METHOD AND SYSTEM FOR GENERATING PEDESTRIAN THERMODYNAMIC DIAGRAM

Application No. 18/702,416

• Filed: Apr 18, 2024
• Priority: Jun 24, 2022 — CN 202210727009.7
  • May 04, 2023 — nonprovisional of PCTCN2023092014
• Examiner: LE, SARAH
• Art Unit: 2614
• Tech Center: 2600 — Communications
• Assignee: BOE TECHNOLOGY GROUP CO., LTD.
• OA Round: 1 (Non-Final)

Office Action

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 .
DETAILED ACTION
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.

Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding independent claim 1, the claim directs to “a method for generating a pedestrian thermodynamic diagram, comprising: obtaining image data containing pedestrians and determining coordinates of at least one pedestrian in a scene; counting, based on the coordinates of at least one pedestrian, the at least one pedestrian separately within a corresponding grid of multiple grids obtained by dividing the scene; counting the number of the at least one pedestrian within each grid of multiple grids to draw a pedestrian thermodynamic diagram in the scene” which is a process and falls within one of the statutory categories of invention.
The claim recites:
The steps of “obtaining image data containing pedestrians” is mere data gathering. The broadest reasonable interpretation, the receiving limitation, as drafted, is process that covers performance of the limitation in human mind with gather data. The limitation falls within the “mental” process” group of abstract idea.
The step of “determining coordinates of at least one pedestrian in a scene” “determining” in this step may be practically performed in the human mind or by hand with pen and paper. The limitation falls withing the “mental process” grouping of an abstract idea.
The step of “counting, based on the coordinates of at least one pedestrian, the at least one pedestrian separately within a corresponding grid of multiple grids obtained by dividing the scene” which encompasses observing a data and performing evaluating. That covers performance of the limitation in human mind or by hand with pen and paper. Such mental observation/evaluations fall within “mental process” grouping of abstract ideas.
The step of “counting the number of the at least one pedestrian within each grid of multiple grids to draw a pedestrian thermodynamic diagram in the scene” The broadest reasonable interpretation, as drafted, covers performance of the limitation in human mind or by hand with pen and paper. The mental process performed by a person with a pen and paper, dividing the scene image to grids, mentally counting number of pedestrian within each grid and draw a diagram showing different number of pedestrian having different colors. Thus, the claim recites a mental process. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recites additional “thermodynamic diagram”, see Fig. 4 and paragraph [0061] “….In a specific example, different display colors are drawn for the grids with difference in the sum of the numbers of the pedestrians counted, allowing observers to intuitively feel the density of pedestrians. Fig. 4 shows a pedestrian thermodynamic diagram according to an embodiment of the present application, where black dots represent the pedestrians, and the grids with different numbers of pedestrians have different colors.” The thermodynamic diagram is color diagram showing different color for different number of persons that a human could mentally perform with a pen and paper. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 2 is dependent from claim 1 recites “wherein the counting, based on the coordinates of at least one pedestrian, the at least one pedestrian separately within the corresponding grid of multiple grids obtained by dividing the scene comprises: dividing the scene into multiple grids based on a predetermined fine granularity; counting the at least one pedestrian separately within the corresponding grid based on the coordinates of at least one pedestrian and the multiple grids.” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind or by hand with pen and paper, dividing the scene image to grids, mentally counting number of pedestrian within each grid. The limitation falls within the “mental” process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 3 is dependent from claim 2, recites “wherein the counting the at least one pedestrian separately within the corresponding grid based on the coordinates of at least one pedestrian and the multiple grids comprises: assigning an index value to each grid of the multiple grids in a coordinate system of the scene; taking, by using length and width values of each grid of the multiple grids, quotient values of the coordinates in length and width directions of each of at least one pedestrian separately along length and width directions of the multiple grids, to obtain a first coordinate quotient value and a second coordinate quotient value corresponding to the coordinates of each pedestrian; and comparing the first coordinate quotient value and the second coordinate quotient value with the index value of each grid to count each pedestrian within the corresponding grid” may be practically performing in human mind with pen and paper.
The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using collection, observation, analyzation and determination. The limitation falls within the “mental” process” group of abstract idea.
This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 4 is dependent from claim 1, recites “wherein the obtaining image data containing pedestrians and determine coordinates of at least one pedestrian in the scene comprises: acquiring the image data of the at least one pedestrian in the scene separately at multiple image data acquisition moments every first predetermined time; and wherein the counting the number of the at least one pedestrian in each grid of the multiple grids comprises: counting the at least one pedestrian in each grid based on the image data acquired at each image data acquisition moment, to count the at least one pedestrian in each grid for each image data acquisition moment” may be practically performing in human mind with pen and paper. The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using collection, observation, analyzation and determination. The limitation falls within the “mental” process” group of abstract idea. his judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 5 is dependent from claim 4, recites “wherein the counting the number of the at least one pedestrian in each grid of multiple grids further comprises: marking and tracking the at least one pedestrian in the scene; and judging whether the coordinates of the tracked pedestrian obtained at two adjacent image data acquisition moments of the image data are located in a same grid; not counting the at least one pedestrian at later image data acquisition moment when the coordinates are located in the same grid; counting the at least one pedestrian at later image data acquisition moment when the coordinates are located in different grids.” may be practically performing in human mind with pen and paper. The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using collection, observation, analyzation and determination. The limitation falls within the “mental” process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.

