DETAILED ACTION
Claims 1-17 and 19-37 are presented for examination.
This Office Action is in response to submission of documents on November 18, 2025.
Objection to the Drawings for including Figures not disclosed in the Specification is withdrawn.
Objection to the Specification for non-compliance with format requirements is withdrawn.
Rejections of claims 1-37 under 35 U.S.C. 112(b) as being indefinite is withdrawn.
Rejection of claims 1-37 under 35 U.S.C. 101 as being directed to unpatentable subject matter is maintained.
Rejection of claims 1-4, 6, 9-10, 18-21, 24-27, 29-30, and 32-37 under 35 U.S.C. 102(a)(2) as being anticipated by Atrazhev is withdrawn.
Rejection of claims 5, 7-8, 11-12, 15-16, and 31 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Fadell is withdrawn.
Rejection of claim 13 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Hollar is withdrawn.
Rejection of claim 14 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Blanch is withdrawn.
Rejection of claims 17 and 28 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Felemban is withdrawn.
Rejection of claim 22 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Blaiotta is withdrawn.
Rejection of claim 23 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Sharma is withdrawn.
Rejection of claims 1-4, 6, 9-10, 19-21, 24-27, 29-30, and 32-37 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen and Shi-Nash.
Rejection of claims 5, 7-8, 11-12, 15-16, and 31 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Fadell.
Rejection of claim 13 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Hollar.
Rejection of claim 14 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Blanch.
Rejection of claims 17 and 28 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Felemban.
Rejection of claim 22 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Blaiotta.
Rejection of claim 23 under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Sharma.
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 .
Response to Arguments
Regarding the objections to the Drawings, Specification, and claims Examiner is persuaded by the amendments and arguments provided with this response. Accordingly, the objections are withdrawn.
Regarding rejection of the claims under 35 U.S.C. 112(b), Examiner is persuaded by the arguments and amendments. Accordingly, the rejection is withdrawn.
Regarding rejection of the claims under 35 U.S.C. 101, Examiner is not persuaded by the presented amendments and argument for the following reasons:
Applicant’s arguments regarding disclosure in the Specification (see Response at pp. 19-20-21) are not persuasive because the claims do not include a number of functional aspects that are disclosed in the cited portions of the Specification. For example, asynchronous reporting times of sensors, “conversations” between sensors, interpolating missing data points, and/or other details that, while may “exceed human cognitive capabilities,” are not recited in the claims and/or are recited with generality that would not inform a person having ordinary skill in the art as to how the invention operates.
Claim 1, as amended, encompasses instances that can be performed by a human. As an example, for the limitation “wherein the sensor data spans a plurality of time steps,” a human reviewing a video feed every 5 minutes would be considered a plurality of times that sensor data (i.e., data from a video camera) is reviewed. In instances such as this, a human would be able to “practically track and process the sensor readings.” Further, multiple video feeds can be “temporally aligned” and be utilized to track movements of people in a location, even if some data is missing between viewings (i.e., “interpolation”).
Further, “generation of a graph representing a space, with graph nodes representing locations and links representing movement capabilities” can include, for example, a human tracking the movement of a human, as observed on a video screen, and recording the movements on a map. Nothing in the claim explicitly requires “movement probabilities” to be tracked, “link weights based on real-time sensor inputs,” and/or “varying temporal characteristics,” as argued.
None of the dependent claims add additional elements that integrate the recited judicial exceptions into a practical application. Instead, the claims add specificity to the judicial exceptions which do not impart patentability on a claim that includes abstract ideas. “[I]t is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.” MPEP 2106.05(a).II.
Thus, Examiner finds that the claims, as presented, do not include additional elements that integrate the recited judicial exceptions into a practical application and further do not recite improvements in a technology or technological field. The Specification does list a number of applications where the claimed invention is practically useful, but none of these applications are present in the claims. See Specification at pp. 2-4. While it can be appreciated that the invention may be implemented to carry out the disclosed embodiments, the claims do not reflect the embodiments. Instead, the claim recites the general concept of “simulating occupancy of the particular space” without details as to how the occupancy is determined nor what is included in “evaluat[ing] a subset of the plurality of subsequent states.”
Regarding rejection of claims under 35 U.S.C. 102(a)(2) as being anticipated by Atrazhev and rejection of claims under 35 U.S.C. 103 as being obvious over Atrazhev in view of additional prior art, Examiner agrees that Atrazhev, alone or in combination with the other references does not near nor disclose at least “wherein the plurality of sensors includes at least two different sensor types reporting data at different temporal intervals, wherein the sensor data spans a plurality of time steps” and “a graph representing the particular space, with graph nodes representing locations in the particular space and links between them representing the ability for people to move from one location to another in the particular space,” as recited by the amended independent claims. Accordingly, rejection of the independent claims under 35 U.S.C. 102(a)(1) is withdrawn. However, as a result of the amendments to the claims, the independent claims are rejected under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen (“A robust and modular multi-sensor fusion approach applied to MAV navigation”) and Shi-Nash, et al. (U.S. Pat. Pub. No. 2017/0195854, hereinafter “Shi-Nash”).
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 1-37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exceptions without significantly more. The claims recite mathematical calculations and/or mental processes. This judicial exception is not integrated into a practical application because the additional elements that are recited in the claims are extra-solution activities that do not integrate the judicial exceptions into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because courts have found that the steps of data gathering, recitations of generic computer components, and ideas of solutions without specific details are not significantly more than a judicial exception.
