1, Ricci discloses a cloud-based learning system (CLS) for autonomous driving, said CLS comprising: a cloud system (see at least paragraph 0113; wherein the communications componentry can include one or more wired or wireless devices such as a transceiver(s) and/or modem that allows communications not only between the various systems disclosed herein but also with other devices, such as devices in the cloud) comprising: 1) a data collection module that collects data from one or more of: (a) a roadside unit (RSU) network, (b) a cloud platform, (c) an onboard unit (OBU) network, (d) a traffic control center/traffic control unit (TCC/TCU), (e) a traffic operations center (TOC) (see at least paragraph 0088; wherein the vehicle control system 348 may receive control information from one or more control sources 356B. The control source 356 may provide vehicle control information including autonomous driving control commands, vehicle operation override control commands, and the like. The control source 356 may correspond to an autonomous vehicle control system, a traffic control system, an administrative control entity, and/or some other controlling server); wherein the cloud system provides customized and vehicle-specific information and computing services for an autonomous vehicle (AV) (see at least paragraph 0246; wherein the control source 356B and control source database 1824 interact with the autonomous driving agent 1604 in each vehicle 100 to receive various types of information regarding vehicle behavior and the behaviors of nearby objects, such as other vehicles and pedestrians, identify specific behaviors and other autonomous driving information, and directly or indirectly provide the autonomous driving information to selected vehicles for use in determining and selecting various autonomous vehicle commands or settings, particularly acceleration rate of the vehicle, deceleration (e.g., braking) rate of the vehicle, steering angle of the vehicle (e.g., for turns and lane changes), and inter-object spacing (e.g., end-to-end or side-to-side spacing between the vehicle and a nearby object)); and wherein the computing services comprise a Storage as a service (STaaS), a Control as a service (CCaaS), a Computing as a service (CaaS), and/or a Sensing as a service (SEaaS) (see at least paragraph 0246; wherein the control source 356B and control source database 1824 interact with the autonomous driving agent 1604 in each vehicle 100 to receive various types of information regarding vehicle behavior and the behaviors of nearby objects, such as other vehicles and pedestrians, identify specific behaviors and other autonomous driving information, and directly or indirectly provide the autonomous driving information to selected vehicles for use in determining and selecting various autonomous vehicle commands or settings, particularly acceleration rate of the vehicle, deceleration (e.g., braking) rate of the vehicle, steering angle of the vehicle (e.g., for turns and lane changes), and inter-object spacing (e.g., end-to-end or side-to-side spacing between the vehicle and a nearby object)). Ricci does not explicitly mention 2) a computation resources module that performs data processing; and 3) a data allocation module that allocates the data to computation resources that process the data. However Kasuga does disclose: 2) a computation resources module that performs data processing (see at least paragraph 0056; wherein the sensing unit 23 performs sensing of sensor data output from the sensor 31, by using computational resources of an allocated amount, for the corresponding sensing, specified based on the allocation rate determined in step S11, and detects objects, such as obstacles and road signs, in a monitoring area. The sensing unit 23 generates sensing information indicating a detected object); and 3) a data allocation module that allocates the data to computation resources that process the data (see at least paragraph 0053’ wherein the control unit 21 determines an allocation rate of computational resources to be allocated to each of a plurality of sensing processes of analyzing sensor data output from a plurality of sensors for observing the area around the moving body). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Kasuga with the teachings as in Ricci. The motivation for doing so would have been to provide efficient sensing control, see Kasuga paragraph 0089. As per claims 2 and 12, Ricci discloses further comprising a component to provide a high- performance computation capability configured to allocate computation power to provide sensing, prediction, planning and decision making, and control at a microscopic level, a mesoscopic level, and/or a macroscopic level (see at least paragraph 0246; wherein the control source 356B and control source database 1824 interact with the autonomous driving agent 1604 in each vehicle 100 to receive various types of information regarding vehicle behavior and the behaviors of nearby objects, such as other vehicles and pedestrians, identify specific behaviors and other autonomous driving information, and directly or indirectly provide the autonomous driving information to selected vehicles for use in determining and selecting various autonomous vehicle commands or settings, particularly acceleration rate of the vehicle, deceleration (e.g., braking) rate of the vehicle, steering angle of the vehicle (e.g., for turns and lane changes), and inter-object spacing (e.g., end-to-end or side-to-side spacing between the vehicle and a nearby object)). As per claims 3 and 13, Ricci discloses wherein the data collection module integrates data from the RSU network, the AV, the TCC/TCU, or the TOC with data from the cloud (see at least paragraph 0088; wherein the vehicle control system 348 may receive control information from one or more control sources 356B. The control source 356 may provide vehicle control information including autonomous driving control commands, vehicle operation override control commands, and the like. The control source 356 may correspond to an autonomous vehicle control system, a traffic control system, an administrative control entity, and/or some other controlling server). As per claims 4 and 14, Kasuga discloses wherein the data allocation module is configured to divide the collected data into