Claim 6 is dependent from claim 4, recites “wherein the counting the number of the at least one pedestrian in each grid of the multiple grids to draw the pedestrian thermodynamic diagram in the scene comprises: counting the at least one pedestrian in each grid separately for multiple image data acquisition moments; marking each grid differently based on difference in a sum of the number of the at least one pedestrian in each grid at each image data acquisition moment; and determining different marks for each grid at multiple image data acquisition moments to obtain the time-varying pedestrian thermodynamic diagram in the scene.” The broadest reasonable interpretation, as drafted, covers performance of the limitation in human mind or by hand with pen and paper. The mental process performed by a person with a pen and paper, mentally counting number of pedestrian within each grid and marking each grid different according determined sum of number of pedestrian within each grid and determining different marks for each grid. Thus, the claim recites a mental process. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. he claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 7 depends from claim 6 and recites “wherein the marking each grid differently comprises: drawing different display colors for the grids depending on the difference in the sum of the number of the at least one pedestrian in each grid.” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind with using pen and paper to drawing color diagram. The limitation falls within the “mental” process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 8 depends from claim 4 and recites “wherein the obtaining image data containing pedestrians and determine coordinates of at least one pedestrian in the scene further comprises: extracting first coordinates of the at least one pedestrian from the image data based on a coordinate system of an image acquisition device of the image data; and transforming the first coordinates to a second coordinates based on a coordinate system of the scene, and using the second coordinates as coordinates of the at least one pedestrian in the scene.” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using collection, analyzation, determination and judgment. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 9 depends from claim 8 recites:
The step of “obtaining original coordinates of the at least one pedestrian based on the coordinate system of the image acquisition device of the image data” is mere data gathering. The broadest reasonable interpretation, the obtaining limitation, as drafted, is process that covers performance of the limitation in human mind with gather data. The limitation falls within the “mental process” group of abstract idea.
The step of “obtaining a correction value of the original coordinates through a following formula .. wherein, H represents a height of the image acquisition device of the image data, x2 represents center coordinates of the scene where the image acquisition device of the image data is located, xl represents the original coordinates obtained by the image acquisition device, h represents a height of the at least one pedestrian, and x represents the correction value of the original coordinates” The broadest reasonable interpretation, the formula limitation, as drafted, is mathematical formulas . The limitation falls within the “mathematical concept” group of abstract idea.
The steps of “correcting the original coordinates by using the correction value of the original coordinates based on a quadrant of the coordinate system of the image acquisition device where the original coordinates are located, to obtain the first coordinates” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using observation, analyzation, determination and judgment. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 10 depends from claim 9 recites “wherein the height of the at least one pedestrian is a mode of heights of pedestrians” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using collection, observation, determination and opinion. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 11 depends from claim 10 recites “wherein the original coordinates, the first coordinates, and the second coordinates comprise head coordinates of the at least one pedestrian” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using observation, analyzation, determination and opinion. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 12 depends from claim 11 recites “wherein the acquiring the image data of the at least one pedestrian in the scene separately at multiple image data acquisition moments every first predetermined time comprises: setting the image acquisition device at a position higher than the height of the at least one pedestrian to acquire head image data of the at least one pedestrian” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using observation, analyzation, determination and collection. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 13 depends from claim 12, recites” wherein the setting the image acquisition device at the position higher than the height of the at least one pedestrian to acquire the head image data of the at least one pedestrian comprises: acquiring the head image data of the at least one pedestrian through an image acquisition device located at a center of the scene.” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using collection, observation, analyzation and determination. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 14 depends from claim 12 recites “wherein the setting the image acquisition device at the position higher than the height of the at least one pedestrian to acquire the head image data of the at least one pedestrian comprises: acquiring the head image data of the at least one pedestrian through multiple image acquisition devices having an acquisition area covering an entire scene.” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using collection, observation, analyzation and determination. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 15 depends from claim 14, recites “wherein, an acquisition overlapping area of at least two image acquisition devices is further included in the scene, and the acquiring the head image data of the at least one pedestrian through multiple image acquisition devices having the acquisition area covering the entire scene comprises: selecting head image data of at least one pedestrian acquired by one of the at least two image acquisition devices corresponding to the acquisition overlapping area.” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using collection, observation, analyzation and determination. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 16 depends from claim 15 recites “wherein a noise area is further included in the scene, and the acquiring the head image data of the at least one pedestrian through multiple image acquisition devices having the acquisition area covering the entire scene comprises: not acquiring head image data of at least one pedestrian in the noise area.” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using collection, observation, analyzation and determination. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 17 depends from claim 8 and recites “wherein the transforming the first coordinates to the second coordinates based on the coordinate system of the scene comprises: transforming, by rotating coordinates, the first coordinates based on the coordinate system of the image acquisition device of the image data to the second coordinates of the coordinate system of the scene” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using analyzation, determination and judgment. The limitation falls within the “mental process” group of abstract idea. This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 18 depends from claim 1 and recites “a system for generating a pedestrian thermodynamic diagram, comprising: at least one image acquisition device; a processor; and a memory, wherein, the at least one image acquisition device is configured to acquire image data of at least one pedestrian in a scene and send the image data to the processor and/or the memory, and the memory stores an instruction that can be executed by the processor, the instruction, when executed by the processor, causes the processor to perform the method according to claim 1.”
As above claim 1, the claim covers performance of the limitation in human mind or by hand with pen and paper. The mental process performed by a person with a pen and paper, dividing the scene image to grids, mentally counting number of pedestrian within each grid and draw a diagram showing different number of pedestrian having different colors. Thus, the claim recites a mental process. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional element: a processor; and a memory, which is considered as component of computer to perform function steps. One image acquisition device is considered a camera. Using a generic computer component cannot provide an invention concepts. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 19 depends from claim 18 recites “wherein the at least one image acquisition device comprises one image acquisition device, a height of the image acquisition device is greater than a height of the at least one pedestrian, and the image acquisition device is located at a center of the scene.” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using observation, judgment and opinion. The limitation falls within the “mental” process” group of abstract idea.
This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 20 depends from claim 18 and recites “wherein the at least one image acquisition device comprises at least two image acquisition devices, heights of at least two image acquisition devices are greater than a height of the at least one pedestrian, and the at least two image acquisition devices have an image acquisition area covering an entire scene.” The broadest reasonable interpretation, this limitation, as drafted, is process that covers performance of the limitation in human mind using observation, judgment and opinion. The limitation falls within the “mental” process” group of abstract idea.
This judicial exception is not integrated into a practical application because the claim does not recite any additional elements beyond the judicial exception. Accordingly, this judicial exception is not integrated into a practical application because the claim does not prove an improvement to functioning of computers or an improvement to other technology or technical field. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.

Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 recites the phrase “ a pedestrian …” in line 7. It should be “the pedestrian..” Appropriate correction is required.

Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.

The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.

Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the number” in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 depends from claim 1 recites the limitation “determine coordinates” in line 2”. It's unclear whether that's the same as the step "determining coordinates..." in line 2 of claim 1 or something else.
Claim 5 recites the limitation “the coordinates of the tracked pedestrian”. It is unclear if the coordinates refers to “coordinates of at least one pedestrian” in line 2 of claim 4 or something else and it’s unclear whether “pedestrian” refers to “the at least one pedestrian” or something else.
Claims 5-17 are rejected based on the rejection of claim 4.
Claim 6 recites the limitation “the time-varying” in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 is rejected based on the rejection of claim 6.
Claims 2-20 are rejected based on the rejection of claim 1.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –

(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.

