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 10/30/2025. Claims 1-19 are pending.
Information Disclosure Statement
The information disclosure statement submitted on 10/30/2025 has 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: an environment determination unit that determines in claims 1, 5, and 6 . Additionally, a recognition processing unit that performs recognition processing in claims 1, 3 and 10. Again, the communication unit transmits the detection result in claims 1, 2, and 8. Finally, the position information being acquired by a position information acquisition unit 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,
[0044] – “a processor included in the automobile 20 may execute the function as the information processing device 100”
[0030] – “Next, a configuration of the information processing device 100 will be described with reference to Fig. 1. The information processing device 100 includes a data acquisition unit 101, an environment determination unit 102, a recognition processing unit 103”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 12 and 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because claim 19 states “A Program…” and when looking into the specification of what the program actually is the definition was provided to be “[0052] The program may be installed in the server 200 in advance, or may be distributed by downloading, a storage medium, or the like and installed by a user or the like..” The words may be and storage medium were bolded to show how only stating that the program may be a storage medium doesn’t strictly limit the program to be hardware and may also then include software per se which is not one of the 4 eligible subject matter that is patentable. See MPEP 2106.03.
Claims 1-19 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 11 & 18 are directed to a method, (i.e., a process). Therefore, claims 11 & 18 are within at least one of the four statutory categories.
Claim 1-10 & 13-17 is directed to An information processing device (i.e., a machine). Therefore, claim 1-10 is within at least one of the four statutory categories.
Claim 12 & 19 is directed to a Program (i.e., a Software per Se). Therefore, claims 12 & 19 are not within at least one of the four statutory 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 recite:
an environment determination unit that determines a state of an environment; a recognition processing unit that performs recognition processing on a basis of a detection result of a sensor; and a communication unit that performs communication processing with an external device, wherein in a case where the environment is in a first state, the recognition processing unit performs recognition processing on a basis of the detection result of the sensor, and the communication unit transmits the detection result of the sensor and the recognition result of the recognition processing unit to the external device.
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, “determines a state of an environment”, and “recognition processing” and “performs communication processing” . In the context of this claim these limitations merely show perception as can be done by human mind and in particular detecting any type of environmental condition and calling it a state, which is something a human mind is capable of. Essentially, this device is collecting data about a location being travelled over and subsequently updating a 3D mapping system. 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”):
an environment determination unit that determines a state of an environment; a recognition processing unit that performs recognition processing on a basis of a detection result of a sensor; and a communication unit that performs communication processing with an external device, wherein in a case where the environment is in a first state, the recognition processing unit performs recognition processing on a basis of the detection result of the sensor, and the communication unit transmits the detection result of the sensor and the recognition result of the recognition processing unit to the external device.
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 “transmits the detection result of the sensor and the recognition result of the recognition processing unit to the external device”, “on a basis of a detection result of a sensor”, “wherein in a case where the environment is in a first state”, These limitations merely are insignificant extra-solution activities that merely use a generic computer and or generic computer components (“processor”) to execute the method’s steps. In particular, the step of transmitting data to an external device it is recited at a high level of generality (i.e., as a general means of gathering and sending map data for use in the mental process step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Regarding the limitation of a “an environment determination unit”, “recognition processing unit”, “external device” , and “communication unit” . This limitation merely is applying the use of a generic computer and or generic computer components (“processor”) to execute the method’s steps, and amounts to implementing an abstract idea on a computer. The system is recited at a high level of generality and merely automates the map updating 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 “transmits” to perform the 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 limitations of “transmits”, and electronic the examiner submits that these limitations are insignificant extra-solution activities.