Claim 1
Step 1: The claim is directed to a process, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 1 limitations include (bolded for abstract idea identification):
Claim 1
Mapping Under Step 2A Prong 1
1. A method performed by at least one computer processor executing computer program instructions stored in at least one non-transitory computer-readable medium, the method comprising:
(A) applying a plurality of sensors within the space that generate sensor data associated with a first plurality of times and spaces, wherein the plurality of sensors includes at least two different sensor types reporting data at different temporal intervals, wherein the sensor data spans a plurality of time steps;
(B) generating a flow model based on a digital representation of a particular space, wherein the flow model includes traversability patterns representing a flow of people into, out of, and within the particular space over time, wherein generating the flow model in (B) comprises generating a graph representing the particular space, with graph nodes representing locations in the particular space and links between them representing the ability for people to move from one location to another in the particular space;
(C) generating a set of initial states and selecting the set of initial states as a current set of states;
(D) simulating occupancy of the particular space, starting from the current set of states and using the flow model, to produce a plurality of subsequent states, wherein each of the plurality of subsequent states indicates a corresponding estimation of occupancy within the particular space; and
(E) using the sensor data to evaluate a subset of the plurality of subsequent states, wherein the estimations of occupancy corresponding to the subset of the plurality of subsequent states are associated with a second plurality of times and spaces that are not within the first plurality of times and spaces associated with the sensor data in step (A), thereby identifying highly- evaluated states within the plurality of subsequent states.
Abstract Idea: Mathematical Concepts
Constructing a model to predict the behavior of people is a mathematical concept that includes performing one or more mathematical operations according to functions that describe the system. See MPEP 2106.04(a)(2), Subsection I.
Abstract Idea: Mental Process
The limitation can include observing the locations of one or more people and selecting those locations as the initial states of the system. See e.g., MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mathematical Concepts
Using a model and/or simulation includes performing one or more operations according to functions that describe the system. See MPEP 2106.04(a)(2), Subsection I.
Abstract Idea: Mental Process
Evaluation of data is a mental process that can be performed by a human using pencil and paper, and includes evaluation of the data and judgment to identify states of interest. See e.g., MPEP 2106.04(a)(2), Subsection III.
Step 2A, Prong 2: The claim 1 limitations recite (bolded for additional element identification):
Claim 1
Mapping Under Step 2A Prong 2
1. A performed by at least one computer processor executing computer program instructions stored in at least one non-transitory computer-readable medium, the method comprising:
(A) applying a plurality of sensors within the space that generate sensor data associated with a first plurality of times and spaces, wherein the plurality of sensors includes at least two different sensor types reporting data at different temporal intervals, wherein the sensor data spans a plurality of time steps;
(B) generating a flow model based on a digital representation of a particular space, wherein the flow model includes traversability patterns representing a flow of people into, out of, and within the particular space over time, wherein generating the flow model in (B) comprises generating a graph representing the particular space, with graph nodes representing locations in the particular space and links between them representing the ability for people to move from one location to another in the particular space;
(C) generating a set of initial states and selecting the set of initial states as a current set of states;
(D) simulating occupancy of the particular space, starting from the current set of states and using the flow model, to produce a plurality of subsequent states, wherein each of the plurality of subsequent states indicates a corresponding estimation of occupancy within the particular space; and
(E) using the sensor data to evaluate a subset of the plurality of subsequent states, wherein the estimations of occupancy corresponding to the subset of the plurality of subsequent states are associated with a second plurality of times and spaces that are not within the first plurality of times and spaces associated with the sensor data in step (A), thereby identifying highly- evaluated states within the plurality of subsequent states.
Reciting generic computer components is the additional element of instructions to apply the recited judicial exception, which courts have found does not integrate the judicial exception into a practical application. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016).
“Applying” sensors can include utilizing sensors to perform one or more sensing functions, such as collecting sensor data. Accordingly, the limitation is directed to data gathering and transmittal, which is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is provided and therefore does not improve the functioning of a computer. See MPEP 2106.05(d)(II).
Step 2B: Regarding Step 2B, the inquiry is whether any of the additional elements (i.e., the elements that are not the judicial exception) amount to significantly more than the recited judicial exception. Besides the judicial exceptions, the claims recite “generating a representation” from data, which is a well-understood, routine, and conventional activity. See e.g., Atrazhev (U.S. Patent Pub. No. 2011/0007944) at [0032]: “Near future occupancy estimates may also be useful to first responders to provide data regarding not only the present location of occupants but the likely future location of building occupants.” Further, the claim recite limitations that courts have found to be insignificantly more than the judicial exception; namely, generic computer components (See e.g., Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), and data gathering and transmission (See e.g., Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Accordingly, claim 1 is rejected for being directed to unpatentable subject matter.
Claim 2
Claim 2 recites (F) providing the highly-evaluated states identified by (E) as a current set of states to (D), repeating (D) and (E), and outputting the highly-evaluated states produced by the most recent iteration of (E). Providing and outputting data are extra-solution activities of data transmission, which courts have found to be insignificant extra-solution activity that does not integrate the exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 2 is not directed to patentable subject matter.
Claim 3
Claim 3 recites wherein (A) is performed before (B)-(E), and wherein (B)-(E) are applied to the sensor data generated in (A) and not to any additional sensor data. The claim merely indicates an order of steps that have been rejected under 35 U.S.C. 101 in reference to claim 1. The claim does not include any other additional elements. Accordingly, claim 3 is not directed to patentable subject matter.