Claims 1-2, 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Heran et al, IDS, CN113468250A (English translated) (“Heran”)
Regarding independent claim 1, Heran teaches a method for generating a pedestrian thermodynamic diagram ([n0004] This disclosure provides a method, apparatus, terminal device, and computer storage medium for generating heat maps”; [n0055] In this embodiment of the disclosure, a new method for generating heat maps will be provided. This method can generate heat maps by combining the dwell time of human data and the number of people, or by combining the dwell time of human data, facial data and the number of people. It can objectively assess the thermal situation of the actual area and the resulting heat map is closer to the actual scene.” where heat maps is considered as thermodynamic diagram), comprising:
obtaining image data containing pedestrians and determining coordinates of at least one pedestrian in a scene (see at least [n0168]-[n0171] “..The data reported by the subsequently selected camera equipment (including human body data and/or facial data). The reported data can be at least one of the coordinates of a human body bounding box and the coordinates of a face bounding box with a fixed time interval. The time interval can be configured, with a default of 1000ms. The coordinates of the human body bounding box and the face bounding box, along with the coordinate sequence number, are reported to the heat map generation device. The heat map generation device can calculate the timestamp of each coordinate based on at least one of the following: the time information of when each human body first appears, the time interval, and the coordinate sequence number… [n0171]The human body information of each person may include at least one of the following: the position information of the human body bounding box of each person in the image, the position information of the feature points (e.g., at least one of the four vertices) on the human body bounding box of each person in the image, and the position information of the feature points (e.g., at least one of the head feature points, arm feature points, and leg feature points) of each person in the image. In other embodiments, the human body information of each person may also include at least one of the following: height information, gender information, body shape information, clothing style information, and occupation information. In this embodiment of the disclosure, the location information may include coordinate information, which may be two-dimensional coordinate information” );
counting, based on the coordinates of at least one pedestrian, the at least one pedestrian separately within a corresponding grid of multiple grids obtained by dividing the scene(see at least [n0072] S102. Obtain multiple grids obtained by dividing the electronic map corresponding to the image.[n0073] At least a portion of the image may correspond to the same scene as at least a portion of the electronic map. For example, an image could be taken by a camera device targeting a specific corridor on a floor, while an electronic map could be a map of that floor. For example, an image can be taken by a camera device targeting a specific floor, and an electronic map can be a map of that floor...”; [n0077] S103. Based on human body information, obtain the number of human body-related points matched in each grid in multiple grids. [n0078] Human-related points may include at least one of the following: at least one vertex of the human body bounding box, or a human body key point. When there are N human bodies in an image, the number of human body related points can be a multiple of N. For example, the number of human body related points can be N, 2N, 3N, or 4N, etc. [n0172] Step 3: Generate grid data. Heatmap generation devices divide electronic maps into rectangular grids, also known as plan grids, and assign a planGrid identifier to each planGrid. A rectangular planGrid consists of n (height) × m (width) pixels. The values of n and m range from 1 to 10. n and m default to 5, which means a 5×5 rectangle. The planGrid consists of at least two coordinate points located within the rectangle. The plangrid is the basic spatial unit for thermal analysis.” where number of human body-related points is considered as counting pedestrian);
counting the number of the at least one pedestrian within each grid of multiple grids to draw a pedestrian thermodynamic diagram in the scene (see at least [n0077] S103. Based on human body information, obtain the number of human body-related points matched in each grid in multiple grids. [n0078] Human-related points may include at least one of the following: at least one vertex of the human body bounding box, or a human body key point. When there are N human bodies in an image, the number of human body related points can be a multiple of N. For example, the number of human body related points can be N, 2N, 3N, or 4N, etc. [n0079] For example, when the human body information includes the position information of the four vertices of the human body bounding box in the image, the number of vertices matched by each grid can be used to determine the number of human body-related points matched by each grid. For example, if the number of vertices falling into a certain grid within a target time period is S, then the number of human-related points matched by that grid is also S, where S is an integer greater than or equal to 0. [n0080] S104. Generate a heat map based on the number of human-related points matched in each grid.” [n0083] In some implementations, a heatmap can be obtained by rendering the grid that matches the number of each human body's related points with the color corresponding to that distribution interval, based on the distribution interval to which the number of each human body's related points belongs. For example, when there are at least two distribution intervals with five values, the grid in the heatmap has five colors. In other implementations, it is not necessary to determine which distribution range the number of human body related points belongs to. The heat map generation device can determine the rendering color of different grids based on the different number of human body related points matched by each grid. For example, the larger the number of human body related points matched by a grid, the darker the color of the grid will be. Correspondingly, the smaller the number of human-related points matched by a grid, the lighter the color of the grid will be.; [n0173] Step 4: Generate heatmap statistics. Within a given time period (corresponding to the target time period mentioned above), the number of human body frames and their coordinates within a planGrid is the heat value of that planGrid during the query period. After calculating the thermal values of all planGrids, the distribution range of the thermal distribution intervals is obtained according to the N thermal intervals, either by average distribution or normal distribution, and each planGrid is assigned to one of the N thermal distribution intervals. The number of thermal zones is 5 by default (it can be configured to other values” where the heatmap is equivalent to a thermodynamic diagram ).
Regarding claim 2, Heran teaches the method according to claim 1, wherein the counting, based on the coordinates of at least one pedestrian, the at least one pedestrian separately within the corresponding grid of multiple grids obtained by dividing the scene comprises:
dividing the scene into multiple grids based on a predetermined fine granularity (see at least [n0072] S102. Obtain multiple grids obtained by dividing the electronic map corresponding to the image.[n0073] At least a portion of the image may correspond to the same scene as at least a portion of the electronic map. For example, an image could be taken by a camera device targeting a specific corridor on a floor, while an electronic map could be a map of that floor. For example, an image can be taken by a camera device targeting a specific floor, and an electronic map can be a map of that floor.. [n0076] In some implementations, each of the multiple grids can be a rectangular grid, which can be a square grid or a rectangular grid, and one side of the rectangular grid can correspond to at least one pixel. For example, in some implementations, each side of the square grid can correspond to 5 pixels.; [n0172] Step 3: Generate grid data. Heatmap generation devices divide electronic maps into rectangular grids, also known as plan grids, and assign a planGrid identifier to each planGrid. A rectangular planGrid consists of n (height) × m (width) pixels. The values of n and m range from 1 to 10. n and m default to 5, which means a 5×5 rectangle. The planGrid consists of at least two coordinate points located within the rectangle. The plangrid is the basic spatial unit for thermal analysis.);
counting the at least one pedestrian separately within the corresponding grid based on the coordinates of at least one pedestrian and the multiple grids (see at least [n0077] S103. Based on human body information, obtain the number of human body-related points matched in each grid in multiple grids. [n0078] Human-related points may include at least one of the following: at least one vertex of the human body bounding box, or a human body key point. When there are N human bodies in an image, the number of human body related points can be a multiple of N. For example, the number of human body related points can be N, 2N, 3N, or 4N, etc. [n0079] For example, when the human body information includes the position information of the four vertices of the human body bounding box in the image, the number of vertices matched by each grid can be used to determine the number of human body-related points matched by each grid. For example, if the number of vertices falling into a certain grid within a target time period is S, then the number of human-related points matched by that grid is also S, where S is an integer greater than or equal to 0.”; [n0083] In some implementations, a heatmap can be obtained by rendering the grid that matches the number of each human body's related points with the color corresponding to that distribution interval, based on the distribution interval to which the number of each human body's related points belongs. For example, when there are at least two distribution intervals with five values, the grid in the heatmap has five colors. In other implementations, it is not necessary to determine which distribution range the number of human body related points belongs to. The heat map generation device can determine the rendering color of different grids based on the different number of human body related points matched by each grid. For example, the larger the number of human body related points matched by a grid, the darker the color of the grid will be. Correspondingly, the smaller the number of human-related points matched by a grid, the lighter the color of the grid will be.”; [n0173] Step 4: Generate heatmap statistics. Within a given time period (corresponding to the target time period mentioned above), the number of human body frames and their coordinates within a planGrid is the heat value of that planGrid during the query period. After calculating the thermal values of all planGrids, the distribution range of the thermal distribution intervals is obtained according to the N thermal intervals, either by average distribution or normal distribution, and each planGrid is assigned to one of the N thermal distribution intervals. The number of thermal zones is 5 by default (it can be configured to other values)”.)
Regarding claim 18, Heran teaches a system for generating a pedestrian thermodynamic diagram, comprising: at least one image acquisition device; a processor; and a memory, wherein, the at least one image acquisition device is configured to acquire image data of at least one pedestrian in a scene and send the image data to the processor and/or the memory (see at least [0058] In some implementations, the architecture may include a camera device, an image analysis device, and a heatmap generation device. The camera device continuously captures each image and sends the captured images to the image analysis device. The image analysis device analyzes the captured images to obtain analysis data and sends the analysis data to the heatmap generation device so that the heatmap generation device can generate a heatmap based on the analysis data. Image analysis equipment and camera equipment can be integrated together or set up separately.”;[n0012] The system receives images transmitted by the electronic device at various times, and position information of at least one of the human body frame and face frame in the image on the image; determines at least one of each human body image and each face image in the image corresponding to the received position information; and analyzes at least one of each human body image and each face image to obtain at least one of the attribute information of each human body and each face.”; [n0187] Figure 12 is a schematic diagram of the composition structure of a heat map generation device provided in an embodiment of this disclosure. As shown in Figure 12, the heat map generation device 1200 can be a processor, a chip, or a heat map generation device. The heat map generation device 1200 includes: an acquisition unit 1201, used to acquire human body information of each human body in an image; acquire multiple grids obtained by dividing an electronic map corresponding to the image; a determination unit 1202, used to obtain the number of human body-related points matched in each grid based on the human body information; and a generation unit 1203, used to generate a heat map based on the number of human body-related points matched in each”), and the memory stores an instruction that can be executed by the processor, the instruction, when executed by the processor ([n0205] Figure 13 is a schematic diagram of the hardware entity of a heat map generation device provided in an embodiment of the present disclosure. As shown in Figure 13, the hardware entity of the heat map generation device 1300 includes a processor 1301 and a memory 1302. The memory 1302 stores a computer program that can run on the processor 1301. When the processor 1301 executes the program, it implements the steps in the method of any of the above embodiments. [n0206] The memory 1302 stores computer programs that can run on the processor. The memory 1302 is configured to store instructions and applications that can be executed by the processor 1301. It can also cache data to be processed or already processed (e.g., image data, audio data, voice communication data, and video communication data) in the processor 1301 and the various modules in the heat map generation device 1300. It can be implemented by flash memory or random access memory (RAM), causes the processor to perform the method according to claim 1. (see the rejection for claim 1)
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.