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 limitation of “electronic” is well-understood, routine, and conventional activities because the specification does not provide any indication that the electronic map is anything other than a conventional computer. The additional limitations of “acquiring” and “displaying” 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 2-10 and 13-17 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 “…transmits …” which would fail under Step 2A prong 2 for being an insignificant extra solution activity of mere data gathering and would not allow claim 2, to be considered eligible subject matter. Claim 3 mentions “…recognition transmitted from the external device …” which would fail under Step 2A prong 2 for being an insignificant extra solution activity within data gathering and would not allow claim 3 to be considered eligible subject matter. Claim 4 mentions “…recognition processing can be performed. …” which would fail under Step 2A prong 1 for being a mental process and would not allow claim 4 to be considered eligible subject matter. Claim 5 and 6 mentions “…determines the state of the environment …” which would fail under Step 2A prong 1 for being a mental process and would not allow claim 4 to be considered eligible subject matter. Claims 7 mentions “…sensor is a single photon avalanche diode …” which would fail under Step 2A prong 2 for being a generic computer component and does not improve the functioning of a computer and would not allow claim 7 to be considered eligible subject matter. Claim 8 mentions“…communication unit transmits, to the external device…” which would fail under Step 2A prong 2 for being insignificant extra solution activity within data gathering and would not allow claim 8 to be considered eligible subject matter. Claims 9 mentions “…LiDAR is provided …” which would fail under Step 2A prong 2 for being a generic computer component and does not improve the functioning of a computer and would not allow claim 9 to be considered eligible subject matter. Claims 10 and 13 mentions “…recognition processing unit …” which would fail under Step 2A prong 2 for being a generic computer component and does not improve the functioning of a computer and would not allow claim 10 to be considered eligible subject matter. Claim 14 and 15 mentions “…position information transmitted from the external device …” which would fail under Step 2A prong 2 for being insignificant extra solution activity within data gathering and would not allow claim 14 and 15 to be considered eligible subject matter. Claims 16 mentions “…recognition information creation unit …” which would fail under Step 2A prong 2 for being a generic computer component and does not improve the functioning of a computer and would not allow claim 9 to be considered eligible subject matter. Claim 17 mentions “…information being transmitted from the external device …” which would fail under Step 2A prong 2 for being an insignificant extra solution activity within data gathering and would not allow claim 17 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.
Claim(s) 1-9 and 11-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fujii (US 11,410,332 B2) .
Regarding Claim 1 Fujii teaches An information processing device comprising: an environment determination unit that determines a state of an environment; (Pg. 1 – [Abstract]- “The processor acquires at least one image representing the environment of the vehicle from the imaging device, acquires the brightness of the environment of the vehicle,” (equates to An information processing device comprising: an environment determination unit that determines a state of an environment; as the state of the environment can be the brightness detected.)) a recognition processing unit that performs recognition processing on a basis of a detection result of a sensor; (Pg. 1 – Abstract – “analyzes the image to calculate the position of the landmark with respect to the road on which the vehicle travels,” (equates to a recognition processing unit that performs recognition processing on a basis of a detection result of a sensor as the quote shows the recognition of a landmark position based on the detection using imaging means or a sensor.) ) and a communication unit that performs communication processing with an external device, (Pg. 24 – [col. 3 – lines 37-39] – “Each vehicle is configured to be capable of wireless communication with the server 3.” ) wherein in a case where the environment is in a first state, the recognition processing unit performs recognition processing on a basis of the detection result of the sensor, (Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to wherein in a case where the environment is in a first state, the recognition processing unit performs recognition processing on a basis of the detection result of the sensor as the quote shows the recognition of image processing being done at multiple time steps and thus a first state within the multiple time steps is had.)) and the communication unit transmits the detection result of the sensor and the recognition result of the recognition processing unit to the external device. (Pg. 28 – [Col. 13 – lines 13- 21] – “The recognition result data at each time stored in the memory 80 are collectively uploaded at predetermined 15 upload intervals. The upload interval is set to, for example, K (K is a natural number) times the execution cycle of the image recognition process. When an equation of K 2 is satisfied, the main processor 40 uploads the data in which the recognition result within a certain period of time stored 20 in the memory 80 is packaged as probe data” & See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data” (equates to and the communication unit transmits the detection result of the sensor and the recognition result of the recognition processing unit to the external device as the quote shows the uploading of the recognition result and sensor data via probe data.) )
Regarding Claim 2 Fujii teaches The information processing device according to claim 1, wherein in a case where the environment is in a second state, the communication unit transmits the detection result of the sensor to the external device. (Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20”& See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data” (equates to wherein in a case where the environment is in a second state, the communication unit transmits the detection result of the sensor to the external device. As the quote shows the time steps in which the detection is done for recognition to take place across a trajectory and thus during a second step the probe data that is sent at each time step at the specific interval can be transmitted.))