Claim 4
Claim 4 recites wherein the plurality of sensors includes at least one video camera, and wherein the sensor data includes: (1) a plurality of images output by the at least one video cameras and (2) locations of detected people in the plurality of images, as generated by analytics run on the images. As indicated with respect to claim 1, “applying” a sensor can include utilizing an element to gather data. The limitation of claim 3 merely specifies a type of sensor and data that is gathered by the sensor. “Analytics run on the images” to determine a location of objects in the images is a mathematical concept that requires executing one or more functions to analyze images captured by a video camera. See MPEP 2106.04(a)(2), Subsection I. Accordingly, claim 4 is not directed to patentable subject matter.
Claim 5
Claim 5 recites wherein the plurality of sensors further includes at least one Wi-Fi-sensing sensor, and wherein (E) comprises using the plurality of images output by the plurality of video cameras, the locations of detected people in the plurality of images, and Wi-Fi signal data from the Wi-Fi-sensing sensors to evaluate the subset of the plurality of subsequent states. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 5 is not directed to patentable subject matter.
Claim 6
Claim 6 recites wherein the plurality of sensors further includes at least one RFID sensor, and wherein (E) comprises using the plurality of images output by the plurality of video cameras, the locations of detected people in the plurality of images, and tag detection from the at least one RFID sensor to evaluate the subset of the plurality of subsequent states. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 6 is not directed to patentable subject matter.
Claim 7
Claim 7 recites wherein the plurality of sensors includes at least one Wi-Fi access point, and wherein the sensor data includes data about communication of at least one device with the at least one Wi-Fi access point. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 7 is not directed to patentable subject matter.
Claim 8
Claim 8 recites wherein the plurality of sensors includes at least one Wi-Fi sensor, and wherein the sensor data includes data about Wi-Fi signals detected by the at least one Wi-Fi sensor. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 8 is not directed to patentable subject matter.
Claim 9
Claim 9 recites wherein the plurality of sensors includes at least one RFID sensor, and wherein the sensor data includes data about nearby RFID tags generated by the at least one RFID sensor. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 9 is not directed to patentable subject matter.
Claim 10
Claim 10 recites wherein the plurality of sensors includes at least one motion sensor, and wherein the sensor data includes motion detection events generated by the at least one motion sensor. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 10 is not directed to patentable subject matter.
Claim 11
Claim 11 recites wherein the plurality of sensors includes at least one carbon dioxide sensor, and wherein the sensor data includes carbon dioxide readings generated by the at least one carbon dioxide sensor. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 11 is not directed to patentable subject matter.
Claim 12
Claim 12 recites wherein the plurality of sensors includes at least one sound sensor, and wherein the sensor data includes sound level readings generated by the at least one sound sensor. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 12 is not directed to patentable subject matter.
Claim 13
Claim 13 recites wherein the plurality of sensors includes at least one ultra-wideband sensor, and wherein the sensor data includes object detection data generated by the at least one ultra-wideband sensor. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 13 is not directed to patentable subject matter.
Claim 14
Claim 14 recites wherein the plurality of sensors includes at least one access control badge sensor, and wherein the sensor data includes badge detection events generated by the at least one badge detection sensor. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 14 is not directed to patentable subject matter.
Claim 15
Claim 15 recites wherein (E) comprises using the sensor data and data about events that are planned in the particular space to evaluate the subset of the plurality of subsequent states. The claim merely includes additional data that is utilized to perform the evaluation (a mental process; see claim 1). Transmitting and receiving additional data is an extra-solution activity that courts have found does not integrate the exception into a practical application and further is not significantly more than the recited judicial exception. Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 15 is not directed to patentable subject matter.
Claim 16
Claim 16 recites wherein (E) comprises using the sensor data and predictions about weather in the particular space's location to evaluate the subset of the plurality of subsequent states. Claim 15 recites wherein (E) comprises using the sensor data and data about events that are planned in the particular space to evaluate the subset of the plurality of subsequent states. The claim merely includes additional data that is utilized to perform the evaluation (a mental process; see claim 1). Transmitting and receiving additional data is an extra-solution activity that courts have found does not integrate the exception into a practical application and further is not significantly more than the recited judicial exception. Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 16 is not directed to patentable subject matter.
Claim 17
Claim 17 recites using the sensor data and public transportation schedules governing arrivals to and departures to locations near the particular space to evaluate the subset of the plurality of subsequent states. The claim merely includes additional data that is utilized to perform the evaluation (a mental process; see claim 1). Transmitting and receiving additional data is an extra-solution activity that courts have found does not integrate the exception into a practical application and further is not significantly more than the recited judicial exception. Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 17 is not directed to patentable subject matter.
Claim 19
Claim 19 recites wherein the flow model comprises a uniform representation of the particular space that locates data from sensors with different spatial coverage and sensing capabilities to be fused in the evaluation in (E). The claim includes limitations that merely further specify a flow model to be utilized to model human movement through a location. The claim does not include any additional elements and therefore does not integrate the claim into a practical application nor amount to significantly more than the judicial exception. Accordingly, claim 19 is not directed to patentable subject matter.
Claim 20
Claim 20 recites wherein the flow model comprises a sensor-agnostic representation of the particular space, enabling the form of the highly-evaluated states produced by the most recent iteration of (E) to be independent of limitations of individual ones of the plurality of sensors. The claim includes limitations that merely further specify a flow model to be utilized to model human movement through a location. The claim does not include any additional elements and therefore does not integrate the claim into a practical application nor amount to significantly more than the judicial exception. Accordingly, claim 20 is not directed to patentable subject matter.