large parallel data and advanced control data (see at least paragraphs 0034-0036; wherein the CPU is a processor for executing programs, and performing processing such as data calculation. The DSP is a processor dedicated to digital signal processing, such as arithmetic calculation and data transfer. For example, processing of a digital signal, such as sensing of sensor data obtained from a sonar, is desirably processed at a fast speed by the DSP, instead of the CPU.The GPU is a processor dedicated to processing images, and is a processor which realizes fast processing by processing a plurality of pieces of pixel data in parallel). As per claims 5 and 14, Kasuga discloses wherein the data allocation module is configured to transmit the large parallel data and the advanced control data to the computation resources module for further processing (see at least paragraphs 0034-0036; wherein the CPU is a processor for executing programs, and performing processing such as data calculation. The DSP is a processor dedicated to digital signal processing, such as arithmetic calculation and data transfer. For example, processing of a digital signal, such as sensing of sensor data obtained from a sonar, is desirably processed at a fast speed by the DSP, instead of the CPU.The GPU is a processor dedicated to processing images, and is a processor which realizes fast processing by processing a plurality of pieces of pixel data in parallel). As per claims 6 and 15, Kasuga discloses wherein the data allocation module is configured to allocate processing of the collected data to computation resources according to a computation resource allocation (see at least paragraph 0053; wherein control unit 21 determines an allocation rate of computational resources to be allocated to each of a plurality of sensing processes of analyzing sensor data output from a plurality of sensors for observing the area around the moving body). As per claims 7 and 16, Kasuga discloses wherein the computation resources comprise: graphic processing units (GPUs) to process large parallel data (see at least paragraph 0036; wherein the GPU is a processor dedicated to processing images, and is a processor which realizes fast processing by processing a plurality of pieces of pixel data in parallel); and central processing units (CPUs) to process advanced control data (see at least paragraph 0034; wherein the CPU is a processor for executing programs, and performing processing such as data calculation). As per claim 8, Ricci discloses wherein the cloud system provides the computation resources (see at least paragraph 0113; wherein the communications componentry can include one or more wired or wireless devices such as a transceiver(s) and/or modem that allows communications not only between the various systems disclosed herein but also with other devices, such as devices in the cloud). As per claims 9 and 17, Ricci discloses wherein the computation resources are provided by one or more of the following physical subsystems: the RSU network, the cloud platform, the OBU network, the TCC/TCU, and the TOC (see at least paragraph 0088; wherein the vehicle control system 348 may receive control information from one or more control sources 356B. The control source 356 may provide vehicle control information including autonomous driving control commands, vehicle operation override control commands, and the like. The control source 356 may correspond to an autonomous vehicle control system, a traffic control system, an administrative control entity, and/or some other controlling server). As per claim 11, Ricci discloses a cloud-based learning system (CLS) for autonomous driving, comprising: a cloud system (see at least paragraph 0113; wherein the communications componentry can include one or more wired or wireless devices such as a transceiver(s) and/or modem that allows communications not only between the various systems disclosed herein but also with other devices, such as devices in the cloud); and one or more of the following physical subsystem(s): (a) a roadside unit (RSU) network, (b) an onboard unit (OBU) network, (c) a traffic control center/traffic control unit (TCC/TCU), (d) a traffic operations center (TOC) (see at least paragraph 0088; wherein the vehicle control system 348 may receive control information from one or more control sources 356B. The control source 356 may provide vehicle control information including autonomous driving control commands, vehicle operation override control commands, and the like. The control source 356 may correspond to an autonomous vehicle control system, a traffic control system, an administrative control entity, and/or some other controlling server), wherein the cloud system comprises: 1) a data collection module that collects data from one or more of: (a) a roadside unit (RSU) network, (b) a cloud platform, (c) an onboard unit (OBU) network, (d) a traffic control center/traffic control unit (TCC/TCU), (e) a traffic operations center (TOC) (see at least paragraph 0088; wherein the vehicle control system 348 may receive control information from one or more control sources 356B. The control source 356 may provide vehicle control information including autonomous driving control commands, vehicle operation override control commands, and the like. The control source 356 may correspond to an autonomous vehicle control system, a traffic control system, an administrative control entity, and/or some other controlling server); wherein the cloud system provides customized and vehicle-specific information and computing services for an autonomous vehicle (AV) (see at least paragraph 0246; wherein the control source 356B and control source database 1824 interact with the autonomous driving agent 1604 in each vehicle 100 to receive various types of information regarding vehicle behavior and the behaviors of nearby objects, such as other vehicles and pedestrians, identify specific behaviors and other autonomous driving information, and directly or indirectly provide the autonomous driving information to selected vehicles for use in determining and selecting various autonomous vehicle commands or settings, particularly acceleration rate of the vehicle, deceleration (e.g., braking) rate of the vehicle, steering angle of the vehicle (e.g., for turns and lane changes), and