Regarding Claim 3 Fujii teaches The information processing device according to claim 1, wherein in a case where the environment is in a second state, the recognition processing unit performs the recognition processing on a basis of information for recognition transmitted from the external device. (Pg. 34 – [Col. 23 – lines 47-50] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates.” & See Also pg. 34 – [Col. 23 – lines 8-14] – “High-precision positioning data refers to data generated by a dedicated mobile mapping system vehicle equipped with a laser radar (LiDAR), optical camera, GNSS receiver, acceleration sensor, and the like, or positioning operations. Hereinafter, the coordinate data determined by precise measurement will be referred to as reference coordinates.” & See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to wherein in a case where the environment is in a second state, the recognition processing unit performs the recognition processing on a basis of information for recognition transmitted from the external device as the first quote shows the data taken in to be compared to a reference coordinate wherein the second quote shows a dedicated external device used for reference coordinate generation to take place. The last quote shows the time step sequence via which the data is taken in and used for landmark recognition wherein inherently there is a second state of the environment along the time sequence data collection. ) )
Regarding Claim 4 Fujii teaches The information processing device according to The information processing device according to claim 1 wherein the first state is a state in which the recognition processing can be performed with high accuracy on a basis of the detection result of the sensor, and the second state is a state in which accuracy of the recognition processing is reduced on a basis of the detection result of the sensor. (Pg. 31 – [Col. 17 – Lines 62-67] – “Next, step S102 is executed. Step S102 is a step in which the main processor 40 determines the detailed position of the own vehicle. The detailed position of the own vehicle is the global coordinates including latitude, longitude and altitude on the earth. The main processor 40 determines the detailed global coordinates of the own vehicle” & See Also Pg. 31 – [Col. 18 – lines 13-21 - ] – “When the situation in which the own vehicle is placed satisfies a predetermined low frequency condition, that is, 15 when the position of the own vehicle exists in a predetermined low frequency area determined in advance, the determination in step S103 is YES, and the process proceeds to step S104. The low frequency area is predetermined on the map as shown in FIG. 5. The low frequency area may be set 20 as a line along the road segment 62, or may be set as a plane having a predetermined area as illustrated in FIG. 5” & See Also pg. 32 – [col. 20 lines 9- 12] – “As described above, the mode of changing the upload frequency of the probe data based on the traveling area, the weather condition, the time zone, and the number of years of use of the image processor 20 has been disclosed” (equates to wherein the first state is a state in which the recognition processing can be performed with high accuracy on a basis of the detection result of the sensor, and the second state is a state in which accuracy of the recognition processing is reduced on a basis of the detection result of the sensor as the quote shows the detection of the environment being done with a detailed positioned of the own vehicle and the probe data being used to detect whether or not a low frequency mode of the recognition should be utilized based on weather or other factors.))
Regarding Claim 5 Fujii teaches The information processing device according to claim 1, wherein the environment determination unit determines the state of the environment on a basis of illuminance. (Pg. 25 – Col. 6 – lines 54- 55 – “The illuminance sensor acquires the brightness around the vehicle.” & See Also Pg. 1 – [Abstract]- “The processor acquires at least one image representing the environment of the vehicle from the imaging device, acquires the brightness of the environment of the vehicle,” )
Regarding Claim 6 Fujii teaches The information processing device according to claim 1, wherein the environment determination unit determines the state of the environment on a basis of information regarding weather. (Pg. 32 – Col. 19 – lines 36-38 – “Further, the normal frequency mode may be changed to the low frequency mode based on the weather conditions in the area where the vehicle travels. For example, in bad weather such as heavy rain, heavy snowfall, heavy fog, and sandstorm, it may tend to be difficult to recognize traffic 40 signs and road markings, and the reliability of determining the position of landmark 63 may be lower than in fine weather.” & See Also pg. 32 – [Col. 19 - lines 16-23] – “to the low frequency mode at night. At night, it tends to be more difficult for the camera 10 to recognize traffic signs and road markings than during the daytime, and the reliability of determining the position of the landmark 63 is lower than 20 during the daytime. Therefore, it may be preferable to reduce the frequency of uploading the location information of the landmark 63 to the server 3.” (equates to wherein the environment determination unit determines the state of the environment on a basis of information regarding weather as the quote shows the weather being used to change the type of data uploading that is being done and thus the low frequency mode that the weather activates is still determining the state of the vehicle. ) )
Regarding Claim 7 Fujii teaches The information processing device according to claim 1, wherein the sensor is a single photon avalanche diode (SPAD) LiDAR capable of detecting distance information, infrared reflected light, and infrared ambient light as the detection result. (Pg. 25 – [Col. 6 – lines 5-7] – “The LiDAR may be preferably SPAD LiDAR (Single Photon Avalanche Diode Light Detection And Ranging) from the viewpoint of resolution and the like” & See Also Pg. 25 – [Col. 6 – lines 10-14] – “The three dimensional ranging point group data generated by the LiDAR, the detection result of the millimeter wave radar, the detection result of the sonar, and the like correspond to the peripheral object data. The three-dimensional ranging point group data is also defined as a distance image” (equates to wherein the sensor is a single photon avalanche diode (SPAD) LiDAR capable of detecting distance information, infrared reflected light, and infrared ambient light as the detection result. As the quote shows the use of the SPAD type LiDar in the in the reference application and the SPAD type is well known in the art to capture infrared light.))