The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
1. Claims 3- 4, 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Heran et al, IDS, CN113468250A (English translated) (“Heran”) in view of Zexu et al, IDS, CN109189878 (English translated) (“Zexu”)
Regarding claim 3, Heran teach the method according to claim 2, wherein the counting the at least one pedestrian separately within the corresponding grid based on the coordinates of at least one pedestrian and the multiple grids comprises: Heran is understood to be silent on the remaining limitations of claim 1.
In the same field of endeavor, Zexu teaches assigning an index value to each grid of the multiple grids in a coordinate system of the scene (see at least [0053] The uniform length and width can be divided into equal parts. For example, the length and width can be divided into ten equal parts, and the resulting data range is 0.1, namely 0 to 0.1, 0.1 to 0.2, 0.2 to 0.3, 0.3 to 0.4, 0.4 to 0.5, 0.5 to 0.6, 0.6 to 0.7, 0.7 to 0.8, 0.8 to 0.9, and 0.9 to 1.0. Therefore, the coordinate value (0.45, 0.44) falls within the data range with a long interval of 0.4 to 0.5 and a wide interval of 0.4 to 0.5. A grid can be formed by dividing a uniform length and width into equal parts.” Where the length and width can be divided into ten equal parts, and the resulting data range is 0.1,namely 0 to 0.1, 0.1 to 0.2, 0.2 to 0.3, 0.3 to 0.4, 0.4 to 0.5, 0.5 to 0.6, 0.6 to 0.7, 0.7 to 0.8, 0.8 to 0.9, and 0.9 to 1.0 is considered as index value);
taking, by using length and width values of each grid of the multiple grids, quotient values of the coordinates in length and width directions of each of at least one pedestrian separately along length and width directions of the multiple grids, to obtain a first coordinate quotient value and a second coordinate quotient value corresponding to the coordinates of each pedestrian (see at least [0052] Specifically, pedestrians on the crowd heat map can be enclosed in boxes, and the midpoint of the bottom of the box corresponding to each pedestrian can be obtained. This midpoint can then be used as the coordinate value of the pedestrian. Specifically, the length and width of each crowd heat map can be standardized to 0 to 1. For example, if the length of the crowd heat map is 20cm and the width is 18cm, and the position of the bottom midpoint of the box of a certain pedestrian in the map is 9cm long and 8cm wide, then the corresponding coordinate value is (9/20, 8/18), that is, (0.45, 0.44). Step S130: Based on the edge data intervals where the coordinate values fall within a preset number of grids, obtain the coordinates of the people who have stayed in each of the preset number of grids and the number of people.”); and
comparing the first coordinate quotient value and the second coordinate quotient value with the index value of each grid to count each pedestrian within the corresponding grid (see at least [0053] “The uniform length and width can be divided into equal parts. For example, the length and width can be divided into ten equal parts, and the resulting data range is 0.1, namely 0 to 0.1, 0.1 to 0.2, 0.2 to 0.3, 0.3 to 0.4, 0.4 to 0.5, 0.5 to 0.6, 0.6 to 0.7, 0.7 to 0.8, 0.8 to 0.9, and 0.9 to 1.0. Therefore, the coordinate value (0.45, 0.44) falls within the data range with a long interval of 0.4 to 0.5 and a wide interval of 0.4 to 0.5. A grid can be formed by dividing a uniform length and width into equal parts. [0054] The coordinates and number of people who have stayed in each grid can be obtained using the above method.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of a thermodynamic diagram generation of Heran with obtaining the coordinates of the people who have stayed in each of the preset number of grids and the number of people as seen in Zexu because this modification would form a crowd heat map ([0055] of Zexu)
Regarding claim 4, Heran teaches the method according to claim 1, wherein the obtaining image data containing pedestrians and determine coordinates of at least one pedestrian in the scene comprises:
acquiring the image data of the at least one pedestrian in the scene separately at multiple image data acquisition moments every first predetermined time ([n0067] At least one frame can be a continuously captured image or a non-continuously captured image. For example, a camera device can take an image at set intervals, and the human body information of each person obtained by the heat map generation device is determined from at least one frame of image taken at set intervals within the target time period. In other implementations, the camera device can capture an image at set intervals. Then, the camera device analyzes whether the similarity between the captured image and the previous image (i.e., the previously captured image) is less than a threshold. If it is less than a threshold, it indicates that the difference between the captured image and the previous image is relatively large. The camera device analyzes the captured image to obtain the human body information of each person in the captured image. If it is greater than or equal to the threshold, the camera device will capture the next image without analyzing the human body information of the captured image, until it is determined that the similarity between a subsequently captured image and the previous image is less than a threshold. The camera device analyzes the image to obtain the human body information of each person in the image, thereby obtaining the human body information of each person in the images obtained within the target time period.”); and wherein the counting the number of the at least one pedestrian in each grid of the multiple grids comprises:
counting the at least one pedestrian in each grid based on the image data acquired at each image data acquisition moment, to count the at least one pedestrian in each grid for each image data acquisition moment (see at least [n0077] S103. Based on human body information, obtain the number of human body-related points matched in each grid in multiple grids.” [n0078] Human-related points may include at least one of the following: at least one vertex of the human body bounding box, or a human body key point. When there are N human bodies in an image, the number of human body related points can be a multiple of N. For example, the number of human body related points can be N, 2N, 3N, or 4N, etc. [n0079] For example, when the human body information includes the position information of the four vertices of the human body bounding box in the image, the number of vertices matched by each grid can be used to determine the number of human body-related points matched by each grid. For example, if the number of vertices falling into a certain grid within a target time period is S, then the number of human-related points matched by that grid is also S, where S is an integer greater than or equal to 0.”) Heran is understood to be silent on the remaining limitations of claim 4.
In the same field of endeavor, Zexu teaches acquiring the image data of the at least one pedestrian in the scene separately at multiple image data acquisition moments every first predetermined time ([0011] In one possible design, before acquiring the human body location recognition result data, the method further includes: acquiring a video stream captured by a camera; controlling a graphics processor to process the video stream to obtain the human body location recognition result data [0030] Grid filtering queries can be based on time. For example, a camera can capture footage all day, while a grid filtering query can select a specific time period, such as 4 pm to 5 pm on August 21st, and then obtain multiple heat maps of the population distribution within that time period.” [0032] The request to obtain a heat map of population distribution can be initiated by a user through their terminal device. After the user initiates the request, the controller receives the request. The request to obtain a population distribution heatmap may include the time information of the population distribution heatmap and the location information corresponding to the population distribution.”;.);
counting the at least one pedestrian in each grid based on the image data acquired at each image data acquisition moment, to count the at least one pedestrian in each grid for each image data acquisition moment ([0052] Specifically, pedestrians on the crowd heat map can be enclosed in boxes, and the midpoint of the bottom of the box corresponding to each pedestrian can be obtained. This midpoint can then be used as the coordinate value of the pedestrian. Specifically, the length and width of each crowd heat map can be standardized to 0 to 1. For example, if the length of the crowd heat map is 20cm and the width is 18cm, and the position of the bottom midpoint of the box of a certain pedestrian in the map is 9cm long and 8cm wide, then the corresponding coordinate value is (9/20, 8/18), that is, (0.45, 0.44). Step S130: Based on the edge data intervals where the coordinate values fall within a preset number of grids, obtain the coordinates of the people who have stayed in each of the preset number of grids and the number of people. [0053] The uniform length and width can be divided into equal parts. For example, the length and width can be divided into ten equal parts, and the resulting data range is 0.1, namely 0 to 0.1, 0.1 to 0.2, 0.2 to 0.3, 0.3 to 0.4, 0.4 to 0.5, 0.5 to 0.6, 0.6 to 0.7, 0.7 to 0.8, 0.8 to 0.9, and 0.9 to 1.0. Therefore, the coordinate value (0.45, 0.44) falls within the data range with a long interval of 0.4 to 0.5 and a wide interval of 0.4 to 0.5.”) A grid can be formed by dividing a uniform length and width into equal parts. [0054] The coordinates and number of people who have stayed in each grid can be obtained using the above method. [0055] Step S140: Take the average value of the coordinates of the people who have stayed in each grid as the average coordinates of that grid, and mark the number of people at the location corresponding to the average coordinates to form a crowd heat map.”; [0071] In the crowd heat map acquisition method and apparatus provided in this application embodiment, the method includes: obtaining multiple crowd heat map distribution images within a first preset time period according to a grid filtering query statement; obtaining the coordinate values corresponding to pedestrians on each of the multiple crowd heat map distribution images; obtaining the coordinate points and number of people who have stayed in each of the preset number of grids according to the edge data intervals where the coordinate values fall within a preset number of grids; taking the average value of the coordinate points of the people who have stayed in each grid as the average coordinate point of that grid, and marking the number of people at the position corresponding to the average coordinate point to form a crowd heat map.”) In addition, the same motivation is used as the rejection for claim 3.
Thus, the combination of Heran and Zexu teaches wherein the obtaining image data containing pedestrians and determine coordinates of at least one pedestrian in the scene comprises: acquiring the image data of the at least one pedestrian in the scene separately at multiple image data acquisition moments every first predetermined time; and wherein the counting the number of the at least one pedestrian in each grid of the multiple grids comprises: counting the at least one pedestrian in each grid based on the image data acquired at each image data acquisition moment, to count the at least one pedestrian in each grid for each image data acquisition moment.
