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 . This office action is in response to an application filed on 9/20/23.
Claims 1-10 are pending.
Information Disclosure Statement
The information disclosure statement submitted on 11/03/2023 have been considered by the
Examiner and made of record in the application.
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 the driving intention recognition unit is configured to acquire the driving intention of the vehicle recognized according to the state information of the vehicle in claim 8, communication unit is configured to transmit the driving intention information of the vehicle and the state information and position information of the vehicle to the cloud dispatching system in claim 8, the output unit is configured to receive a global dispatching result of driving intentions of driving dispatching area vehicles transmitted by the cloud dispatching system in claim 8.
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. See at least,
a driving intention recognition unit , a communication unit , and an output unit
Pg. 17 - [lines 9-11] – “In the present embodiment, the vehicle-mounted driving intention control system 20 includes a driving intention recognition unit 21, a communication unit 22 and an output unit 23”
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.
Regarding claim 8, it discloses claim limitations: “a driving intention recognition unit , a communication unit , and an output unit” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. As seen in the specification – Pg. 17 - [lines 9-11] – “In the present embodiment, the vehicle-mounted driving intention control system 20 includes a driving intention recognition unit 21, a communication unit 22 and an output unit 23” yet the disclosure is devoid of any structure that performs the function in the claim as no definite structure is given to the “vehicle-mounted driving intention control system”. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
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 pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, 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 and clearly links them to the function 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.
Hybrid Claims
A claim that recites both an apparatus and a method of using that apparatus renders a claim
indefinite under section 112, paragraph 2—the precursor statute to 35 U.S.C. § 112(b). Ex parte Lyell, 17
USPQ2d 1548 (BPAT 1990); see also IPXL Holdings, L.L.C. v. Amazon.com, Inc., 430 F.3d 1377, 1384
(Fed. Cir. 2005) (citing Lyell with approval). In Lyell, the Board’s predecessor tribunal further noted “the statutory class of invention is important in determining patentability and infringement.” Id. at 1550
(citing In re Kuehl, 475 F.2d 658, 665 (CCPA 1973); Providence Rubber Co. v. Goodyear, 76 U.S. 788,
796 (1869)). See MPEP 2173.05(p)(II)
Dependent Claim 6 purports to be directed to a machine: “A system for regionally co-dispatching driving intentions of intelligent vehicles…” Claim 6 further recites a system comprising a global dispatching result generation module, information acquisition module, a global driving intention graph generation module, and a map model construction module. However the last line of the claim reads as “the cloud dispatching system is configured to perform the method for regionally co- dispatching driving intentions of intelligent vehicles according to claim 1” which is in line with a method step rather than a structural limitation.
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.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Pg. 16 - [lines 4-5] – “The specific implementation of each of the above-mentioned modules in the present embodiment may be seen in Embodiment 1, and detailed descriptions thereof will be omitted herein”, Pg. 15 – [lines 13-16] – “The present embodiment discloses an apparatus for regionally co-dispatching driving intentions of intelligent vehicles. As shown in Fig. 4, the apparatus includes an information acquisition module, a global driving intention graph generation module, a map model construction module, and a global dispatching result generation module.” And see Pg. 15 - [lines 1-3] – “method of the present embodiment may be completed by a program that instructs associated hardware, and that the corresponding program may be stored in a computer-readable storage medium.” The words: program may be stored were bolded to show how stating the apparatus may be a program in a computer readable storage medium and may also include signals/software per se as the listed modules comprising the apparatus doesn’t give a definite structure corresponding to the modules and thus the apparatus may comprise parts or be in its entirety software per se. Which is not one of the 4 eligible subject matter that is patentable.