Regarding Claim 8 Fujii teaches The information processing device according to claim 1, wherein the communication unit transmits, to the external device, position information of a position detected by the sensor, the position information being acquired by a position information acquisition unit. (Pg. 33 – [Col. 21 – lines 47-53] – “communication. In the configuration in which the server 3 appoints the transmission assignment vehicle, under a precondition, each vehicle sequentially reports vehicle information such as position 50 information, the performance of the image processor 20, and information indicating GPS positioning accuracy to the server 3.” (equates to wherein the communication unit transmits, to the external device, position information of a position detected by the sensor, the position information being acquired by a position information acquisition unit. As the quote shows the position info being captured by vehicles and then sent to the server.) )
Regarding Claim 9 Fujii teaches The information processing device according to claim 1, wherein the LiDAR is provided in a moving body. (Pg. 25 – [Col. 5 – lines 64-65] – “The in-vehicle system 2 of the present embodiment 65 includes a camera 10 as a peripheral monitoring sensor, but the peripheral monitoring sensor constituting the map system… The peripheral monitoring sensor may be a millimeter wave radar or LiDAR” )
Regarding Claim 11 Fujii teaches An information processing method comprising: determining a state of an environment; (Pg. 1 – [Abstract]- “The processor acquires at least one image representing the environment of the vehicle from the imaging device, acquires the brightness of the environment of the vehicle,”) performing recognition processing on a basis of a detection result of a sensor; (Pg. 1 – Abstract – “analyzes the image to calculate the position of the landmark with respect to the road on which the vehicle travels,” (equates to performing recognition processing on a basis of a detection result of a sensor as the quote show recognition performed based on the detection using imaging means or a sensor )) and performing communication processing with an external device, (Pg. 24 – [col. 3 – lines 37-39] – “Each vehicle is configured to be capable of wireless communication with the server 3.” ) wherein in a case where the environment is in a first state, the recognition processing is performed on a basis of the detection result of the sensor, (Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to wherein in a case where the environment is in a first state, the recognition processing unit performs recognition processing on a basis of the detection result of the sensor as the quote shows the recognition of image processing being done at multiple time steps and thus a first state within the multiple time steps is had.)) and the detection result of the sensor and the recognition result of the recognition processing are transmitted to the external device. (Pg. 28 – [Col. 13 – lines 13- 21] – “The recognition result data at each time stored in the memory 80 are collectively uploaded at predetermined 15 upload intervals. The upload interval is set to, for example, K (K is a natural number) times the execution cycle of the image recognition process. When an equation of K 2 is satisfied, the main processor 40 uploads the data in which the recognition result within a certain period of time stored 20 in the memory 80 is packaged as probe data” & See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data” (equates to and the communication unit transmits the detection result of the sensor and the recognition result of the recognition processing unit to the external device as the quote shows the uploading of the recognition result and sensor data via probe data.) )
Regarding Claim 12 Fujii teaches A program for causing a computer to execute an information processing method, the method comprising: determining a state of an environment; (Pg. 1 – [Abstract]- “The processor acquires at least one image representing the environment of the vehicle from the imaging device, acquires the brightness of the environment of the vehicle,”) performing recognition processing on a basis of a detection result of a sensor; (Pg. 1 – Abstract – “analyzes the image to calculate the position of the landmark with respect to the road on which the vehicle travels,” (equates to performing recognition processing on a basis of a detection result of a sensor as the quote show recognition performed based on the detection using imaging means or a sensor )) and performing communication processing with an external device, (Pg. 24 – [col. 3 – lines 37-39] – “Each vehicle is configured to be capable of wireless communication with the server 3.” ) wherein in a case where the environment is in a first state, the recognition processing is performed on a basis of the detection result of the sensor, (Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to wherein in a case where the environment is in a first state, the recognition processing unit performs recognition processing on a basis of the detection result of the sensor as the quote shows the recognition of image processing being done at multiple time steps and thus a first state within the multiple time steps is had.)) and the detection result of the sensor and the recognition result of the recognition processing are transmitted to the external device. (Pg. 28 – [Col. 13 – lines 13- 21] – “The recognition result data at each time stored in the memory 80 are collectively uploaded at predetermined 15 upload intervals. The upload interval is set to, for example, K (K is a natural number) times the execution cycle of the image recognition process. When an equation of K 2 is satisfied, the main processor 40 uploads the data in which the recognition result within a certain period of time stored 20 in the memory 80 is packaged as probe data” & See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data” (equates to and the communication unit transmits the detection result of the sensor and the recognition result of the recognition processing unit to the external device as the quote shows the uploading of the recognition result and sensor data via probe data.) )