Regarding claim 6, Heran and Zexu teach the method according to claim 4, wherein the counting the number of the at least one pedestrian in each grid of the multiple grids to draw the pedestrian thermodynamic diagram in the scene comprises:
counting the at least one pedestrian in each grid separately for multiple image data acquisition moments (see at least Heran:[n0077] S103. Based on human body information, obtain the number of human body-related points matched in each grid in multiple grids.” [n0078] Human-related points may include at least one of the following: at least one vertex of the human body bounding box, or a human body key point. When there are N human bodies in an image, the number of human body related points can be a multiple of N. For example, the number of human body related points can be N, 2N, 3N, or 4N, etc. [n0079] For example, when the human body information includes the position information of the four vertices of the human body bounding box in the image, the number of vertices matched by each grid can be used to determine the number of human body-related points matched by each grid. For example, if the number of vertices falling into a certain grid within a target time period is S, then the number of human-related points matched by that grid is also S, where S is an integer greater than or equal to 0.”; [0052] of Zexu “ Specifically, pedestrians on the crowd heat map can be enclosed in boxes, and the midpoint of the bottom of the box corresponding to each pedestrian can be obtained. This midpoint can then be used as the coordinate value of the pedestrian. Specifically, the length and width of each crowd heat map can be standardized to 0 to 1. For example, if the length of the crowd heat map is 20cm and the width is 18cm, and the position of the bottom midpoint of the box of a certain pedestrian in the map is 9cm long and 8cm wide, then the corresponding coordinate value is (9/20, 8/18), that is, (0.45, 0.44). Step S130: Based on the edge data intervals where the coordinate values fall within a preset number of grids, obtain the coordinates of the people who have stayed in each of the preset number of grids and the number of people. [0053] The uniform length and width can be divided into equal parts. For example, the length and width can be divided into ten equal parts, and the resulting data range is 0.1, namely 0 to 0.1, 0.1 to 0.2, 0.2 to 0.3, 0.3 to 0.4, 0.4 to 0.5, 0.5 to 0.6, 0.6 to 0.7, 0.7 to 0.8, 0.8 to 0.9, and 0.9 to 1.0. Therefore, the coordinate value (0.45, 0.44) falls within the data range with a long interval of 0.4 to 0.5 and a wide interval of 0.4 to 0.5.”) A grid can be formed by dividing a uniform length and width into equal parts. [0054] The coordinates and number of people who have stayed in each grid can be obtained using the above method. [0055] Step S140: Take the average value of the coordinates of the people who have stayed in each grid as the average coordinates of that grid, and mark the number of people at the location corresponding to the average coordinates to form a crowd heat map.”) ;
marking each grid based on a sum of the number of the at least one pedestrian in each grid at each image data acquisition moment ( see at least Heran [n0083] In some implementations, a heatmap can be obtained by rendering the grid that matches the number of each human body's related points with the color corresponding to that distribution interval, based on the distribution interval to which the number of each human body's related points belongs. For example, when there are at least two distribution intervals with five values, the grid in the heatmap has five colors.In other implementations, it is not necessary to determine which distribution range the number of human body related points belongs to. The heat map generation device can determine the rendering color of different grids based on the different number of human body related points matched by each grid. For example, the larger the number of human body related points matched by a grid, the darker the color of the grid will be. Correspondingly, the smaller the number of human-related points matched by a grid, the lighter the color of the grid will be.” ; [0071] of Zexu “ In the crowd heat map acquisition method and apparatus provided in this application embodiment, the method includes: obtaining multiple crowd heat map distribution images within a first preset time period according to a grid filtering query statement; obtaining the coordinate values corresponding to pedestrians on each of the multiple crowd heat map distribution images; obtaining the coordinate points and number of people who have stayed in each of the preset number of grids according to the edge data intervals where the coordinate values fall within a preset number of grids; taking the average value of the coordinate points of the people who have stayed in each grid as the average coordinate point of that grid, and marking the number of people at the position corresponding to the average coordinate point to form a crowd heat map. This application embodiment can first obtain multiple crowd heat maps within a first preset time period, obtain the coordinate values of pedestrians on each crowd heat map, obtain the coordinate points and number of people who have stayed in each grid based on the edge data intervals where the coordinate values fall within a preset number of grids, and then take the average of the coordinate points of the people who have stayed in each grid as the average coordinate points of that grid, and mark the number of people at the corresponding positions to form a crowd heat map, making the data display more intuitive.”); and determining different marks for each grid at multiple image data acquisition moments to obtain the time-varying pedestrian thermodynamic diagram in the scene (see at least Heran [n0083] In some implementations, a heatmap can be obtained by rendering the grid that matches the number of each human body's related points with the color corresponding to that distribution interval, based on the distribution interval to which the number of each human body's related points belongs. For example, when there are at least two distribution intervals with five values, the grid in the heatmap has five colors. In other implementations, it is not necessary to determine which distribution range the number of human body related points belongs to. The heat map generation device can determine the rendering color of different grids based on the different number of human body related points matched by each grid. For example, the larger the number of human body related points matched by a grid, the darker the color of the grid will be. Correspondingly, the smaller the number of human-related points matched by a grid, the lighter the color of the grid will be. [n0077] S103. Based on human body information, obtain the number of human body-related points matched in each grid in multiple grids.” [n0078] Human-related points may include at least one of the following: at least one vertex of the human body bounding box, or a human body key point. When there are N human bodies in an image, the number of human body related points can be a multiple of N. For example, the number of human body related points can be N, 2N, 3N, or 4N, etc. [n0079] For example, when the human body information includes the position information of the four vertices of the human body bounding box in the image, the number of vertices matched by each grid can be used to determine the number of human body-related points matched by each grid. For example, if the number of vertices falling into a certain grid within a target time period is S, then the number of human-related points matched by that grid is also S, where S is an integer greater than or equal to 0.”; [0071] of Zexu “ In the crowd heat map acquisition method and apparatus provided in this application embodiment, the method includes: obtaining multiple crowd heat map distribution images within a first preset time period according to a grid filtering query statement; obtaining the coordinate values corresponding to pedestrians on each of the multiple crowd heat map distribution images; obtaining the coordinate points and number of people who have stayed in each of the preset number of grids according to the edge data intervals where the coordinate values fall within a preset number of grids; taking the average value of the coordinate points of the people who have stayed in each grid as the average coordinate point of that grid, and marking the number of people at the position corresponding to the average coordinate point to form a crowd heat map. This application embodiment can first obtain multiple crowd heat maps within a first preset time period, obtain the coordinate values of pedestrians on each crowd heat map, obtain the coordinate points and number of people who have stayed in each grid based on the edge data intervals where the coordinate values fall within a preset number of grids, and then take the average of the coordinate points of the people who have stayed in each grid as the average coordinate points of that grid, and mark the number of people at the corresponding positions to form a crowd heat map, making the data display more intuitive.”) In addition, the same motivation is used as the rejection for claim 3.
Regarding claim 7, Heran and Zexu teach the method according to claim 6, wherein the marking each grid differently comprises: drawing different display colors for the grids depending on the difference in the sum of the number of the at least one pedestrian in each grid (see at least Heran [n0083] In some implementations, a heatmap can be obtained by rendering the grid that matches the number of each human body's related points with the color corresponding to that distribution interval, based on the distribution interval to which the number of each human body's related points belongs. For example, when there are at least two distribution intervals with five values, the grid in the heatmap has five colors. In other implementations, it is not necessary to determine which distribution range the number of human body related points belongs to. The heat map generation device can determine the rendering color of different grids based on the different number of human body related points matched by each grid. For example, the larger the number of human body related points matched by a grid, the darker the color of the grid will be. Correspondingly, the smaller the number of human-related points matched by a grid, the lighter the color of the grid will be. [n0077] S103. Based on human body information, obtain the number of human body-related points matched in each grid in multiple grids.” [n0078] Human-related points may include at least one of the following: at least one vertex of the human body bounding box, or a human body key point. When there are N human bodies in an image, the number of human body related points can be a multiple of N. For example, the number of human body related points can be N, 2N, 3N, or 4N, etc. [n0079] For example, when the human body information includes the