Claims 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Similarly claim 9 does not fall within at least one of the four categories of patent eligible subject matter because claim 9 states “a storage medium …” and when looking into the specification of what the apparatus actually is the definition was provided to be Pg. 20 - [lines 21-23] – “In the present embodiment, the storage medium may be a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a U disk, a removable hard disk, or the like.” The words: may be were bolded to show how stating the storage medium may be any of the given hardware components may also then include software per se as the computer readable storage medium isn’t limited to strictly non-transitory storage medium which is not one of the 4 eligible subject matter that is patentable. See MPEP 2106.03.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
101 Analysis – Step 1
Claims 1-4, and 6-8 and 10 are directed to A method for regionally co-dispatching driving intentions of intelligent vehicles, (i.e., a process). Therefore, claims 1-4, and 6-8 and 10 are within at least one of the four statutory categories.
Claim 5 and 9 are directed to An apparatus and storage medium for regionally co-dispatching driving intentions of intelligent vehicles (i.e., a machine). But the machine and storage medium as shown above aren’t limited strictly to non-transitory media and therefor is not within one of the four patent eligible categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (mental process) and will be used as a representative claim for the remainder of the 101 rejections. Independent claim 1, and 5 recite:
acquiring state information and position information of vehicles; acquiring driving intention information of the vehicles recognized by the state information of the vehicles; generating a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range, the state information of the vehicles and the position information of the vehicles; constructing a dispatching area occupancy grid map model; and co-dispatching global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model, and generating a global dispatching result of driving intentions of dispatching area vehicles, so as to guide driving decisions of the vehicles within the dispatching area range by means of the global dispatching result of driving intentions of dispatching area vehicles.
The examiner submits that the foregoing bolded limitation constitutes a “mental process” because under its broadest reasonable interpretation, the claim covers a mental process . For example, “generating a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range”, “constructing a dispatching area occupancy grid map model; and co-dispatching global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model”, and “generating a global dispatching result of driving intentions of dispatching area vehicles, so as to guide driving decisions of the vehicles within the dispatching area range by means of the global dispatching result of driving intentions of dispatching area vehicles”. In the context of this claim these limitations merely show perception and relaying of information as can be done by human mind and generating paths of other vehicles, maps after data has been collected, and relaying said generated information, which is something a human mind is capable of with the aid of pen and paper. For example, Essentially, this process is collecting data about an area a vehicle is driving within and subsequently generating maps and relaying generated information to nearby vehicles. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (Where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
acquiring state information and position information of vehicles; acquiring driving intention information of the vehicles recognized by the state information of the vehicles; generating a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range, the state information of the vehicles and the position information of the vehicles; constructing a dispatching area occupancy grid map model; and co-dispatching global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model, and generating a global dispatching result of driving intentions of dispatching area vehicles, so as to guide driving decisions of the vehicles within the dispatching area range by means of the global dispatching result of driving intentions of dispatching area vehicles.
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “acquiring state information and position information of vehicles; acquiring driving intention information of the vehicles recognized by the state information of the vehicles”, “the state information of the vehicles and the position information of the vehicles” These limitations merely are insignificant extra-solution activities that merely use a generic computer and or generic computer components (“computer” / “computing device”) to execute the method’s steps. In particular, the step acquiring state and position information it is recited at a high level of generality (i.e., as a general means of gathering vehicle data for use in the mental process step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The method is recited at a high level of generality and merely automates the navigating step.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “acquiring” to perform the map generation and navigating amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitation of “acquiring” the examiner submits that this limitation is an insignificant extra-solution activity.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “acquiring” are well-understood, routine, and conventional activities because the background recites that the components are all conventional computers and the data transferring over a network is done through well understood and routine communication pathways. 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, as well as, transmitting data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner.