Regarding Claim 13 Fujii teaches An information processing device comprising: a 3D map creation unit that creates a 3D map on a basis of a detection result and a recognition result of a sensor transmitted from an external device in a case where an environment is in a first state; (Pg. 34 – [Col. 23 – lines 47-64 ] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates. Here, the reference coordinates exist at the landmark 63b, and, for example, the reference coordinates are defined as Xref. When the coordinates of the landmark 63b on the probe data measured in real time are defined as X, the coordinates X match the coordinates Xref. That is, it is displaced parallel by a value of "Xref-X". By this operation, the coordinates of the landmarks 63b as all the reference marks recorded in the plurality of probe data become Xref. On the other hand, as shown in FIG. 8, the coordinates of the landmarks 63a, 63c, 63d other than the landmark 63b are also displaced parallel by a value of "Xref-X". Although the coordinates are expressed as one dimension here for convenience, they are actually calculated in three dimensions of latitude, longitude, and altitude” & See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to a 3D map creation unit that creates a 3D map on a basis of a detection result and a recognition result of a sensor transmitted from an external device in a case where an environment is in a first state as the first quote shows the landmark data being generated and map matched across reference and generated map data wherein the comparison comprises the landmarks in 3D and thus the 3d map is being generated and updated, the second quote shows the first state of the environment as the time sequenced data collection done for recognition inherently has a first state before the time passes to the next.)) a recognition information creation unit that creates information for recognition from the 3D map in a case where the environment is in a second state (Pg. 34 – [Col. 23 – lines 47-64 ] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates. Here, the reference coordinates exist at the landmark 63b, and, for example, the reference coordinates are defined as Xref. When the coordinates of the landmark 63b on the probe data measured in real time are defined as X, the coordinates X match the coordinates Xref. That is, it is displaced parallel by a value of "Xref-X". By this operation, the coordinates of the landmarks 63b as all the reference marks recorded in the plurality of probe data become Xref. On the other hand, as shown in FIG. 8, the coordinates of the landmarks 63a, 63c, 63d other than the landmark 63b are also displaced parallel by a value of "Xref-X". Although the coordinates are expressed as one dimension here for convenience, they are actually calculated in three dimensions of latitude, longitude, and altitude”& See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” & See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data” (equates to a recognition information creation unit that creates information for recognition from the 3D map in a case where the environment is in a second state as the first quote shows the recognition result being compared to prior map data to generate and update a 3d map and the second quote shows the recognition being done in a time sequence manner and thus a second state within the time sequence exists to generate a recognition result.)); and a communication unit that transmits the information for recognition to the external device. (Pg. 24 – [col. 3 – lines 37-39] – “Each vehicle is configured to be capable of wireless communication with the server 3.” & See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data”)
Regarding Claim 14 Fujii teaches The information processing device according to claim 13, wherein the recognition information creation unit creates the information for recognition from the 3D map on a basis of 3D point cloud information that is the detection result of the sensor transmitted from the external device in a case where the environment is in a second state. (Pg. 26 – [Col. 8 – lines 28-30] – “The position information of the lane mark is expressed as a coordinate group (that is, a point cloud) of the points where the lane mark is formed” & See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” & See Also Pg. 36 – [Col. 27 – lines 20-25] – “In addition, features that are included in the integrated 20 data and are not registered in the map data are detected as features that may have been newly created. In addition, features that are registered in the map data and are not included in the integrated data are detected as features that may have been deleted.” (equates to wherein the recognition information creation unit creates the information for recognition from the 3D map on a basis of 3D point cloud information that is the detection result of the sensor transmitted from the external device in a case where the environment is in a second state as the first quote shows the point cloud data being used to identify lane marks, the second quote showing the time based recognition being done wherein a plurality of state of environment are captured and thus a second state is captured and finally the last quote showing the map created missing components detected by the imaging means provided wherein the point cloud data would be used to help update the map.) )
Regarding Claim 15 Fujii teaches The information processing device according to claim 13, wherein the recognition information creation unit creates the information for recognition from the 3D map on a basis of position information transmitted from the external device in a case where the environment is in a second state. (Pg. 34 – [Col. 23 – lines 47-50] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates.” & See Also pg. 34 – [Col. 23 – lines 8-14] – “High-precision positioning data refers to data generated by a dedicated mobile mapping system vehicle equipped with a laser radar (LiDAR), optical camera, GNSS receiver, acceleration sensor, and the like, or positioning operations. Hereinafter, the coordinate data determined by precise measurement will be referred to as reference coordinates.” & See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to wherein the recognition information creation unit creates the information for recognition from the 3D map on a basis of position information transmitted from the external device in a case where the environment is in a second state as the first quote shows the data taken in to be compared to a reference coordinate wherein the second quote shows a dedicated external device used for reference coordinate generation to take place. The last quote shows the time step sequence via which the data is taken in and used for landmark recognition wherein inherently there is a second state of the environment along the time sequence data collection. ) )