Claim 21
Claim 21 recites wherein the simulating in (D) is informed by prior knowledge about qualities of the particular space and similarities of the particular space to previously analyzed spaces. The claim includes limitations that merely further specify how the simulation is initiated and/or executed. The claim does not include any additional elements and therefore does not integrate the claim into a practical application nor amount to significantly more than the judicial exception. Accordingly, claim 21 is not directed to patentable subject matter.
Claim 22
Claim 22 recites wherein the simulating in (D) comprises simulating the occupancy of the space using Sequential Monte Carlo simulation. The claim includes limitations that merely further specify a type of algorithm to be utilized to model human movement through a location. The claim does not include any additional elements and therefore does not integrate the claim into a practical application nor amount to significantly more than the judicial exception. Accordingly, claim 22 is not directed to patentable subject matter.
Claim 23
Claim 23 recites wherein the simulating in (D) is informed by hyperparameters derived from offline simulations that model individual, artificially intelligent agents that move within the particular space. The claim includes limitations that merely further specify how the simulation is initiated and/or executed. Further, the claim recites utilizing additional simulations, which are directed to mathematical concepts. See MPEP 2106.04(a)(2), Subsection I. The claim does not include any additional elements and therefore does not integrate the claim into a practical application nor amount to significantly more than the judicial exception. Accordingly, claim 23 is not directed to patentable subject matter.
Claim 24
Claim 24 recites wherein simulating in (D) is informed by hyperparameters modeling human-observed ground truth about the occupancy of the particular space being simulated. Human-observed ground truth is a mental process that includes observation and evaluation. See MPEP 2106.04(a)(2), Subsection III. Further, the claim includes limitations that merely further specify how the simulation is initiated and/or executed. The claim does not include any additional elements and therefore does not integrate the claim into a practical application nor amount to significantly more than the judicial exception. Accordingly, claim 24 is not directed to patentable subject matter.
Claim 25
Claim 25 recites wherein producing the plurality of subsequent states comprises applying a probability distribution describing likely movements of people between locations in the particular space. Applying a “probability distribution” is a mathematical concept because the limitation utilizes mathematical calculations to determine the likely movements of people. See MPEP 2106.04(a)(2), Subsection I. Accordingly, claim 25 is not directed to patentable subject matter.
Claim 26
Claim 26 recites wherein the probability distribution incorporates prior knowledge about how people enter and leave the particular space. Prior knowledge includes gathering of data from one or more sources, which is an extra-solution activity that does not integrate the judicial exception into a practical application and does not amount to significantly more than the recited judicial exception. Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 26 is not directed to patentable subject matter.
Claim 27
Claim 27 recites generating the probability distribution based on prior knowledge about maximum flow rates that are possible between areas in the particular space. Prior knowledge includes gathering of data from one or more sources, which is an extra-solution activity that does not integrate the judicial exception into a practical application and does not amount to significantly more than the recited judicial exception. Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 27 is not directed to patentable subject matter.
Claim 28
Claim 28 recites generating the probability distribution based on prior knowledge about occupants' typical goals and preferred navigation paths in the particular space. Prior knowledge includes gathering of data from one or more sources, which is an extra-solution activity that does not integrate the judicial exception into a practical application and does not amount to significantly more than the recited judicial exception. Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 28 is not directed to patentable subject matter.
Claim 29
Claim 29 recites generating the probability distribution based on prior knowledge about how crowding affects occupants' possible movements in the particular space. Prior knowledge includes gathering of data from one or more sources, which is an extra-solution activity that does not integrate the judicial exception into a practical application and does not amount to significantly more than the recited judicial exception. Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 29 is not directed to patentable subject matter.
Claim 30
Claim 30 recites using the sensor data to evaluate the subset of the plurality of subsequent states comprises finding a joint probability distribution of the plurality of subsequent states and an observation probability distribution that summarizes the sensor data. Probability distributions are mathematical concepts (See MPEP 2106.04(a)(2), Subsection I) that are generated and/or constructed based on observing and evaluating events that are utilized as data to result in the distribution, which is a mental process (See MPEP 2106.04(a)(2), Subsection III).
Claim 31
Claim 31 recites using the sensor data to evaluate the subset of the plurality of subsequent states comprises combining information about individual occupants' likely locations in the particular space over time with anonymous data within the sensor data. Evaluation of data is a mental process that can be performed by a human using pencil and paper, and includes evaluation of the data and judgment to identify states of interest. See e.g., MPEP 2106.04(a)(2), Subsection III. Accordingly, claim 31 is not directed to patentable subject matter.
Claim 32
Claim 32 recites outputting the highly-evaluated states comprises generating, for each of the plurality of subsequent states, a probabilistic certainty of that state representing an actual state of the particular space's occupancy. Outputting data is the extra-solution activity of data transmission, which courts have found to be insignificant extra-solution activity that does not integrate the exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 32 is not directed to patentable subject matter.
Claim 33
Claim 33 recites outputting the highly-evaluated states comprises providing a human-interpretable representation of a likely actual occupancy of the particular space. Outputting data is the extra-solution activity of data transmission, which courts have found to be insignificant extra-solution activity that does not integrate the exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 33 is not directed to patentable subject matter.