position information of the four vertices of the human body bounding box in the image, the number of vertices matched by each grid can be used to determine the number of human body-related points matched by each grid. For example, if the number of vertices falling into a certain grid within a target time period is S, then the number of human-related points matched by that grid is also S, where S is an integer greater than or equal to 0.”; [0071] of Zexu “ In the crowd heat map acquisition method and apparatus provided in this application embodiment, the method includes: obtaining multiple crowd heat map distribution images within a first preset time period according to a grid filtering query statement; obtaining the coordinate values corresponding to pedestrians on each of the multiple crowd heat map distribution images; obtaining the coordinate points and number of people who have stayed in each of the preset number of grids according to the edge data intervals where the coordinate values fall within a preset number of grids; taking the average value of the coordinate points of the people who have stayed in each grid as the average coordinate point of that grid, and marking the number of people at the position corresponding to the average coordinate point to form a crowd heat map. This application embodiment can first obtain multiple crowd heat maps within a first preset time period, obtain the coordinate values of pedestrians on each crowd heat map, obtain the coordinate points and number of people who have stayed in each grid based on the edge data intervals where the coordinate values fall within a preset number of grids, and then take the average of the coordinate points of the people who have stayed in each grid as the average coordinate points of that grid, and mark the number of people at the corresponding positions to form a crowd heat map, making the data display more intuitive.”) In addition, the same motivation is used as the rejection for claim 3.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over
Heran et al, IDS, CN113468250A (English translated) (“Heran”) in view of Zexu et al, IDS, CN109189878 (English translated) (“Zexu”) further in view of Xiaobo et al., CN109902551A (English translated) (“Xiaobo”)
Regarding claim 5, Heran and Zexu teach the method according to claim 4, wherein the counting the number of the at least one pedestrian in each grid of multiple grids further comprises:
marking the at least one pedestrian in the scene (see at least [n0073] of Heran “At least a portion of the image may correspond to the same scene as at least a portion of the electronic map. For example, an image could be taken by a camera device targeting a specific corridor on a floor, while an electronic map could be a map of that floor. For example, an image can be taken by a camera device targeting a specific floor, and an electronic map can be a map of that floor”)
and judging whether the coordinates of the tracked pedestrian obtained in a same grid; not counting the at least one pedestrian at later image data acquisition moment when the coordinates are located in the same grid; counting the at least one pedestrian at later image data acquisition moment when the coordinates are located in different grids (see at least Heran: [n0173] Step 4: Generate heatmap statistics. Within a given time period (corresponding to the target time period mentioned above), the number of human body frames and their coordinates within a planGrid is the heat value of that planGrid during the query period. After calculating the thermal values of all planGrids, the distribution range of the thermal distribution intervals is obtained according to the N thermal intervals, either by average distribution or normal distribution, and each planGrid is assigned to one of the N thermal distribution intervals. The number of thermal zones is 5 by default (it can be configured to other values); Zexu :“[0054] The coordinates and number of people who have stayed in each grid can be obtained using the above method. [0055] Step S140: Take the average value of the coordinates of the people who have stayed in each grid as the average coordinates of that grid, and mark the number of people at the location corresponding to the average coordinates to form a crowd heat map. [0056] The average coordinates of the people who have stayed in each grid are taken as the average coordinates of that grid. For example, for grid A, if 5 people have stayed in the grid during the first preset time period, the coordinates (horizontal and vertical coordinates) of the 5 people can be obtained. Then, the average of the horizontal and vertical coordinates are taken as the average coordinates. The average horizontal and vertical coordinates are then used as the average coordinates. The number 5 is then marked at the position corresponding to the average coordinates, thus forming a heat map of the crowd. [0057] The first embodiment of this application can first obtain multiple crowd heat maps within a first preset time period, obtain the coordinate values of pedestrians on each crowd heat map, obtain the coordinate points and number of people who have stayed in each grid based on the edge data intervals where the coordinate values fall within a preset number of grids, then take the average of the coordinate points of the people who have stayed in each grid as the average coordinate points of that grid, and mark the number of people at the corresponding positions to form a crowd heat map, making the data display more intuitive.”) In addition, the same motivation is used as the rejection for claim 3. Heran and Zexu are understood to be silent on the remaining limitations of claim 5.
In the same field of endeavor, Xiaobo teaches marking and tracking the at least one pedestrian in the scene (see at least [0043] Specifically, for the position of each pedestrian output by the object detection algorithm, and the portion of the image region occupied by that pedestrian, the positional features of that pedestrian are generated from the position of each pedestrian (such as the coordinates of the portion of the image region occupied by that pedestrian in the image coordinate system), and the appearance features of that pedestrian (such as clothing color, clothing texture, handbag, backpack, hat, etc.) are generated from the image of the portion of the image region occupied by that pedestrian. Using the location and appearance features of a pedestrian in the current valid frame, search in the previous N valid frames to see if the pedestrian already exists. If not, generate a new person identifier for the pedestrian and mark the pedestrian with the generated person identifier. The person identifier is used to uniquely represent a pedestrian and can be an index number, string, etc., without any restrictions. If the pedestrian already exists, then the pedestrian already has its own person identifier, and the existing person identifier can be used to mark the pedestrian in the current valid frame. The above search process is performed on each pedestrian detected in the current valid frame until each detected pedestrian is marked with a person identifier.
and judging whether the coordinates of the tracked pedestrian obtained at two adjacent image data acquisition moments of the image data are located in a same grid (see at least [0050] “In applications that require counting the number of people passing through a predetermined statistical area in an open scene, for a given valid frame, if a pedestrian appears in the statistical area within that valid frame and has not appeared in the statistical area in the N valid frames preceding that valid frame, then that pedestrian is considered a newly appearing pedestrian in that valid frame (i.e., a pedestrian entering the statistical area); if a pedestrian appears in the statistical area of an adjacent valid frame preceding that valid frame and has not appeared in the statistical area in that valid frame and the N valid frames following that valid frame, then that pedestrian is considered a pedestrian leaving the valid frame (i.e., a pedestrian leaving the statistical area).;
not counting the at least one pedestrian at later image data acquisition moment when the coordinates are located in the same grid (see at least [0038] Step 230: Use a pedestrian re-identification algorithm to identify pedestrians in the current valid frame who are the same as those in at least one previous valid frame. [0039] When a pedestrian passes through an open scene, they will be captured in multiple valid frames. When performing pedestrian flow statistics, it is necessary to find the same pedestrians in each valid frame to avoid counting the same person multiple times in order to obtain accurate data. [0040] Person Re-identification (Person ReID) uses computer vision technology to determine whether a specific person exists in an image, and can be used for tracking people within the same camera or across cameras. In the embodiments of this specification, a pedestrian re-identification algorithm is used to determine which pedestrians detected in the current valid frame are those that have appeared in previous valid frames and which are new pedestrians that appear in the current valid frame.”);
counting the at least one pedestrian at later image data acquisition moment when the coordinates are located in different grids (see at least [0047] The first example is to accumulate the number of new pedestrians appearing in each valid frame within the statistical time period and use the accumulated result as the number of people in the statistical time period. If a pedestrian in the current valid frame is different from the pedestrians in the previous N valid frames, then the pedestrian is a newly appearing pedestrian in the current valid frame; the total number of newly appearing pedestrians in all valid frames within the statistical time period can be regarded as the total number of pedestrians in the statistical time period.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of a thermodynamic diagram generation of Heran and Zexu with marking and tracking the at least one pedestrian in the scene as seen in Xiaobo because this modification would t avoid counting the same person multiple times in order to obtain accurate data ([0039] of Xiaobo)
Thus, the combination of Heran, Zexu and Xiaobo teaches wherein the counting the number of the at least one pedestrian in each grid of multiple grids further comprises: marking and tracking the at least one pedestrian in the scene; and judging whether the coordinates of the tracked pedestrian obtained at two adjacent image data acquisition moments of the image data are located in a same grid; not counting the at least one pedestrian at later image data acquisition moment when the coordinates are located in the same grid; counting the at least one pedestrian at later image data acquisition moment when the coordinates are located in different grids.
3. Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Heran et al, IDS, CN113468250A (English translated) (“Heran”) in view of Zexu et al, IDS, CN109189878 (English translated) (“Zexu”) further in view of KAWABAYASHI, U.S Patent Application Publication No.20210148707 (“KAWABAYASHI”)