Dependent claims 2-4 and 6-10 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of 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. Claim 2 mentions “…recognizing the driving intentions …” which would fail under Step 2A prong 1 for being an abstract idea – mental process and would not allow claim 2 to be considered eligible subject matter. Claim 3 mentions “…generating a global driving intention graph…” which would fail under Step 2A prong 1 for being an abstract idea – mental process and would not allow claim 3 to be considered eligible subject matter. Claim 4 mentions “…generating the global dispatching result…” which would fail under Step 2A prong 1 for being an abstract idea – mental process and would not allow claim 4 to be considered eligible subject matter. Claim 6 mentions “…acquire state information and position information of the vehicle…” which would fail under Step 2A prong 2 for being an insignificant extra solution activity – data gathering and does not allow claim 6 to be considered eligible subject matter. Claims 7 mentions “…acquire the state information of the vehicle…” which would fail under Step 2A prong 2 for being an insignificant extra solution activity – data gathering and does not allow claim 7 to be considered eligible subject matter. Claim 8 mentions“…receive a global dispatching result of driving intentions …” which would fail under Step 2A prong 2 for being an insignificant extra solution activity – data gathering and does not allow claim 8 to be considered eligible subject matter. Claim 9 and 10 mention “…co-dispatching driving intentions of intelligent vehicles …” which would fail under Step 2A prong 1 for being an abstract idea – mental process and would not allow claim 9 or 10 to be considered eligible subject matter.
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.
Claims 1,5, 9, and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huasheng et al (CN108919803A).
Regarding Claim 1 Huasheng teaches A method for regionally co-dispatching driving intentions of intelligent vehicles, (Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles, the second control command including collision avoidance” & See Also Pg. 1 – [1] – “Cooperative control method and device for mine unmanned vehicle” (equates to A method for regionally co-dispatching driving intentions of intelligent vehicles as the quote shows multiple vehicles in communication with one another wherein they can inform one another of a driving path being taken.)) comprising: acquiring state information and position information of vehicles; (Pg. 13 – [142] – “the central server can receive vehicle position information transmitted by a plurality of mining unmanned vehicles to determine its position in the grid map” & See Also Pg. 14 – [143] – “the center server can receive the vehicle speed information in the vehicle state transmitted by the plurality of mining unmanned vehicles to obtain the traveling speeds of the plurality of mining unmanned vehicles.” (equates to acquiring state information and position information of vehicles as the first quote shows the plurality of vehicles providing position information and the second quote showing the speed information being attained from the same plurality of intelligent vehicles. )) acquiring driving intention information of the vehicles recognized by the state information of the vehicles; (Pg. 14 – [143] – “The central server can determine the position of the unmanned mine car in the grid map and the respective driving speed according to the vehicle position information of the plurality of unmanned mining vehicles, if at least two mining unmanned vehicles A and B are predicted to pass at the same time At the same grid, it is determined that there is a path conflict between at least two of the mine unmanned vehicles.” (equates to acquiring driving intention information of the vehicles recognized by the state information of the vehicles as the speed and position are used to see if the paths the vehicles will take conflict and thus the intention is establish through the path being taken then compared.)) generating a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range, (Pg. 6 – [56] – “travel areas, or may be a preset range of the map. The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map. The central server can determine the grid position of the two mining unmanned vehicles in the grid map based on the vehicle position information of the two mining unmanned vehicles, thereby determining the grid between the two mining unmanned vehicles. The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles.” (equates to generating a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range as a grid in this art is formed showing each position of each vehicle relative to one another where their paths based on intention are utilized in the grid to determine if they’ll collide.)) the state information of the vehicles and the position information of the vehicles; (Pg. 6 – [56] – “The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map. The central server can determine the grid position of the two mining unmanned vehicles in the grid map based on the vehicle position information of the two mining unmanned vehicles, thereby determining the grid between the two mining unmanned vehicles. The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles. The central server can predict whether two unmanned vehicles will pass the same grid at the same time according to the grid distance between the two unmanned mine cars and their respective travel speeds,” (Equates to the state information of the vehicles and the position information of the vehicles as the grid map stores position information and speed information to determine collision instances.)) constructing a dispatching area occupancy grid map model; (Pg. 6 – [56] – “The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map…The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles. The central server can predict whether two unmanned vehicles will pass the same grid at the same time according to the grid distance between the two unmanned mine cars and their respective travel speeds” (equates to constructing a dispatching area occupancy grid map model as a grid is formed between the plurality of vehicles where driving paths and thus intentions are recorded by the centrals server.)) and co-dispatching global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model, (Pg. 8 – [88] – “…the data sending process between the central server and the plurality of mining unmanned vehicles may be: setting a communication IP; binding the communication port; and calling a sending function to send the data.” & See Also Pg. 9 – [89] [90] [91] – “The mission planning may be a mission plan that the central server separately formulates for multiple mining unmanned vehicles according to transportation tasks.