Claim 34
Claim 34 recites outputting the highly-evaluated states comprises providing a representation of a likely actual occupancy of the particular space as soon as sufficient data is available. Outputting data is the extra-solution activity of data transmission, which courts have found to be insignificant extra-solution activity that does not integrate the exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 34 is not directed to patentable subject matter.
Claim 35
Claim 35 recites outputting the highly-evaluated states comprises providing a representation of a likely actual occupancy of the particular space during a particular time period retroactively, based on data that arrived before, during, and after the time period. . Outputting data is the extra-solution activity of data transmission, which courts have found to be insignificant extra-solution activity that does not integrate the exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 35 is not directed to patentable subject matter.
Claim 36
Claim 36 recites wherein the plurality of sensors includes at least one sensor that senses an area not physically located within the space being monitored, and wherein the sensor data includes data that indirectly indicates occupancy in the space. The claim merely specifies a type of sensor and data gathered by the sensor, which is an extra-solution activity courts have found does not amount to significantly more than the judicial exception and does not integrate the judicial exception into a practical application. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 36 is not directed to patentable subject matter.
Claim 37
Claim 37 recites a system comprising at least one non-transitory computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions are executable by at least one computer processor to perform a method for generating a representation of predicted occupancy in a particular space that includes steps substantially the same as the steps and limitations recited in claim 1. The claim recites generic computer components, which are additional elements that do not integrate the judicial exceptions into a practical application. See Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016). Accordingly, for at least the same reasons as provided for claim 1, claim 37 is not directed to patentable subject matter.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6, 9-10, 19-21, 24-27, 29-30, and 32-37 are rejected under 35 U.S.C. 103 as being obvious over Atrazhev, et al., (U.S. Patent Pub. No. 2011/0007944, hereinafter “Atrazhev”) in view of Lynen (“A robust and modular multi-sensor fusion approach applied to MAV navigation”) and Shi-Nash, et al. (U.S. Pat. Pub. No. 2017/0195854, hereinafter “Shi-Nash”).
Claim 1
Atrazhev discloses:
A method performed by at least one computer processor executing computer program instructions stored in at least one non-transitory computer-readable medium, the method comprising:
For example, although a computer system including a processor and memory was described for implementing the occupancy estimation algorithm, any number of suitable combinations of hardware and software may be employed for executing the mathematical functions employed by the occupancy estimation algorithm. Atrazhev at [0116].
(A) applying a plurality of sensors within the space that generate sensor data
Sensor data may be provided by a variety of different types of sensor devices, each providing a different type of sensor output that is analyzed to detect occupant movements or locations throughout an area or region. Atrazhev at [0022].
(B) generating a flow model based on a digital representation of a particular space, wherein the flow model includes traversability patterns representing a flow of people into, out of, and within the particular space over time,
The occupant traffic model seeks to predict how occupants will move throughout a region. In one embodiment, the occupant traffic model is a single-phase occupant traffic model that predicts the movement of occupants based on models of how occupants can flow from one region to another. In another embodiment, the occupant traffic model is a kinetic motion (KM)-based model that uses a two-phased approach to modeling how occupants move between adjacent regions. In particular, the two-phased approach takes into account how traffic congestion affects the movement of occupants through a region. Atrazhev at [0020].
The “traffic model” is analogous to the “flow model” which indicates how occupants move through a region.
(C) generating a set of initial states and selecting the set of initial states as a current set of states;
The initial state (e.g., initial occupancy estimates) to which the KM-based model would be applied (for instance, upon the issuance of a fire alarm signifying an egress mode of operation) may be modeled based on statistical occupancy data, simulated occupancy data or stored data regarding the location of occupants. For instance, statistical data may include defining initial occupant locations based on assignment of a simple distribution describing the likely location of occupants (e.g., Gaussian distribution). Atrazhev at [0091].
(D) simulating occupancy of the particular space, starting from the current set of states and using the flow model, to produce a plurality of subsequent states, wherein each of the plurality of subsequent states indicates a corresponding estimation of occupancy within the particular space; and
At step 32, occupant traffic model ƒ(t) is applied to the current occupancy estimate
x
^
(
t
|
t
)
to generate occupancy prediction or model-based occupancy estimate
x
^
(
t
+
1
|
t
)
. That is, the occupancy in the next state is predicted based on the current state estimate and the occupant traffic model ƒ(t). The notation
x
^
t
+
1
t
denotes that this is the state prediction for time t+1 based on observations made at time t (i.e., the update is not based on the most recently observed events). Atrazhev at [0043].
The state at “t+1” is a “subsequent state.”
(E) using the sensor data to evaluate a subset of the plurality of subsequent states, wherein the estimations of occupancy corresponding to the subset of the plurality of subsequent states are associated with a second plurality of times and spaces that are not within the first plurality of times and spaces associated with the sensor data in step (A), thereby identifying highly- evaluated states within the plurality of subsequent states.
At step 36, measurement prediction
z
^
t
+
1
t
is compared with actual sensor data z(t+1) to generate a difference signal represented by the innovation variable u(t+1). Atrazhev at [0044].
In an exemplary embodiment, innovation u(t+1) indicates the difference between expected sensor outputs (calculated at step 34) and the actual observed sensor outputs. Atrazhev at [0044].
The “actual observed outputs” is the “sensor data” and determining the “different between the expected sensor outputs and the actual observed sensor outputs” is an “evaluation.”