inter-object spacing (e.g., end-to-end or side-to-side spacing between the vehicle and a nearby object)); and wherein the computing services comprise a Storage as a service (STaaS), a Control as a service (CCaaS), a Computing as a service (CaaS), and/or a Sensing as a service (SEaaS) (see at least paragraph 0246; wherein the control source 356B and control source database 1824 interact with the autonomous driving agent 1604 in each vehicle 100 to receive various types of information regarding vehicle behavior and the behaviors of nearby objects, such as other vehicles and pedestrians, identify specific behaviors and other autonomous driving information, and directly or indirectly provide the autonomous driving information to selected vehicles for use in determining and selecting various autonomous vehicle commands or settings, particularly acceleration rate of the vehicle, deceleration (e.g., braking) rate of the vehicle, steering angle of the vehicle (e.g., for turns and lane changes), and inter-object spacing (e.g., end-to-end or side-to-side spacing between the vehicle and a nearby object)). Ricci does not explicitly mention wherein said one or more physical subsystem(s) provide(s) the cloud system with additional computation resources when required; 2) a computation resources module that performs data processing; and 3) a data allocation module that allocates the data to computation resources that process the data. However Kasuga does disclose: wherein said one or more physical subsystem(s) provide(s) the cloud system with additional computation resources when required (see at least paragraph 0105; wherein the front sensing is the only sensing process with a high level of importance, but in the case where two or more sensing processes are determined to have a high level of importance, control may be performed in such a way that the level of accuracy of sensing in an area with the highest level of importance is increased as much as possible within the available computational resources, or in such a way that the levels of accuracy are increased in a balanced manner according to the levels of importance); 2) a computation resources module that performs data processing (see at least paragraph 0056; wherein the sensing unit 23 performs sensing of sensor data output from the sensor 31, by using computational resources of an allocated amount, for the corresponding sensing, specified based on the allocation rate determined in step S11, and detects objects, such as obstacles and road signs, in a monitoring area. The sensing unit 23 generates sensing information indicating a detected object); and 3) a data allocation module that allocates the data to computation resources that process the data (see at least paragraph 0053’ wherein the control unit 21 determines an allocation rate of computational resources to be allocated to each of a plurality of sensing processes of analyzing sensor data output from a plurality of sensors for observing the area around the moving body). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Kasuga with the teachings as in Ricci. The motivation for doing so would have been to provide efficient sensing control, see Kasuga paragraph 0089. As per claim 19, Ricci discloses a cloud-based learning system (CLS) for autonomous driving, comprising: a cloud system (see at least paragraph 0113; wherein the communications componentry can include one or more wired or wireless devices such as a transceiver(s) and/or modem that allows communications not only between the various systems disclosed herein but also with other devices, such as devices in the cloud) comprising: 1) a data collection module that collects data from one or more of: (a) a roadside unit (RSU) network, (b) a cloud platform, (c) an onboard unit (OBU) network, (d) a traffic control center/traffic control unit (TCC/TCU), (e) a traffic operations center (TOC) (see at least paragraph 0088; wherein the vehicle control system 348 may receive control information from one or more control sources 356B. The control source 356 may provide vehicle control information including autonomous driving control commands, vehicle operation override control commands, and the like. The control source 356 may correspond to an autonomous vehicle control system, a traffic control system, an administrative control entity, and/or some other controlling server); wherein the cloud system is configured to optimize and generate detailed customized information and time-sensitive control instructions for an autonomous vehicle (AV) by processing data through a model to fulfill driving tasks and provide operations and maintenance services for the AV (see at least paragraph 0247; wherein control source databases 1816 and 1824 can be constructed according to any data model, whether conceptual, logical, or physical, such as a flat model, hierarchical model, network model, relational model, object-relational model, star schema, entity-relationship model, geographic model, generic model, semantic model, and the like). Ricci does not explicitly mention 2) a computation resources module that performs data processing; and 3) a data allocation module that allocates the data to computation resources that process the data. However Kasuga does disclose: 2) a computation resources module that performs data processing (see at least paragraph 0056; wherein the sensing unit 23 performs sensing of sensor data output from the sensor 31, by using computational resources of an allocated amount, for the corresponding sensing, specified based on the allocation rate determined in step S11, and detects objects, such as obstacles and road signs, in a monitoring area. The sensing unit 23 generates sensing information indicating a detected object); and 3) a data allocation module that allocates the data to computation resources that process the data (see at least paragraph 0053’ wherein the control unit 21 determines an allocation rate of computational resources to be allocated to each of a plurality of sensing processes of analyzing sensor data output from a plurality of sensors for observing the area around the moving body). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Kasuga with the teachings as in Ricci. The motivation for doing so would have been to provide efficient sensing control, see Kasuga paragraph 0089.