Regarding Claim 16 Fujii teaches The information processing device according to claim 15, wherein the recognition information creation unit cuts out the 3D map on a basis of the position information and collates the cut- out 3D map with the 3D point cloud information. (Pg. 24 – [Col. 4 – lines 34 - 37] – “map update procedure, the information obtained by the camera 10 and the sensor 30 mounted on the vehicle is uploaded to the server 3 as probe data, and the map information in the server 3 is sequentially updated” & See Also Pg. 30 – [Col. 15 – [lines 22- 29] - ] – “Quasi-static information is, for example, information that is required to be updated within one to several hours. Road construction information, traffic regulation information, traffic congestion 25 information, and wide area weather information correspond to the quasi-static information. Semi-dynamic information is, for example, information that is required to be updated every 10 minutes.” & See Also Pg. 26 – [col. 8 – lines 28-29] – “The position information of the lane mark is expressed as a coordinate group (that is, a point cloud) of the points where the lane mark is formed. As” (equates to wherein the recognition information creation unit cuts out the 3D map on a basis of the position information and collates the cut- out 3D map with the 3D point cloud information as the first and second quote show the map updating procedure based on the type of landmark item is needing to be updated, the reference coordinates exists and then get updated based on the newly detected information. ))
Regarding Claim 17 Fujii teaches The information processing device according to claim 13, wherein the 3D map creation unit creates the 3D map on a basis of the recognition result, Pg. 34 – [Col. 23 – lines 47-64 ] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates. Here, the reference coordinates exist at the landmark 63b, and, for example, the reference coordinates are defined as Xref. When the coordinates of the landmark 63b on the probe data measured in real time are defined as X, the coordinates X match the coordinates Xref. That is, it is displaced parallel by a value of "Xref-X". By this operation, the coordinates of the landmarks 63b as all the reference marks recorded in the plurality of probe data become Xref. On the other hand, as shown in FIG. 8, the coordinates of the landmarks 63a, 63c, 63d other than the landmark 63b are also displaced parallel by a value of "Xref-X". Although the coordinates are expressed as one dimension here for convenience, they are actually calculated in three dimensions of latitude, longitude, and altitude” & See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to wherein the 3D map creation unit creates the 3D map on a basis of the recognition result as the first quote shows the recognition result of the 3d ;landmark data being used to update mapping systems.)) 3D point cloud information, (Pg. 26 – [Col. 8 – lines 28-30] – “The position information of the lane mark is expressed as a coordinate group (that is, a point cloud) of the points where the lane mark is formed” & See Also Pg. 27 – [Col. 9 – lines 60-64] – “Further, the main processor 40 performs lateral localization using landmarks such as lane marks, road edges, and guardrails. Lateral localization refers to specifying of the driving lane and specifying of the detailed position of the own vehicle in the driving lan” ) and position information, (Pg. 31 – [Col. 17 – lines 32-35] – “The map system 1 uploads information about the map collected by the vehicle to the server 3 included in the map system 1, and the map information stored in the server 3 can be updated” ) the recognition result, the 3D point cloud information, and the position information being transmitted from the external device. (Pg. 40 Col. 36 – lines 40-44 – “When identifying the position of the own vehicle, the map system 1 identifies the rough position of the own vehicle by positioning with a satellite such as GPS, and determines the detailed position of the own vehicle based on the map information downloaded from the server 3” & See Also Pg. 41 – Col. 38 – lines 16-20 – “By adopting the above configuration, the map system 1 also improves the calculation frequency of the coordinates of the landmark 63 from the image, so that it is possible t verify with the coordinates of the landmark 63 held by the map information for a longer period of time so that the position of the own vehicle can be specified more accurately.” & See also Pg. 34 – [Col. 23 – lines 47-50 ] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates… Although the coordinates are expressed as one dimension here for convenience, they are actually calculated in three dimensions of latitude, longitude, and altitude.” (equates to the recognition result, the 3D point cloud information, and the position information being transmitted from the external device as the quote shows the map information being downloaded from the server to compare and verify the recognition result wherein the map data include 3d data and position data used in the recognition result of map building.) )