Atrazhev does not appear to teach:
(A) applying a plurality of sensors
(B)
Lynen, which is analogous to the prior art, discloses:
(A) applying a plurality of sensors that generate sensor data associated with a first plurality of times and spaces, wherein the plurality of sensors includes at least two different sensor types reporting data at different temporal intervals, wherein the sensor data spans a plurality of time steps;
In a typical scenario, inertial measurements arriving at rates of several 100 Hz to 2 kHz are fused with lower rate exteroceptive updates (∼5−90 Hz), coming from e.g., GPS or visual odometery, to mitigate drifts. Lynen at pg. 3924, col. 1.
Lynen is analogous art to the claimed invention because both are directed to analysis of sensor data from multiple types of sensors, each of which can provide sensor data at different frequencies. It would have been obvious to a person having ordinary skill in the art to combine Atrazhev with Lynen to result in a system that receives data from multiple sensors at varied time intervals and utilizes the sensor with a model because, in both instances, a Kalman filer is used as the model. It would be obvious by substituting the Kalman filter for another type of model to interpolate missing data. Motivation to combine includes utilizing multiple types of sensors, thereby expanding the types of systems that can utilize the system. Doing so increases reusability of the system.
Shi-Nash, which is prior art to the claimed invention, discloses:
(B)
FIG. 16 is a diagram illustration of an example embodiment 1600 showing an analysis that may be performed using behavior models on a network. In the example of embodiment 1600, a transportation network may be analyzed using the known behavior models of three users, Alice 1602, Bob 1604, and Charlie 1606, as well as behavior models of new residents of an apartment complex. Shi-Nash at [0215].
with graph nodes representing locations in the particular space and
The office 1608 is near a subway hub 1620, and both Alice 1602 and Charlie 1606 regularly commute through the subway hub 1620 on their way to the office 1608. Shi-Nash at [0215].
links between them representing the ability for people to move from one location to another in the particular space;
The behavior models may be used to determine when the various employees travel during the day to arrive and leave work. Alice 1602 may have a home 1614 that is near a subway station 1616. Subway station 1616 is on the subway green line 1618, which connects to a subway hub 1620, which is near the office 1608. Shi-Nash at [0218]-[0219].
Shi-Nash is analogous art to the claimed invention because both are directed to graphing the movement of human between locations. It would have been obvious to a person having ordinary skill in the art to combine Shi-Nash with Atrazhev and Lynen to result in a system that utilizes a graph in a flow model to track the movement of people between locations because both Atrazhev and Shi-Nash utilize graphs to track people based on statistical information related to the movements. Motivation to combine includes improved operation of the flow model by using the graph of Shi-Nash, thereby improving the reliability of the system.
Claim 2
Atrazhev discloses:
(F) providing the highly-evaluated states identified by (E) as a current set of states to (D), repeating (D) and (E), and outputting the highly-evaluated states produced by the most recent iteration of (E).
At step 38, the occupancy estimate
x
^
t
t
is updated based on occupancy prediction
x
^
t
+
1
t
, innovation u(t+1) and a weighting coefficient W(t+1) discussed in more detail with respect to the covariance calculations. As indicated by this equation, the updated occupancy estimate
x
^
t
+
1
t
+
1
is based on both the model-based occupancy estimate
x
^
t
+
1
t
generated based on the occupant traffic model ƒ(t) and the observed sensor data z(t+1). The updated state estimate
x
^
t
+
1
t
+
1
becomes the current state estimate
x
^
t
t
in the next iteration. Atrazhev at [0045].
A state that is evaluated using the sensor data (analogous to Step (E)) is then used as a current state in the simulation (analogous to Step (D)).
Claim 3
Atrazhev discloses:
wherein (A) is performed before (B)-(E), and wherein (B)-(E) are applied to the sensor data generated in (A) and not to any additional sensor data.
Sensor data may be provided by a variety of different types of sensor devices, each providing a different type of sensor output that is analyzed to detect occupant movements or locations throughout an area or region. To this end, the present invention discloses an occupancy estimator that takes as input both the sensor data and occupant traffic models, and executes an algorithm to generate an occupancy estimate for the area or region based on the provided inputs. Atrazhev at [0022].
Claim 4
Atrazhev discloses:
wherein the plurality of sensors includes at least one video camera, and wherein the sensor data includes: (1) a plurality of images output by the at least one video cameras and (2) locations of detected people in the plurality of images, as generated by analytics run on the images.
The sensor data is communicated to computer or controller 154. Depending on the type of sensors employed, and whether the sensors include any ability to process captured data, computer 154 may provide initial processing of the provided sensor data. For instance, video data captured by a video camera sensing device may require some video data analysis pre-processing to determine whether the video data shows occupants traversing from one zone to another zone. Atrazhev at [0107].
Claim 6
Atrazhev discloses:
wherein the plurality of sensors further includes at least one RFID sensor, and wherein (E) comprises using the plurality of images output by the plurality of video cameras, the locations of detected people in the plurality of images, and tag detection from the at least one RFID sensor to evaluate the subset of the plurality of subsequent states.
The results of these estimates were compared with the simulation in which all sensor data (including motion sensor data) was employed, as well as the EKF employing the KM-based model. Atrazhev at [0103].
Sensor devices 158 a-158N are distributed throughout a particular region, and may include a variety of different types of sensors, including video detectors, passive infra-red motion sensors, access control devices, elevator load measurements, IT-related techniques such as detection of computer keystrokes, as well as other related sensor devices. In addition, many occupants carry active devices, such as active or passive radio frequency identification (RFID) cards, cell phones, or other devices that can be detected to provide sensor data. Atrazhev at [0106].