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ricci (USPGPub 2021/0280055), in view of Kasuga (USPGPub 2019/0042863), and further in view of Konrardy et al. (USPGPub 2023/0143946). As per claims 10 and 18, Ricci and Kasuga do not explicitly mention wherein the computation resources are used for data processing to provide the prediction, planning, and decision making functionality of the AV. However Konrardy does disclose: wherein the computation resources are used for data processing to provide the prediction, planning, and decision making functionality of the AV (see at least paragraph 0007; wherein this virtual testing may include presentation of fixed inputs or may include a simulation of a dynamic virtual environment in which a virtual vehicle is controlled by the one or more autonomous operation features. The one or more autonomous operation features generate output signals that may then be used to determine the effectiveness of the control decisions by predicting the responses of vehicles to the output signals. Risk levels associated with the effectiveness of the autonomous operation features may be used to determine a premium for an insurance policy associated with the vehicle, which may be determined by reference to a risk category). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Konrardy with the teachings as in Ricci and Kasuga. The motivation for doing so would have been to improve the effectiveness of the autonomous operation features, see Konrardy paragraph 0079.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Ricci (USPGPub 2021/0280055), in view of Kasuga (USPGPub 2019/0042863), and further in view of Moghe et al. (USPGPub 2018/0299274). As per claim 20, Ricci and Kasuga do not explicitly mention wherein the model is a learning based model, a statistical model, and/or an empirical model. However Moghe does disclose: wherein the model is a learning based model, a statistical model, and/or an empirical model (see at least paragraph 0022; wherein data 234 may also include traffic pattern model information 238, e.g., a highly detailed model indicating the distribution of typical or expected speeds, trajectories, locations, accelerations/decelerations (changes in speed), or other such characteristics of vehicles or other moving objects on the locations of the map 236. This data may be generated, for example, by observing how vehicles, pedestrians, bicycles, etc. move at different locations in the map 236. That is, data for the traffic pattern model information 238 might be generated based on long term observation and learning of traffic patterns). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Moghe with the teachings as in Ricci and Kasuga. The motivation for doing so would have been to provide vehicle safety and efficiently navigate the vehicle without input from the operator, see Moghe paragraph 0003.
Relevant Art
The prior art made of record and not relied upon are considered pertinent to applicant’s disclosure: USPGPub 2020/0410787 – Provide a system and method that includes collecting vehicle sensor data, wherein prioritizing vehicle sensor data includes identifying a level of importance for each of a plurality of vehicle sensor data types included in the vehicle sensor data; generating a vehicle sensor data schedule, wherein generating the vehicle data schedule includes one or more of (i) identifying a transmission order for each of the plurality of vehicle sensor data types and (ii) identifying a storage scheme selected from a hierarchy of data storage types for each of the plurality of vehicle sensor data types; transforming vehicle sensor data into message data, wherein the transforming includes selectively converting one or more of the vehicle sensor data types of the vehicle sensor data to a messaging format based on the prioritization; and transmitting, via one or more selected communication networks, the message data according to the vehicle sensor data schedule. USPGPub 2006/0025897 – Provide sensor assembly capable of obtaining and providing a measurement of a physical quantity, e.g., measurement of temperature and/or pressure of a vehicular tire, includes an antenna capable of receiving a radio frequency signal, a radio frequency identification (RFID) device coupled to the antenna, a sensor coupled to the RFID device arranged to generate a measurement of the physical quantity or quantities, and a switch coupled to the RFID device and arranged to connect or disconnect the sensor from a circuit with the antenna dependent on whether the antenna receives a particular signal associated with the RFID device. When the antenna receives the particular signal associated with the RFID device, the RFID device causes the switch to close and connect the sensor in the circuit with the antenna to enable the measurement generated by the sensor to be directed to and transmitted by the antenna.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHMOUD S ISMAIL whose telephone number is (571)272-1326. The examiner can normally be reached M - F: 8:00AM- 4:00PM.
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, Jelani Smith can be reached at 571-270-3969. 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.
/MAHMOUD S ISMAIL/Primary Examiner, Art Unit 3662
Prosecution Insights