Regarding Claim 18 Fujii teaches An information processing method comprising: generating a 3D map on a basis of a detection result and a recognition result of a sensor in a case where an environment is in a first state, (Pg. 34 – [Col. 23 – lines 47-64 ] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates. Here, the reference coordinates exist at the landmark 63b, and, for example, the reference coordinates are defined as Xref. When the coordinates of the landmark 63b on the probe data measured in real time are defined as X, the coordinates X match the coordinates Xref. That is, it is displaced parallel by a value of "Xref-X". By this operation, the coordinates of the landmarks 63b as all the reference marks recorded in the plurality of probe data become Xref. On the other hand, as shown in FIG. 8, the coordinates of the landmarks 63a, 63c, 63d other than the landmark 63b are also displaced parallel by a value of "Xref-X". Although the coordinates are expressed as one dimension here for convenience, they are actually calculated in three dimensions of latitude, longitude, and altitude” & See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to a 3D map creation unit that creates a 3D map on a basis of a detection result and a recognition result of a sensor transmitted from an external device in a case where an environment is in a first state as the first quote shows the landmark data being generated and map matched across reference and generated map data wherein the comparison comprises the landmarks in 3D and thus the 3d map is being generated and updated, the second quote shows the first state of the environment as the time sequenced data collection done for recognition inherently has a first state before the time passes to the next.)) the detection result and the recognition result being transmitted from an information processing device; (Pg. 28 – [Col. 13 – lines 13- 21] – “The recognition result data at each time stored in the memory 80 are collectively uploaded at predetermined 15 upload intervals. The upload interval is set to, for example, K (K is a natural number) times the execution cycle of the image recognition process. When an equation of K 2 is satisfied, the main processor 40 uploads the data in which the recognition result within a certain period of time stored 20 in the memory 80 is packaged as probe data” & See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data” ) creating information for recognition from the 3D map in a case where the environment is in a second state; (Pg. 34 – [Col. 23 – lines 47-64 ] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates. Here, the reference coordinates exist at the landmark 63b, and, for example, the reference coordinates are defined as Xref. When the coordinates of the landmark 63b on the probe data measured in real time are defined as X, the coordinates X match the coordinates Xref. That is, it is displaced parallel by a value of "Xref-X". By this operation, the coordinates of the landmarks 63b as all the reference marks recorded in the plurality of probe data become Xref. On the other hand, as shown in FIG. 8, the coordinates of the landmarks 63a, 63c, 63d other than the landmark 63b are also displaced parallel by a value of "Xref-X". Although the coordinates are expressed as one dimension here for convenience, they are actually calculated in three dimensions of latitude, longitude, and altitude”& See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” & See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data” (equates to a recognition information creation unit that creates information for recognition from the 3D map in a case where the environment is in a second state as the first quote shows the recognition result being compared to prior map data to generate and update a 3d map and the second quote shows the recognition being done in a time sequence manner and thus a second state within the time sequence exists to generate a recognition result.)) and transmitting the information for recognition to the information processing device. (Pg. 34 – [Col. 23 – lines 47-50] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates.” & See Also pg. 34 – [Col. 23 – lines 8-14] – “High-precision positioning data refers to data generated by a dedicated mobile mapping system vehicle equipped with a laser radar (LiDAR), optical camera, GNSS receiver, acceleration sensor, and the like, or positioning operations. Hereinafter, the coordinate data determined by precise measurement will be referred to as reference coordinates.” & See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to transmitting the information for recognition to the information processing device as the first quote shows the data taken in to be compared to a reference coordinate wherein the second quote shows a dedicated external device used for reference coordinate generation to take place. The last quote shows the time step sequence via which the data is taken in and used for landmark recognition wherein inherently there is a second state of the environment along the time sequence data collection. ) )
Regarding Claim 19 Fujii teaches A program for causing a computer to execute an information processing method, the method comprising: generating a 3D map on a basis of a detection result and a recognition result of a sensor in a case where an environment is in a first state, (Pg. 34 – [Col. 23 – lines 47-64 ] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates. Here, the reference coordinates exist at the landmark 63b, and, for example, the reference coordinates are defined as Xref. When the coordinates of the landmark 63b on the probe data measured in real time are defined as X, the coordinates X match the coordinates Xref. That is, it is displaced parallel by a value of "Xref-X". By this operation, the coordinates of the landmarks 63b as all the reference marks recorded in the plurality of probe data become Xref. On the other hand, as shown in FIG. 8, the coordinates of the landmarks 63a, 63c, 63d other than the landmark 63b are also displaced parallel by a value of "Xref-X". Although the coordinates are expressed as one dimension here for convenience, they are actually calculated in three dimensions of latitude, longitude, and altitude” & See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to a 3D map creation unit that creates a 3D map on a basis of a detection result and a recognition result of a sensor transmitted from an external device in a case where an environment is in a first state as the first quote shows the landmark data being generated and map matched across reference and generated map data wherein the comparison comprises the landmarks in 3D and thus the 3d map is being generated and updated, the second quote shows the first state of the environment as the time sequenced data collection done for recognition inherently has a first state before the time passes to the next.)) the detection result and the recognition result being transmitted from an information processing device; (Pg. 28 – [Col. 13 – lines 13- 21] – “The recognition result data at each time stored in the memory 80 are collectively uploaded at predetermined 15 upload intervals. The upload interval is set to, for example, K (K is a natural number) times the execution cycle of the image recognition process. When an equation of