Claim 9
Atrazhev discloses:
wherein the plurality of sensors includes at least one RFID sensor, and wherein the sensor data includes data about nearby RFID tags generated by the at least one RFID sensor.
Sensor devices 158 a-158N are distributed throughout a particular region, and may include a variety of different types of sensors, including video detectors, passive infra-red motion sensors, access control devices, elevator load measurements, IT-related techniques such as detection of computer keystrokes, as well as other related sensor devices. In addition, many occupants carry active devices, such as active or passive radio frequency identification (RFID) cards, cell phones, or other devices that can be detected to provide sensor data. Atrazhev at [0106].
Claim 10
Atrazhev discloses:
wherein the plurality of sensors includes at least one motion sensor, and wherein the sensor data includes motion detection events generated by the at least one motion sensor.
Sensor devices 158 a-158N are distributed throughout a particular region, and may include a variety of different types of sensors, including video detectors, passive infra-red motion sensors, access control devices, elevator load measurements, IT-related techniques such as detection of computer keystrokes, as well as other related sensor devices. In addition, many occupants carry active devices, such as active or passive radio frequency identification (RFID) cards, cell phones, or other devices that can be detected to provide sensor data. Atrazhev at [0106].
Claim 19
Atrazhev discloses:
wherein the flow model comprises a uniform representation of the particular space that locates data from sensors with different spatial coverage and sensing capabilities to be fused in the evaluation in (E).
As shown in FIG. 8, computer or controller 154 generates an occupancy estimate that is provided to display or controller device 160. The occupancy estimate may include data including mean estimates of the number of occupants located in a region, probabilities associated with each possible occupancy level, changes in occupancy, data indicative of the reliability or confidence associated with an estimate of occupancy, as well as other useful data related to occupancy. Atrazhev at [0110].
“Mean estimates” are analogous to “uniform representations” of the spaces.
Claim 20
Atrazhev discloses:
wherein the flow model comprises a sensor-agnostic representation of the particular space, enabling the form of the highly-evaluated states produced by the most recent iteration of (E) to be independent of limitations of individual ones of the plurality of sensors.
In the embodiment shown in FIG. 9A, distributed system 162 a includes sensor devices located in nodes 171 and 173, wherein each sensor device (or associated hardware) includes the capability of processing the data provided by the associated sensor device and applying the occupancy estimator algorithm based on the sensed data and associated occupant traffic models (e.g., single-phase model or KM-based model) and sensor models. Atrazhev at [0112].
The “sensor device” is an agnostic representation.
Claim 21
Atrazhev discloses:
wherein the simulating in (D) is informed by prior knowledge about qualities of the particular space and similarities of the particular space to previously analyzed spaces.
[T]he occupant traffic model is based on historical or expected traffic patterns of occupants throughout the area or region and may take into account factors such as layout of the region or building. Atrazhev at [0021].
“Historical traffic patterns” is analogous to “prior knowledge.”
Claim 24
Atrazhev discloses:
wherein simulating in (D) is informed by hyperparameters modeling human-observed ground truth about the occupancy of the particular space being simulated.
Simulated occupancy data may be based on historical or observed data regarding the likely location of occupants (e.g., a classroom may be modeled as containing a particular number of occupants depending on time of day). In addition, any other stored data such as knowledge regarding scheduled meeting times may be used to initialize the initial occupancy state. Atrazhev at [0091].
“Observed data” is analogous to “human-observed ground truth.”
Claim 25
Atrazhev discloses:
wherein producing the plurality of subsequent states comprises applying a probability distribution describing likely movements of people between locations in the particular space.
For instance, the probability of occupancy in a region may be described as a probability distribution by a probability distribution function (PDF) that defines the likelihood associated with each possible state or occupancy level. Atrazhev at [0031].
Claim 26
Atrazhev discloses:
wherein the probability distribution incorporates prior knowledge about how people enter and leave the particular space.
The initial state (e.g., initial occupancy estimates) to which the KM-based model would be applied (for instance, upon the issuance of a fire alarm signifying an egress mode of operation) may be modeled based on statistical occupancy data, simulated occupancy data or stored data regarding the location of occupants. For instance, statistical data may include defining initial occupant locations based on assignment of a simple distribution describing the likely location of occupants (e.g., Gaussian distribution). Simulated occupancy data may be based on historical or observed data regarding the likely location of occupants (e.g., a classroom may be modeled as containing a particular number of occupants depending on time of day). In addition, any other stored data such as knowledge regarding scheduled meeting times may be used to initialize the initial occupancy state. Atrazhev at [0091].
Claim 27
Atrazhev discloses:
generating the probability distribution based on prior knowledge about maximum flow rates that are possible between areas in the particular space.
The term C21 is a flow constraint value that in this equation sets a maximum value for the number of occupants who can flow from zone 2 to zone 1 in a given timestep. Thus, if the left term of equation 8 exceeds the flow constraint value C21, then equation 8 limits the expected flow estimate y21 to equal the flow constraint value C21. Atrazhev at [0068].
The “constraints” are imposed limitations that are analogous to a “maximum flow rate” between areas.
Claim 29
Atrazhev discloses:
generating the probability distribution based on prior knowledge about how crowding affects occupants' possible movements in the particular space.