Regarding claim 8, Heran and Zexu teach the method according to claim 4, wherein the obtaining image data containing pedestrians and determine coordinates of at least one pedestrian in the scene further comprises: extracting first coordinates of the at least one pedestrian from the image data (see at least [n0168]-[n0171] of Heran“..The data reported by the subsequently selected camera equipment (including human body data and/or facial data). The reported data can be at least one of the coordinates of a human body bounding box and the coordinates of a face bounding box with a fixed time interval. The time interval can be configured, with a default of 1000ms. The coordinates of the human body bounding box and the face bounding box, along with the coordinate sequence number, are reported to the heat map generation device. The heat map generation device can calculate the timestamp of each coordinate based on at least one of the following: the time information of when each human body first appears, the time interval, and the coordinate sequence number… [n0171]The human body information of each person may include at least one of the following: the position information of the human body bounding box of each person in the image, the position information of the feature points (e.g., at least one of the four vertices) on the human body bounding box of each person in the image, and the position information of the feature points (e.g., at least one of the head feature points, arm feature points, and leg feature points) of each person in the image. In other embodiments, the human body information of each person may also include at least one of the following: height information, gender information, body shape information, clothing style information, and occupation information. In this embodiment of the disclosure, the location information may include coordinate information, which may be two-dimensional coordinate information”; [0052] of Zexu “ Specifically, pedestrians on the crowd heat map can be enclosed in boxes, and the midpoint of the bottom of the box corresponding to each pedestrian can be obtained. This midpoint can then be used as the coordinate value of the pedestrian. Specifically, the length and width of each crowd heat map can be standardized to 0 to 1. For example, if the length of the crowd heat map is 20cm and the width is 18cm, and the position of the bottom midpoint of the box of a certain pedestrian in the map is 9cm long and 8cm wide, then the corresponding coordinate value is (9/20, 8/18), that is, (0.45, 0.44). Step S130: Based on the edge data intervals where the coordinate values fall within a preset number of grids, obtain the coordinates of the people who have stayed in each of the preset number of grids and the number of people). In addition, the same motivation is used as the rejection for claim 3. Heran and Zexu are understood to be silent on the remaining limitations of claim 8.
In the same field of endeavor, KAWABAYASHI teaches extracting first coordinates of the at least one pedestrian from the image data based on a coordinate system of an image acquisition device of the image data and transforming the first coordinates to a second coordinates based on a coordinate system of the scene, and using the second coordinates as coordinates of the at least one pedestrian in the scene (see at least [0051] The first lateral location calculator 31a then calculates the location of the vehicle 10 at the first information acquisition time at which the first image has been acquired, based on the first image and the map information, as an example of locational relationship information (step S605). First, the first lateral location calculator 31a carries out viewpoint conversion processing on the coordinates of objects represented in the first image, using information such as the installation location information and internal parameters of the first camera 2, representing the result in a camera coordinate system with the location of the first camera 2 as the origin. The coordinates of an object represented in the first image can be represented in an image coordinate system with an Xi axis extending to the right and a Yi axis extending downward, using the location at the top left corner of the first image as the origin. In the camera coordinate system, using the center of the imaging surface as the origin, a Zc axis is set in the traveling direction of the vehicle 10, an Xc axis is set in a direction perpendicular to the Zc axis and parallel to the ground, and a Yc axis is set in the vertical direction, the origin being at a height from the ground that is the height where the first camera 2 is installed. The first lateral location calculator 31a then takes the coordinates of objects shown in the first image which have been represented in the camera coordinate system, and represents them in a world coordinate system. In the world coordinate system, an Xw axis and Zw axis are set within a plane parallel to the ground and a Yw axis is set in the vertical direction, with a predetermined reference location in real space as the origin. The first lateral location calculator 31a estimates the location and traveling direction of the vehicle 10 at the first information acquisition time, based on the location of the vehicle 10 at a positioning time that is before the first information acquisition time and nearest to the first information acquisition time, and the amount of movement and moving direction of the vehicle 10 between that positioning time and the first information acquisition time. For the location and traveling direction of the vehicle 10 at the first information acquisition time, the first lateral location calculator 31a can use the value determined when calculating the reference location of the vehicle 10. The first lateral location calculator 31a estimates an assumed location and assumed posture of the first camera 2 at the first information acquisition time, using the installation location information and internal parameters of the first camera 2, and the estimated location and moving direction of the vehicle 10. The first lateral location calculator 31a devrives a conversion formula from the camera coordinate system to the world coordinate system, according to the assumed location and assumed posture of the first camera 2 at the first information acquisition time. The conversion formula is represented as a combination between a rotation matrix representing rotation within the coordinate system and a translation vector representing parallel movement within the coordinate system. The first lateral location calculator 31a then takes the coordinates of objects shown in the first image which have been represented in the camera coordinate system, and converts them to coordinates represented by the world coordinate system, according to the aforementioned conversion formula.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of a thermodynamic diagram generation of Heran and Zexu with transforming the camera coordinate system to the world coordinate system as seen in KAWABAYASHI because this modification would take the coordinates of objects shown in the first image which have been represented in the camera coordinate system, and converts them to coordinates represented by the world coordinate system ([0051] of KAWABAYASHI).
Thus, the combination of Heran, Zexu and KAWABAYASHI teaches wherein the obtaining image data containing pedestrians and determine coordinates of at least one pedestrian in the scene further comprises: extracting first coordinates of the at least one pedestrian from the image data based on a coordinate system of an image acquisition device of the image data; and transforming the first coordinates to a second coordinates based on a coordinate system of the scene, and using the second coordinates as coordinates of the at least one pedestrian in the scene.
Regarding claim 17, Heran, Zexu and KAWABAYASHI teach the method according to claim 8,wherein the transforming the first coordinates to the second coordinates based on the coordinate system of the scene comprises: transforming, by rotating coordinates, the first coordinates based on the coordinate system of the image acquisition device of the image data to the second coordinates of the coordinate system of the scene (see at least [n0168]-[n0171] of Heran; [00052] of Zexu; [0051] of KAWABAYASHI The first lateral location calculator 31a then calculates the location of the vehicle 10 at the first information acquisition time at which the first image has been acquired, based on the first image and the map information, as an example of locational relationship information (step S605). First, the first lateral location calculator 31a carries out viewpoint conversion processing on the coordinates of objects represented in the first image, using information such as the installation location information and internal parameters of the first camera 2, representing the result in a camera coordinate system with the location of the first camera 2 as the origin. The coordinates of an object represented in the first image can be represented in an image coordinate system with an Xi axis extending to the right and a Yi axis extending downward, using the location at the top left corner of the first image as the origin. In the camera coordinate system, using the center of the imaging surface as the origin, a Zc axis is set in the traveling direction of the vehicle 10, an Xc axis is set in a direction perpendicular to the Zc axis and parallel to the ground, and a Yc axis is set in the vertical direction, the origin being at a height from the ground that is the height where the first camera 2 is installed. The first lateral location calculator 31a then takes the coordinates of objects shown in the first image which have been represented in the camera coordinate system, and represents them in a world coordinate system. In the world coordinate system, an Xw axis and Zw axis are set within a plane parallel to the ground and a Yw axis is set in the vertical direction, with a predetermined reference location in real space as the origin. The first lateral location calculator 31a estimates the location and traveling direction of the vehicle 10 at the first information acquisition time, based on the location of the vehicle 10 at a positioning time that is before the first information acquisition time and nearest to the first information acquisition time, and the amount of movement and moving direction of the vehicle 10 between that positioning time and the first information acquisition time. For the location and traveling direction of the vehicle 10 at the first information acquisition time, the first lateral location calculator 31a can use the value determined when calculating the reference location of the vehicle 10. The first lateral location calculator 31a estimates an assumed location and assumed posture of the first camera 2 at the first information acquisition time, using the installation location information and internal parameters of the first camera 2, and the estimated location and moving direction of the vehicle 10. The first lateral location calculator 31a devrives a conversion formula from the camera coordinate system to the world coordinate system, according to the assumed location and assumed posture of the first camera 2 at the first information acquisition time. The conversion formula is represented as a combination between a rotation matrix representing rotation within the coordinate system and a translation vector representing parallel movement within the coordinate system. The first lateral location calculator 31a then takes the coordinates of objects shown in the first image which have been represented in the camera coordinate system, and converts them to coordinates represented by the world coordinate system, according to the aforementioned conversion formula.”) In addition, the same motivation is used as the rejection for claim 8.
4. Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Heran et al, IDS, CN113468250A (English translated) (“Heran”) in view of Noest et al., U.S Patent Application Publication No.2021/0264132 (“Noest”)
Regarding claim 19, Heran teaches the system according to claim 18, wherein the at least one image acquisition device comprises one image acquisition device, the image acquisition device is greater than a height of the at least one pedestrian ([n0067] At least one frame can be a continuously captured image or a non-continuously captured image. For example, a camera device can take an image at set intervals, and the human body information of each person obtained by the heat map generation device is determined from at least one frame of image taken at set intervals within the target time period. In other implementations, the camera device can capture an image at set intervals. Then, the camera device analyzes whether the similarity between the captured image and the previous image (i.e., the previously captured image) is less than a threshold. If it is less than a threshold, it indicates that the difference between the captured image and the previous image is relatively large. The camera device analyzes the captured image to obtain the human body information of each person in the captured image. If it is greater than or equal to the threshold, the camera device will capture the next image without analyzing the human body information of the captured image, until it is determined that the similarity between a subsequently captured image and the previous image is less than a threshold. The camera device analyzes the image to obtain the human body information of each person in the image, thereby obtaining the human body information of each person in the images obtained within the target time period.”) Heran is understood to be silent on the remaining limitations of claim 19.
In the same field of endeavor, Noest teaches wherein the at least one image acquisition device comprises one image acquisition device , a height of the image acquisition device is greater than a height of the at least one pedestrian, and the image acquisition device is located at a center of the scene ([0035] The 3D object recognition system that is illustrated in FIGS. 1 to 3 is intended to recognise human faces at or adjacent an entrance 2 to a building 1. An array of five cameras 11-15 is arranged in an arc such that all of the cameras are directed generally towards the building entrance 2. The cameras 11-15 are suspended from a roof 4 of the building 1, such that their apertures (or lenses) are at a height of about 2.5 m above the level of a floor 3. Thus, all of the cameras 11-15 face generally towards people 5 entering the building 1 and slightly downwardly. If the cameras 11-15 are located above an area of the floor 3 where people 5 would not normally walk, the camera apertures may be at a somewhat lower level—e.g. about 2 m above the level of floor 3. The camera apertures are preferably no higher than 5 m above the level of floor 3. In this example, all cameras 11-15 are at the same height. However, they could be at differing heights, which could be helpful in viewing subjects of different heights and or with various tilts of their faces. The cameras 11-15 may be spaced at least 0.5 m apart from one another, measured horizontally—i.e. the horizontal component of their mutual spacing. ; [0040] If a person 5 enters the building 1 in approximately the middle of the entrance 2, walking forward and looking ahead, the centre camera 13 will capture an image of the person's face that is substantially a frontal view. This is usually the ideal view to facilitate facial recognition. The leftmost (as seen) camera 11 captures a left side view of the face and the intermediate camera 12 captures a view between frontal and left side, which we conveniently refer to here as a left three-quarter view. (It will be appreciated that a ‘side view’ may not be an exact side view and a ‘three-quarter view’ may not be an exact three-quarter view, depending upon the respective positions of the person 5 with respect to the cameras 11-15.) The rightmost (as seen) camera 15 captures a right side view of the face and the intermediate camera 14 captures a right three-quarter view. The respective facial views as captured by the cameras 11-15 are illustrated diagrammatically in FIG. 1.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of a thermodynamic diagram generation of Heran with including center camera which apertures are preferably no higher than 5 m above the level of floor as seen in Noest because this modification would recognize human faces at or adjacent an entrance to a building ([0035] of Noest).
Thus, the combination of Heran and Noest teaches wherein the at least one image acquisition device comprises one image acquisition device , a height of the image acquisition device is greater than a height of the at least one pedestrian, and the image acquisition device is located at a center of the scene.
Regarding claim 20, Heran teaches the system according to claim 18, wherein the at least one image acquisition device comprises at least two image acquisition devices, and the at least two image acquisition devices have an image acquisition area covering an entire scene([n0150] For example, the pictures and images can both be taken by one or at least two cameras from a fixed position and perspective, or the pictures and images can both be stitched together or merged from at least two images taken by at least two cameras from a fixed position and perspective.”) Heran is understood to be silent on the remaining limitations of claim 19.
In the same field of endeavor, Noest teaches wherein the at least one image acquisition device comprises at least two image acquisition devices , heights of at least two image acquisition devices are greater than a height of the at least one pedestrian ([0035] The 3D object recognition system that is illustrated in FIGS. 1 to 3 is intended to recognise human faces at or adjacent an entrance 2 to a building 1. An array of five cameras 11-15 is arranged in an arc such that all of the cameras are directed generally towards the building entrance 2. The cameras 11-15 are suspended from a roof 4 of the building 1, such that their apertures (or lenses) are at a height of about 2.5 m above the level of a floor 3. Thus, all of the cameras 11-15 face generally towards people 5 entering the building 1 and slightly downwardly. If the cameras 11-15 are located above an area of the floor 3 where people 5 would not normally walk, the camera apertures may be at a somewhat lower level—e.g. about 2 m above the level of floor 3. The camera apertures are preferably no higher than 5 m above the level of floor 3. In this example, all cameras 11-15 are at the same height. However, they could be at differing heights, which could be helpful in viewing subjects of different heights and or with various tilts of their faces. The cameras 11-15 may be spaced at least 0.5 m apart from one another, measured horizontally—i.e. the horizontal component of their mutual spacing. ; [0040] If a person 5 enters the building 1 in approximately the middle of the entrance 2, walking forward and looking ahead, the centre camera 13 will capture an image of the person's face that is substantially a frontal view. This is usually the ideal view to facilitate facial recognition. The leftmost (as seen) camera 11 captures a left side view of the face and the intermediate camera 12 captures a view between frontal and left side, which we conveniently refer to here as a left three-quarter view. (It will be appreciated that a ‘side view’ may not be an exact side view and a ‘three-quarter view’ may not be an exact three-quarter view, depending upon the respective positions of the person 5 with respect to the cameras 11-15.) The rightmost (as seen) camera 15 captures a right side view of the face and the intermediate camera 14 captures a right three-quarter view. The respective facial views as captured by the cameras 11-15 are illustrated diagrammatically in FIG. 1.”) and the at least two image acquisition devices have an image acquisition area covering an entire scene ([0059] As an alternative (or addition, depending upon circumstances) to simple selection of images from the processors 34, images from two or more of the cameras 11-15 may be combined and processed to synthesise a canonical viewpoint image from the different viewpoints of the respective cameras, such that the reconstructed canonical viewpoint matches the viewpoint of the gallery images. For example, suppose that the face of a subject is pointing towards central camera 13, but is obscured from that camera by another person, and yet the two cameras either side of this, cameras 12 and 14, pick up partial views of the left and right sides of the face. These two partial views can then be processed to reconstruct the canonical (e.g. frontal) view, which is then passed to the recogniser 43 to match against the stored images in the gallery 44, with the same canonical viewpoint. Techniques for synthesising one view from other views are known in the art.”) In addition, the same motivation is used as the rejection for claim 19.
Thus, the combination of Heran and Noest teaches wherein the at least one image acquisition device comprises at least two image acquisition devices, heights of at least two image acquisition devices are greater than a height of the at least one pedestrian, and the at least two image acquisition devices have an image acquisition area covering an entire scene.

Allowable Subject Matter
Claims 9-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten to overcome the rejections under 35 USC § 101; 35 USC § 112(b) set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Regarding claim 9, the prior art of record taken alone or in combination, fails to disclose or render obvious: “obtaining a correction value of the original coordinates through a following formula:

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50
136
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wherein, H represents a height of the image acquisition device of the image data, x2 represents center coordinates of the scene where the image acquisition device of the image data is located, xl represents the original coordinates obtained by the image acquisition device, h represents a height of the at least one pedestrian, and x represents the correction value of the original coordinates; and correcting the original coordinates by using the correction value of the original coordinates based on a quadrant of the coordinate system of the image acquisition device where the original coordinates are located, to obtain the first coordinates.”
Claims 10-16 are objected based on objection of claim 9.

Contact

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/SARAH LE/Primary Examiner, Art Unit 2614

Prosecution Timeline

• Apr 18, 2024: Application Filed
• Mar 30, 2026: Non-Final Rejection mailed — §101, §102, §103 (current)