K 2 is satisfied, the main processor 40 uploads the data in which the recognition result within a certain period of time stored 20 in the memory 80 is packaged as probe data” & See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data” ) creating information for recognition from the 3D map in a case where the environment is in a second state; (Pg. 34 – [Col. 23 – lines 47-64 ] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates. Here, the reference coordinates exist at the landmark 63b, and, for example, the reference coordinates are defined as Xref. When the coordinates of the landmark 63b on the probe data measured in real time are defined as X, the coordinates X match the coordinates Xref. That is, it is displaced parallel by a value of "Xref-X". By this operation, the coordinates of the landmarks 63b as all the reference marks recorded in the plurality of probe data become Xref. On the other hand, as shown in FIG. 8, the coordinates of the landmarks 63a, 63c, 63d other than the landmark 63b are also displaced parallel by a value of "Xref-X". Although the coordinates are expressed as one dimension here for convenience, they are actually calculated in three dimensions of latitude, longitude, and altitude”& See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” & See Also Pg. 28 – Col. 12 – lines 54-57 – “The main processor 40 transmits a data set including the travel trajectory information, the travelling route information, and the feature information stored in the memory 80 to the server 3 as probe data” (equates to a recognition information creation unit that creates information for recognition from the 3D map in a case where the environment is in a second state as the first quote shows the recognition result being compared to prior map data to generate and update a 3d map and the second quote shows the recognition being done in a time sequence manner and thus a second state within the time sequence exists to generate a recognition result.))and transmitting the information for recognition to the information processing device. (Pg. 34 – [Col. 23 – lines 47-50] – “Step S204 is a step in which the server processor 31 matches the coordinates of the landmark 63b as the reference mark measured in real time by the camera 10 and the sensor 30 with the reference coordinates.” & See Also pg. 34 – [Col. 23 – lines 8-14] – “High-precision positioning data refers to data generated by a dedicated mobile mapping system vehicle equipped with a laser radar (LiDAR), optical camera, GNSS receiver, acceleration sensor, and the like, or positioning operations. Hereinafter, the coordinate data determined by precise measurement will be referred to as reference coordinates.” & See Also Pg. 28 – [Col. 13 – lines 1-15 ] – “Specifically, the main processor 40 sequentially acquires feature information, travelling route information, and own vehicle position coordinates (hereinafter, recogmt10n results) obtained by image recognition or the like, and stores the information in the memory 80 in chronological order so 5 as to connect to the acquisition time (in other words, observation time). The recognition result of the feature information or the like is sequentially provided by, for example, the image processor 20 (for example, every 100 milliseconds). The feature information may be sequentially 10 generated by the main processor 40 in collaboration with the image processor 20” (equates to transmitting the information for recognition to the information processing device as the first quote shows the data taken in to be compared to a reference coordinate wherein the second quote shows a dedicated external device used for reference coordinate generation to take place. The last quote shows the time step sequence via which the data is taken in and used for landmark recognition wherein inherently there is a second state of the environment along the time sequence data collection. ) )
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(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fujii in view of Huan (CN116109825A).
Regarding Claim 10 Fujii teaches The information processing device according to claim 1,
Yet Fujii fails to teach wherein the recognition processing unit performs semantic segmentation.
Huan teaches wherein the recognition processing unit performs semantic segmentation (Pg. 10 – “the initial semantic segmentation model performs semantic recognition processing on the training sample image, and obtains a semantic segmentation result corresponding to the training sample image (ie, a first semantic segmentation result). Wherein, the initial shared module in the initial semantic segmentation model performs general feature extraction processing (as an example of the first feature processing) on the training sample image to obtain multi-scale general features”) It would have been an advantageous addition to the system disclosed by Fujii to include wherein the recognition processing unit performs semantic segmentation as this allows for individual pixels to be compared to one another and allow for a more precise means of object mapping.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein the recognition processing unit performs semantic segmentation as this allows for precise means of comparing a set of given data to freshly taken data using pixel to pixel matching.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
JP2022176816A - driving assistance system of the present invention for solving the above problems is a driving assistance system that recognizes the surrounding environment of a moving object using a recognition model supplied from a cloud server, wherein the moving object is a surrounding environment of the moving object. and a priority setting unit for setting the priority of recognition conditions indicating the degree of influence of the environmental data on the recognition accuracy of the surrounding environment, wherein the cloud server selects a recognition model corresponding to the surrounding environment of the moving object from among a plurality of recognition models for recognizing the surrounding environment of the moving object based on the priority of the environment data and the recognition conditions; and.
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/R.A.W./Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
9/1/26
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