Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claims 1 and 20 are directed to a monitoring apparatus (i.e., a machine). Claim 22 is directed to a monitoring method (i.e., a process). Therefore, claims 1-18, 20 and 22 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, 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. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A monitoring apparatus comprising:
at least one memory storing instructions, and
at least one processor configured to execute the instructions stored in the at least one memory to;
acquire, by a camera, an image of a road captured at a first point;
determine, by analyzing the image, traffic information indicating a traffic condition of the road at the first point [mental process/step];
acquire road information indicating whether or not there is a traffic-limiting situation at a second point leading to the first point; and
detect an abnormality in the camera image based on the traffic information, statistical information of the traffic information, and the road information [mental process/step].
The examiner submits that the foregoing bolded limitation constitutes a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, detecting in the context of this claim encompasses a person (driver) looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
Independent claims 20 and 22 contain similar limitations. Therefore, claims 20 and 22 recite similar abstract ideas to those recited in claim 1.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). 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” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A monitoring apparatus comprising:
at least one memory storing instructions [applying the abstract idea using generic computing module], and
at least one processor configured to execute the instructions stored in the at least one memory [applying the abstract idea using generic computing module] to;
acquire, by a camera, an image of a road captured at a first point [pre-solution activity (data gathering) using generic sensors];
determine, by analyzing the image, traffic information indicating a traffic condition of the road at the first point [mental process/step];
acquire road information indicating whether or not there is a traffic-limiting situation at a second point leading to the first point [pre-solution activity (data gathering) using generic sensors]; and
detect an abnormality in the camera image based on the traffic information, statistical information of the traffic information, and the road information [mental process/step].
For the following reason(s), 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 “at least one memory storing instructions…,” “at least one processor…,” “acquire, by a camera,…” and “acquire road information…,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (vehicle controller) to perform the process. In particular, the acquiring steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amount to mere data gathering, which is a form of insignificant extra-solution activity.
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 limitation(s) 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. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, 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 using a vehicle controller to perform the evaluating… amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “acquire, by a camera, an image…,” “at least one processor…,” and “displaying…,” the examiner submits that these limitations are insignificant extra-solution activities. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Dependent claims 2-18 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 since they mainly teach a subsequent step of executing instructions to gather more data from the same sensors. Therefore, dependent claims 2-18 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1-18, 20 and 22 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-18, 20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Ahire, et al., US 2021/0300423 A1, in view of Georgiou, et al., US 2021/0233390 A1.
As per Claim 1, Ahire teaches a monitoring apparatus (¶ 44) comprising:
at least one memory storing instructions (¶ 58), and at least one processor configured to execute the instructions stored in the at least one memory (¶ 58) to;
acquire, by a camera, an image of a road captured at a first point (¶ 59);
determine, by analyzing the image, traffic information indicating a traffic condition of the road at the first point (¶¶ 64-65); and
acquire road information indicating whether or not there is a traffic-limiting situation at a second point leading to the first point (¶ 64; “a roadway obstruction (or potential roadway obstruction)”).
Ahire does not expressly teach detecting an abnormality in the image based on the traffic information and the road information. Georgiou teaches detecting an abnormality in the image based on the traffic information and the road information (¶¶ 160-161; after “determining a discrepancy between the traffic light data of each of the plurality of autonomous vehicles (102) and the known traffic light data” of Figure 1, per method 400B of Figure 4B). At the time of the invention, a person of skill in the art would have thought it obvious to compare the camera and sensor data collected as per Ahire with stored data records, as Georgiou teaches, in order to determine appropriate vehicle responses to avoid obstructions and pedestrians along a roadway.
As per Claim 2, Ahire teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to cause, according to the road information, the detection of the abnormality in the camera image (¶¶ 64-65) using the traffic information and statistical information of the traffic information (¶ 63; as per “information, such as one or more of the roadway manager identification information, a date, a time, a compensation and a location of the roadway traffic”).
As per Claim 3, Ahire teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to cause, based on the road information indicating there is no traffic-limiting situation at the second point, the detection of the abnormality in the image (¶¶ 65-66; “responsive to the second threat level determination being identified as below the threshold, the transport may proceed along the roadway”).
As per Claim 4, Ahire teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to detect the abnormality in the image based on a discrepancy between the traffic information and the statistical information (¶¶ 69, 76, 79-80).
As per Claim 5, Ahire teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to detect the abnormality in the image based on a result of comparing the discrepancy with a threshold (¶¶ 58, 65-66, 83, 91-94).
As per Claim 6, Ahire teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to set the threshold according to a traffic obstruction level at the second point indicated by the road information (¶¶ 99-100).
