DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This communication is a non-final action in response to the communications filed on 1/17/2025. Claims 1-10 are currently pending and have been considered below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “first obtaining module… to obtain”, “second obtaining module… to obtain”, “prediction module configured to predict” in claim 9.
Based on specification paragraph 0137, these modules are interpreted as “software, firmware, hardware, and suitable combinations thereof”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1-10 are determined to be directed to an abstract idea.
The claims 1-10 are directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea), without providing a practical application integration and without providing significantly more.
As per Step 1 of the subject matter eligibility analysis, Claims 1-10 are directed to a method (i.e., process), a system (i.e., apparatus), and non-transitory medium (i.e., product) which are statutory categories of invention.
As per Step 2A-Prong 1 of the subject matter eligibility analysis, Claims 1 and 9 are directed specifically to the abstract idea of passenger flow prediction method, comprising: obtaining a communication relationship between each preset node in a preset region and an adjacent node thereof, wherein the preset region comprises a plurality of preset nodes and connection channels between the preset nodes, and the communication relationship represents a flow direction of a corresponding connection channel; obtaining passenger density information corresponding to the plurality of preset nodes; and predicting passenger flow information in the preset region according to the passenger density information corresponding to the plurality of preset nodes, and the communication relationship between each preset node and the adjacent node thereof; which include mental processes (observing and evaluating data relating to passenger flow for making an opinion or judgment on predicting passenger flow), and certain methods of organizing human activity based on fundamental economic practice ([monitoring demand by] predicting passenger flow), and based on managing personal behavior and interactions between people (following rules and instruction for predicting passenger flow). Claims 2-8 and 10 are directed to the abstract idea of claim 1 with further details on the parameters/attributes of the abstract idea which includes mental processes and certain methods of organizing human activity for similar reasons as provided above for claim 1. After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
As per Step 2A-Prong 2 of the subject matter eligibility analysis, while the claims 1-10 recite additional limitations which are hardware or software elements, such as deep learning, obtaining modules, prediction module, image capture devices, cameras, these limitations are not enough to qualify as a practical application being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of an abstract idea in a particular technological environment, and mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Alternatively, receiving and/or transmitting data between devices is mere data gathering and insignificant extrasolution activity, which does not provide a practical application for the abstract idea (MPEP 2106.05(g)).
As per Step 2B of the subject matter eligibility analysis, while the claims 1-10 recite additional limitations which are hardware or software elements, such as deep learning, obtaining modules, prediction module, image capture devices, cameras, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of an abstract idea in a particular technological environment, and mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do provide significantly more to an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Alternatively, receiving and/or transmitting data between devices is mere data gathering and insignificant extrasolution activity, and also is well-understood, routine and conventional which do not provide a practical application for the abstract idea (MPEP 2106.05(g) & (d)).
Therefore, since there are no limitations in the claims 1-10 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-4, 9-10 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over YUAN et al (CN 111710008 A)
As per Claim 1, Yuan teaches a passenger flow prediction method (Abstract), comprising:
obtaining a communication relationship between each preset node in a preset region and an adjacent node thereof, wherein the preset region comprises a plurality of preset nodes and connection channels between the preset nodes, and the communication relationship represents a flow direction of a corresponding connection channel; obtaining passenger density information corresponding to the plurality of preset nodes; predicting passenger flow information in the preset region according to the passenger density information corresponding to the plurality of preset nodes, and the communication relationship between each preset node and the adjacent node thereof (“The method for generating human flow density provided by the invention, after obtaining the image, firstly performing normalization processing to the image to generate the normalized image, then obtaining the pixel value of each pixel point in the normalized image, and then subtracting the pixel value of each pixel point from the preset average value, then dividing the pixel value of each pixel point by the preset variance; then inputting the processed image to the human flow density estimation model to generate the thermal image corresponding to the image; then obtaining the pixel value of each pixel point in the thermal graph, and generating the pixel value corresponding to the thermal image according to the pixel value of each pixel point, and generating the human flow density according to the pixel value corresponding to the thermal map. Therefore, realizing the pre-processing of the obtained image, and the pre-processed image using the human flow density estimation model for human flow density estimation, improving the accuracy of the human flow density estimation result, effectively avoiding the congestion of dense crowd, occurrence of danger event such as trample.”; also see abstract).