The central server sends the path plan to each of the multiple mine unmanned vehicles. Path planning refers to the insertion of a sequence of intermediate points for control between a given path starting point and a target point based on a certain environmental model, given the starting point and the target point of the driverless car. Collision, a valid path that can safely reach the target point.
The central server may plan a first path for a plurality of mining unmanned vehicles according to the improved artificial potential field method, avoiding static obstacles in the environment, and proceeding toward the target.” ( equates to co-dispatching global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model as the quote show a communication established between the server and the intelligent vehicles wherein the server ensures that the paths taken by each within the grid allow for safe transit of each.)) and generating a global dispatching result of driving intentions of dispatching area vehicles, (Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles,” (equates to generating a global dispatching result of driving intentions of dispatching area vehicles as the vehicles in this mentioned driving area have communicated their path intention and the control commands are dispatched according to the driving intentions of the vehicles.)) so as to guide driving decisions of the vehicles within the dispatching area range by means of the global dispatching result of driving intentions of dispatching area vehicles. (Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles, the second control command including collision avoidance The second control instruction is used to instruct at least one mining unmanned vehicle to automatically travel according to a conflict avoidance strategy.” (equates to so as to guide driving decisions of the vehicles within the dispatching area range by means of the global dispatching result of driving intentions of dispatching area vehicles as the quote shows driving intention of each vehicles being reported and then based on the paths each vehicles takes the driving decisions are then altered to avoid collision. ))
Regarding Claim 5 Huasheng teaches An apparatus for regionally co-dispatching driving intentions of intelligent vehicles, comprising: an information acquisition module, (Pg. 4 – [38] – “Receiving vehicle information transmitted by the plurality of mining unmanned vehicles” & See Also Pg. 15 – [165] - “the device and the unit described above can refer to the corresponding process in the foregoing method embodiment” (equates to an information acquisition module as the first quote shows the ability to receive vehicle information wherein the second quote shows a device and unit that can perform any disclosed method steps therein. )) configured to acquire state information and position information of vehicles, (Pg. 4 – [40] – “…vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed…” & See Also Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle…”) and acquire driving intentions of the vehicles recognized by the state information and position information of the vehicles; (Pg. 15 – [158] – “vehicle information to the central server, where the vehicle information is used by the central server to determine that there is a path conflict between at least two mining unmanned vehicles of the plurality of mining unmanned vehicle” (equates to acquire driving intentions of the vehicles recognized by the state information and position information of the vehicles as the quote shows the vehicles information being used to determine a path conflict and thus a path is only known based on driving intention which is determined from aforementioned vehicle information.)) a global driving intention graph generation module, configured to generate a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range, (Pg. 6 – [56] – “travel areas, or may be a preset range of the map. The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map. The central server can determine the grid position of the two mining unmanned vehicles in the grid map based on the vehicle position information of the two mining unmanned vehicles, thereby determining the grid between the two mining unmanned vehicles. The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles.” (equates to a global driving intention graph generation module, configured to generate a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range as the quote shows a server acting as the global driving intention graph generation module wherein the server can determine path conflicts and thus intention graph of each vehicles in the disclosed area.)) the state information of the vehicles and the position information of the vehicles; (Pg. 13 – [138] – “The center server determines that the conflict between the plurality of mining unmanned vehicles that have collided has been eliminated based on the vehicle position information in the vehicle information transmitted by the mine unmanned vehicle to the center server and the mine unmanned vehicle speed information”) a map model construction module, configured to construct a dispatching area occupancy grid map model; (Pg. 6 – [56] – “The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map…The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles. The central server can predict whether two unmanned vehicles will pass the same grid at the same