As per Claim 7, Ahire does not expressly teach that the at least one processor is further configured to execute the instructions stored in the at least one memory to detect no abnormality in the image, based on the road information indicating that there is a traffic-limiting situation at the second point. Georgiou teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to detect no abnormality in the image (¶ 147; after looking for “[t]raffic objects” in an image), based on the road information indicating that there is a traffic-limiting situation at the second point (¶¶ 107-108; after “comparing image data to static models (e.g., static lane marker proportion data) or to dynamic models (e.g., models developed or updated by machine learning techniques)”). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 8, Ahire does not expressly teach that the at least one processor is further configured to execute the instructions stored in the at least one memory to determine an abnormality level of the image according to a degree of discrepancy between the traffic information and statistical information of the traffic information. Georgiou teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to determine an abnormality level of the image (¶ 46) according to a degree of discrepancy between the traffic information and statistical information of the traffic information (¶ 166; after “comparing of the traffic light data of each of the plurality of autonomous vehicles with the known traffic light data 404” as in Figure 4B). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 9, Ahire does not expressly teach that the at least one processor is further configured to execute the instructions stored in the at least one memory to determine the abnormality detection means determines the abnormality level of the camera image based on a degree of discrepancy estimated from a traffic obstruction level at the second point indicated by the road information, and based on the degree of discrepancy between the traffic information and the statistical information. Georgiou teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to determine the abnormality detection means determines the abnormality level of the camera image based on a degree of discrepancy estimated from a traffic obstruction level at the second point indicated by the road information (¶ 147; after looking for “[t]raffic objects” in an image), and based on the degree of discrepancy between the traffic information and the statistical information (¶ 88; after “comparing image data to pre-existing image data (e.g., of a building, of the road surface, etc.) known to correspond to a particular location”). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 10, Ahire does not expressly teach that the at least one processor is further configured to execute the instructions stored in the at least one memory to detect the abnormality detection means detects the abnormality in the camera image resulting from a camera malfunction. Georgiou teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to detect the abnormality detection means detects the abnormality in the camera image resulting from a camera malfunction (¶ 109; in case of “sensor error”). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 11, Ahire teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to acquire the road information from an image captured by a camera disposed at the second point (¶ 74; as “cameras” are positioned “in proximity to the vehicle(s)”).
As per Claim 12, Ahire teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to acquire the road information at the second point from a management apparatus that manages the road information (¶ 74; as other “sensors” are positioned “in proximity to the vehicle(s)”).
As per Claim 13, Ahire the at least one processor is further configured to execute the instructions stored in the at least one memory to acquire a plurality of pieces of the road information at a plurality of second points, and detect the abnormality in the image based on the plurality of pieces of the road information (¶¶ 93-94; as per an “obstruction” or “too many vehicles that exceed the degree of traffic (or the traffic threshold)”).
As per Claim 14, Ahire does not expressly teach that the road information includes information indicating that the road is closed or information indicating that the road is restricted. Georgiou teaches that the road information includes information indicating that the road is closed or information indicating that the road is restricted (¶ 147; e.g., by “constructions barriers, construction objects, or any other traffic object suitable for the intended purpose”). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 15, Ahire teaches that the traffic information includes a traffic volume, a speed, or a type of an object passing on the road (¶ 105; after “identifying one or more traffic conditions based on the plurality of transports 484, such as traffic congestion, accidents, preferred routes, new routes, rush hour, etc.”).
As per Claim 16, Ahire teaches that the object includes a vehicle or a person (¶¶ 64-70, 100).
As per Claim 17, Ahire teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to store the statistical information of the traffic information, and detect the abnormality in the camera image based on the stored statistical information (¶¶ 58-59; as data management server 128 of Figure 1D compiles data collected from “a camera, audio or other sensors”).
As per Claim 18, Ahire teaches that the at least one processor is further configured to execute the instructions stored in the at least one memory to generate the statistical information based on a result of aggregating the traffic information in a predetermined period (¶ 59; “assigned as the traffic manager for a period of time”).
As per Claim 20, Ahire teaches a monitoring system comprising a camera disposed at a first point and a monitoring apparatus (¶¶ 44, 59-60), wherein the monitoring apparatus includes:
at least one memory storing instructions (¶ 58), and at least one processor configured to execute the instructions stored in the at least one memory (¶ 58) to;
acquire, by the camera, an image of a road captured at the first point (¶ 59);
determine, by analyzing the image, traffic information indicating a traffic condition of the road at the first point (¶¶ 64-65); and
acquire road information indicating whether or not there is a traffic-limiting situation at a second point leading to the first point (¶ 64; “a roadway obstruction (or potential roadway obstruction)”).
Ahire does not expressly teach detecting an abnormality in the image based on the traffic information and the road information. Georgiou teaches detecting an abnormality in the image based on the traffic information and the road information (¶¶ 160-161; after “determining a discrepancy between the traffic light data of each of the plurality of autonomous vehicles (102) and the known traffic light data” of Figure 1, per method 400B of Figure 4B).
As per Claim 22, Ahire teaches a monitoring method (¶¶ 58-59) comprising:
acquiring, by a camera, an image of a road captured at a first point (¶ 59);
determining, by analyzing the image, traffic information indicating a traffic condition of the road at the first point (¶¶ 64-65); and
acquiring road information indicating whether or not there is a traffic-limiting situation at a second point leading to the first point (¶ 64; “a roadway obstruction (or potential roadway obstruction)”).
Ahire does not expressly teach detecting an abnormality in the image based on the traffic information and the road information. Georgiou, et al., US 2021/0233390 A1 teaches detecting an abnormality in the image based on the traffic information and the road information (¶¶ 160-161; after “determining a discrepancy between the traffic light data of each of the plurality of autonomous vehicles (102) and the known traffic light data” of Figure 1, per method 400B of Figure 4B). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ATUL TRIVEDI whose telephone number is (313)446-4908. The examiner can normally be reached Mon-Fri; 9:00 AM-5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
ATUL TRIVEDI
Primary Examiner
Art Unit 3661
/ATUL TRIVEDI/Primary Examiner, Art Unit 3661