As per Claim 3, Yuan teaches a method as provided in claim 1 above. Yuan further teaches wherein obtaining the passenger density information corresponding to the plurality of preset nodes comprises: obtaining multiple frames of images to be processed, wherein the images to be processed are images obtained by capturing images of passengers at each preset node with at least one image capture device provided at each preset node; and analyzing the multiple frames of images to be processed according to a deep learning algorithm to determine the passenger density information corresponding to at least one of the preset nodes (“The invention relates to the technical field of image processing, specifically relates to the technical field of deep learning and computer vision, especially relates to a method for generating human flow density, a device, an electronic device and a storage medium.”; “The method for generating human flow density provided by the invention, after obtaining the image, firstly performing normalization processing to the image to generate the normalized image, then obtaining the pixel value of each pixel point in the normalized image, and then subtracting the pixel value of each pixel point from the preset average value, then dividing the pixel value of each pixel point by the preset variance; then inputting the processed image to the human flow density estimation model to generate the thermal image corresponding to the image; then obtaining the pixel value of each pixel point in the thermal graph, and generating the pixel value corresponding to the thermal image according to the pixel value of each pixel point, and generating the human flow density according to the pixel value corresponding to the thermal map. Therefore, realizing the pre-processing of the obtained image, and the pre-processed image using the human flow density estimation model for human flow density estimation, improving the accuracy of the human flow density estimation result, effectively avoiding the congestion of dense crowd, occurrence of danger event such as trample.”; “Specifically, the training set can be obtained in advance, wherein the training set comprises a plurality of sample images, and each sample image respectively corresponding to the thermal map, and pre-set the structure and initial parameters of the flow density estimation model, then obtaining a sample image from the training set, supposing the obtained sample image is A1. the heat map corresponding to the training set A1 is B1, then inputting the A1 into the preset human flow density estimation model, obtaining the heat graph C1 corresponding to A1, and according to the difference between B1 and C1, determining the first correction coefficient, then using the first correction coefficient, and performing the first correction to the preset human flow density estimation model. then, obtaining another sample image from the training set; supposing the obtained sample image is A2, the heat map corresponding to the training set A2 is B2, then inputting A2 into the preset human flow density estimation model; obtaining the heat graph C2 corresponding to A2, and according to the difference between B2 and C2, determining the second correction coefficient, then using the second correction coefficient, performing the second correction to the first corrected human flow density estimation model. Through the similar process, the preset human flow density estimation model is modified for multiple times, namely obtaining the trained human flow density estimation model.”; also see abstract).
As per Claim 4, Yuan teaches a method as provided in claim 1 above. Yuan further teaches wherein before obtaining the passenger density information corresponding to the plurality of preset nodes, the method further comprises: determining whether surveillance cameras are present in the plurality of connection channels; and under the condition that surveillance cameras are present in the connection channels, collecting passenger flow information in the plurality of connection channels according to a passenger flow statistical algorithm, respectively, to obtain passenger flow statistical information corresponding to the plurality of connection channels (“wherein the image can be a static image directly shot, for example, photographing device according to a certain photographing frequency after photographing in real time to obtain the image. Alternatively, the image may also be a frame image captured from the dynamic image, for example, a frame image captured from the monitoring video captured by the monitoring device. Alternatively, the image may also be an image obtained by other means, and the present application is not limited thereto.”; “The method for generating human flow density provided by the invention, after obtaining the image, firstly performing normalization processing to the image to generate the normalized image, then obtaining the pixel value of each pixel point in the normalized image, and then subtracting the pixel value of each pixel point from the preset average value, then dividing the pixel value of each pixel point by the preset variance; then inputting the processed image to the human flow density estimation model to generate the thermal image corresponding to the image; then obtaining the pixel value of each pixel point in the thermal graph, and generating the pixel value corresponding to the thermal image according to the pixel value of each pixel point, and generating the human flow density according to the pixel value corresponding to the thermal map. Therefore, realizing the pre-processing of the obtained image, and the pre-processed image using the human flow density estimation model for human flow density estimation, improving the accuracy of the human flow density estimation result, effectively avoiding the congestion of dense crowd, occurrence of danger event such as trample.”; also see abstract).