time according to the grid distance between the two unmanned mine cars and their respective travel speeds” (equates to a map model construction module, configured to construct a dispatching area occupancy grid map model as the quote shows the server having the ability to generate a map and in particular a grid map that is used for determining path conflicts between the vehicles. )) and a global dispatching result generation module, configured to co-dispatch global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model, (Pg. 1 – [11] – “the cooperative control method for the mine unmanned vehicle further includes: determining, according to the vehicle information, that there is a path conflict between at least two of the plurality of mining unmanned vehicles, Including: according to the driving direction and position of multiple mining unmanned vehicles,” & See Also Pg. 15 – [165] – “the device and the unit described above can refer to the corresponding process in the foregoing method embodiment,” (equates to a global dispatching result generation module, configured to co-dispatch global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model as the first quote show paths being generated and taken in by the method wherein a path conflict between the various vehicles can be determined and the second quote shows how the method steps disclosed in the art can be performed by a unit and device.)) and generate a global dispatching result of driving intentions of dispatching area vehicles, so as to guide driving decisions of the vehicles within the dispatching area range by means of the global dispatching result of driving intentions of dispatching area vehicles. (Pg. 15 – [157] – “receiving module 610 is configured to receive a first control instruction sent by the central server, where the first control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan” Pg. 9 – [89]– “The mission planning may be a mission plan that the central server separately formulates for multiple mining unmanned vehicles according to transportation tasks. The central server sends the path plan to each of the multiple mine unmanned vehicles” (equates to generate a global dispatching result of driving intentions of dispatching area vehicles, so as to guide driving decisions of the vehicles within the dispatching area range by means of the global dispatching result of driving intentions of dispatching area vehicles as the first quote show how control can be implemented based on received driving intentions from other vehicles where the second quote further shows the module taking into account a plurality of vehicles and their individual intention.))
Regarding Claim 9 Huasheng teaches A storage medium storing a program which, (Pg. 15 – [163] – “storage medium” & See Also Pg. 16 – [169] – “can store a program”)when executed by a processor, (Pg. 15 – [163] – “executed by a processor of the apparatus 700”) implements the method for regionally co-dispatching driving intentions of intelligent vehicles according to Claim 1. (Pg. 15 – [163] – “when the instructions in the storage medium are executed by a processor of the apparatus 700, enabling the apparatus 700 to perform a cooperative control method for a mine unmanned vehicle”)
Regarding Claim 10 Huasheng teaches A computing device, (Pg. 4 – [35] – “…can be performed by a computing device…”) comprising a processor and a memory for storing a program executable by the processor, (Pg. 15 – [161] – “processing component 710 that further includes one or more processors, and memory resources” & See Also Pg. 15 – [163] – “executed by a processor of the apparatus 700” & See Also Pg. 16 – [169] – “…can store a program…”) wherein the processor, when executing the program stored in the memory, implements the method for regionally co-dispatching driving intentions of intelligent vehicles according to Claim 1 . (Pg. 15 – [163] – “when the instructions in the storage medium are executed by a processor of the apparatus 700, enabling the apparatus 700 to perform a cooperative control method for a mine unmanned vehicle”)
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Huasheng in view of Xue-Wu (“Intention Recognition and Trajectory Prediction for Vehicles Using LSTM Network MT”) and Xiaofeng (CN110843789A).
Regarding Claim 2 Huasheng teaches The method for regionally co-dispatching driving intentions of intelligent vehicles according to claim 1, wherein the process of recognizing the driving intentions of the vehicles through the state information and position information of the vehicles; (Pg. 6 – [56] – “The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map. The central server can determine the grid position of the two mining unmanned vehicles in the grid map based on the vehicle position information of the two mining unmanned vehicles, thereby determining the grid between the two mining unmanned vehicles. The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles. The central server can predict whether two unmanned vehicles will pass the same grid at the same time according to the grid distance between the two unmanned mine cars and their respective travel speeds,” (Equates to wherein the process of recognizing the driving intentions of the vehicles through the state information and position information of the vehicles as the grid map stores position information and speed information to determine potential collision instances.))
Yet Huasheng fails to teach comprises: constructing a driving intention recognition model based on a convolutional neural network, wherein vehicle state information is taken as an input quantity I of the driving intention recognition model, and a recognition vector w=(wl, w2,w3,w4,w5) for the driving intentions is output by a Softmax layer of the driving intention recognition model, where wl, w2, w3, w4, and w5 are probabilities of driving intention categories: traveling to a left lane, keeping unchanged, traveling to a right lane, speeding up, and slowing down, respectively; and setting confidence thresholds for various driving intention categories, and when an output probability of a certain driving intention category is greater than the corresponding confidence threshold, determining that a vehicle has a driving intention C corresponding to the category, where C
∈
{Ca: traveling to a left lane, Cb: keeping unchanged, Cc: traveling to a right lane, Cd: speeding up, Cf slowing down}.
Xue-Wu teaches wherein vehicle state information is taken as an input quantity I of the driving intention recognition model, (Pg. 16 – “it can be seen that only on the basis of the input trajectory position information, adding vehicle speed information, surrounding vehicle information and driving intention information” (equates to wherein vehicle state information is taken as an input quantity I of the driving intention recognition model as the quote shows both surrounding vehicle position and speed data being used as an input. )) and a recognition vector w=(wl, w2,w3 w4,w5) for the driving intentions is output by a Softmax layer of the driving intention recognition model, (Pg. 4 – 1 Model Framework – “The Softmax function is used to calculate the probability of the driving intention to change lanes to the left, drive straight, and change lanes to the right” & See Also Pg. 4 – 1 Model Framework – “Set as the input of the model; C=(C1,C2,C3), is the intention category vector output by the intention recognition module, C1, C2, C3 represent the three intention categories of changing lanes to the left, driving straight, and changing lanes to the right;” & See Also Pg. 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.” (equates to and a recognition vector w=(wl, w2,w3 w4,w5) for the driving intentions is output by a Softmax layer of the driving intention recognition model as the quote shows the various changing lanes to the left, driving straight, and changing lanes to the right that re computed by the Softmax layer. Wherein the speeding up and slowing down are shown to be input to the predicted vehicle behavior as seen from the last quote to enhance the model they are building gup to in the paper.)) where wl, w2, w3, w4, and w5 are probabilities of driving intention categories. (Pg. 4 – 1 Model Framework – “is a vector composed of the probabilities of each intention category,; (i=1,2,3) represent the probabilities of changing lanes to the left, driving straight, and changing lanes to the right, respectively” & See Also Pg. 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.” (equates to where wl, w2, w3, w4, and w5 are probabilities of driving intention categories as the first quote shows the lane changing an then later in the paper the insertion of speeding up and down is included in the model to further the predictive capabilities.)) traveling to a left lane, keeping unchanged, traveling to a right lane, (Pg. 4 – 1 model framework – “change lanes to the left, drive straight, and change lanes to the right”) speeding up, and slowing down, respectively (Pg, 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.”) and setting confidence thresholds for various driving intention categories, (Pg. 7 – “the confidence threshold for changing lanes to the left and right is set to 80%, and the confidence threshold for driving in a straight line is set to 70%.”) and when an output probability of a certain driving intention category is greater than the corresponding confidence threshold (Pg. 7 – “When the assumption of a certain type of intention is greater than the corresponding confidence threshold”) determining that a vehicle has a driving intention C corresponding to the category, where C
∈
{Ca: traveling to a left lane, Cb: keeping unchanged, Cc: traveling to a right lane,} (Pg. 7 - “it is determined that t