As per claims 9-10, Claims 9-10 recite substantially similar limitations as claims 1- and 4, respectively; therefore, claims 9-10 are rejected with the same reasoning, rationale and motivation as recited above for claim 1 and 4, respectively.
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 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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al (CN 111710008 A) in view of Ferreira (US 20200284883 A1).
As per Claim 2, Yuan teaches a method as provided in claim 1 above. Yuan does not teach; however, Ferreira further teaches wherein after obtaining the communication relationship between each preset node in the preset region and the adjacent node thereof, and before obtaining the passenger density information corresponding to the plurality of preset nodes, the method further comprises: constructing an adjacency topological model according to the plurality of preset nodes and the communication relationship between at least one of the preset nodes and the adjacent node thereof, wherein the adjacency topological model is represented in an Nth order square matrix form, where N is an integer greater than 1 (para. 1416 “The first plurality of sensor pixels may include the same number of pixels as the second plurality (e.g. the pixels may be arranged in a square matrix). ”; para. 5390 “The information described by the traffic density probability map may enable a driver and/or an autonomously driving vehicle to adjust its route, for example to reduce the incidence of or to avoid a risky situation or a risky area (e.g., a location with an excessive vehicle density or an excessive accident rate).”; para. 5397 “The method 12700 may include, in 12706, receiving a traffic map associated with the location of the vehicle (or a plurality of traffic maps each associated with the location of the vehicle). Illustratively, the method 12700 may include determining (e.g., generating) a traffic map associated with (or based on) the location of the vehicle. By way of example, a vehicle-external device or system (e.g., the traffic map provider) may be configured to generate (or retrieve from a database) a traffic map based on the location of the vehicle. The vehicle-external device or system may be configured to transmit the traffic map to the vehicle. As another example, the vehicle (e.g., one or more processors of the vehicle) may be configured to determine (e.g., to generate) a traffic map associated with the location of the vehicle (for example by receiving data from other vehicles and/or traffic control devices). As another example, the traffic map may be stored in a data storage system (e.g., in a memory) of the vehicle. The vehicle may be configured to retrieve the traffic map from the data storage system, e.g. it may be configured to retrieve the traffic map associated with its location. The traffic map may be GPS-coded, e.g. the traffic map (e.g., the traffic map data) may be associated with GPS-coordinates.”).
It would be obvious to one of ordinary skill in the art, before the earliest effective filing date of the invention, to modify Yuan with the aforementioned teachings of Ferreira, in the field of determining traffic conditions, with the motivation to provide a more user friendly reporting in the form of a matrix.
Conclusion
Claims 5-8 are not rejected with prior art. Closest prior art to the invention recited in these claims include Yuan and Ferreira as recited above in the rejection sections.
Additional relevant art not relied upon includes:
Zhang (CN 111259833 A), regarding “a video detecting technology based on traffic density qualitative discrimination method ", comprising the following steps: 1) the region of interest defining the monitoring image in the following steps to carry out the treatment to the region of interest, 2) calculating the fractal dimension of the image, 3) performing statistic analysis to the fractal dimension calculation result, judging the road traffic density size; vehicle density level of the patent to decision generally according to the calculation method of pore structure fractal image, but can not obtain the specific vehicle number, lack of quantitative analysis ability. in the prior art also claims method for deep learning neural network, China such as China University of Science and Technology of , , , , applications, invention patent with patent number of 201611267917.3. claims a traffic density estimation method based on deep convolutional neural network ", comprising using the camera collecting the road video image, through the image pre-processing, the multi-size pyramid image block into a convolutional neural network, extracting the bottom characteristic high-level abstractions of simple to, obtain the distribution density map of various sizes using fully connected network layer traffic image, learning density map to multi-scale distribution mapping of the whole image distribution density and image total vehicle number, distribution density graph partition of the video image output by the convolutional neural network of the region of interest, the region of interest pixel sum to obtain single-lane or multi-lane vehicle number, calculating to obtain instantaneous traffic density of the area by area length; the patent using a convolutional neural network training multi-scale traffic density characteristic. but the structure redundancy is large, multi-stage training time-consuming and difficult to control the precision.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHMET YESILDAG whose telephone number is (571)272-3257. The examiner can normally be reached M-F 8:30 am - 5:00 pm.
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Sincerely,
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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