FIG. 4B illustrates a PDF (illustrated by line 90) associated with estimated occupant flow y21 in which flow constraints are employed to modify the probabilities associated with flow estimates existing above the defined flow constraint threshold. Thus, all probabilities existing above the flow constraint threshold are consolidated onto the value representing the flow constraint threshold (e.g. C21). For example, if the flow constraint threshold C21 is equal to three occupants, then all probabilities associated with more than three occupants flowing between these zones are removed and consolidated onto the threshold value. Atrazhev at [0070].
Once a constrain is met (i.e., a crowd is at a particular level), no more flow is permitted. Hence, the “crowding” affects “possible movement.”
Claim 30
Atrazhev discloses:
using the sensor data to evaluate the subset of the plurality of subsequent states comprises finding a joint probability distribution of the plurality of subsequent states and
In addition, data generated as part of the occupancy estimate
x
^
may be interrelated to one another. For instance, the probability of occupancy in a region may be described as a probability distribution by a probability distribution function (PDF) that defines the likelihood associated with each possible state or occupancy level. Atrazhev at [0031].
an observation probability distribution that summarizes the sensor data.
The initial state (e.g., initial occupancy estimates) to which the KM-based model would be applied (for instance, upon the issuance of a fire alarm signifying an egress mode of operation) may be modeled based on statistical occupancy data, simulated occupancy data or stored data regarding the location of occupants. For instance, statistical data may include defining initial occupant locations based on assignment of a simple distribution describing the likely location of occupants (e.g., Gaussian distribution). Atrazhev at [0091].
Claim 32
Atrazhev discloses:
outputting the highly-evaluated states comprises generating, for each of the plurality of subsequent states, a probabilistic certainty of that state representing an actual state of the particular space's occupancy.
For instance, the probability of occupancy in a region may be described as a probability distribution by a probability distribution function (PDF) that defines the likelihood associated with each possible state or occupancy level. The peak of the curve would represent the most likely estimate of the occupancy associated with the zone, but in addition, the shape of the curve (e.g., the standard deviation associated with the curve) would provide an indication of the confidence or reliability associated with the occupancy estimate. Atrazhev at [0031].
The “confidence or reliability” is analogous to “probabilistic certainty.”
Claim 33
Atrazhev discloses:
outputting the highly-evaluated states comprises providing a human-interpretable representation of a likely actual occupancy of the particular space.
As shown in FIG. 8, computer or controller 154 generates an occupancy estimate that is provided to display or controller device 160. The occupancy estimate may include data including mean estimates of the number of occupants located in a region, probabilities associated with each possible occupancy level, changes in occupancy, data indicative of the reliability or confidence associated with an estimate of occupancy, as well as other useful data related to occupancy. Atrazhev at [0110].
Claim 34
Atrazhev discloses:
outputting the highly-evaluated states comprises providing a representation of a likely actual occupancy of the particular space as soon as sufficient data is available.
The occupancy estimates may be simply displayed to a user or users (e.g., first-responders) via a display device, or may be provided to a controller device that takes some action based on the received occupancy estimate. Atrazhev at [0110].
Claim 35
Atrazhev discloses:
outputting the highly-evaluated states comprises providing a representation of a likely actual occupancy of the particular space during a particular time period retroactively, based on data that arrived before, during, and after the time period.
The occupancy estimates may be simply displayed to a user or users (e.g., first-responders) via a display device, or may be provided to a controller device that takes some action based on the received occupancy estimate. Atrazhev at [0110].
Claim 36
Atrazhev discloses:
wherein the plurality of sensors includes at least one sensor that senses an area not physically located within the space being monitored, and wherein the sensor data includes data that indirectly indicates occupancy in the space.
In the embodiment shown in FIG. 9A, distributed system 162 a includes sensor devices located in nodes 171 and 173, wherein each sensor device (or associated hardware) includes the capability of processing the data provided by the associated sensor device and applying the occupancy estimator algorithm based on the sensed data and associated occupant traffic models (e.g., single-phase model or KM-based model) and sensor models. In an exemplary embodiment, the occupancy estimation algorithm is implemented with an Extended Kalman Filter which generates based on these inputs an occupancy estimation and a covariance (as well as any other useful statistical outputs). For purposes of this description, the distributed occupancy estimation system 162 a that includes both the sensor device and the components used to generate the occupancy estimate, which may include a combination of hardware and software for applying the occupancy estimation algorithm to the sensor data, will be referred to generally as occupancy estimator (OE). In the embodiment shown in FIG. 9A, sensor data observed at node 171 is provided to occupancy estimator OE1, which generates occupancy estimates…corresponding to nodes 171 and 172, respectively. Atrazhev at [0112].
Node 172 does not have a sensor, but the occupancy is estimated based on sensors in nodes 171 and 173, which are “an area not physically located within the space being monitored.”
Claim 37
Atrazhev discloses:
A system comprising at least one non-transitory computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions are executable by at least one computer processor to perform a method for generating a representation of predicted occupancy in a particular space, the method comprising:
For example, although a computer system including a processor and memory was described for implementing the occupancy estimation algorithm, any number of suitable combinations of hardware and software may be employed for executing the mathematical functions employed by the occupancy estimation algorithm. Atrazhev at [0116].
Further, claim 37 recites limitations that are substantially the same as the limitations of claim 1.
Atrazhev teaches those limitations for at least the same reasons as provided with regards to the rejection of claim 1.
Claims 5, 7-8, 11-12, 15-16, and 31 are rejected under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, Fadell, et al., (U.S. Patent No. 8,788,448, hereinafter “Fadell”).
Claim 5
Atrazhev discloses:
The sensor data is communicated to computer or controller 154. Depending on the type of sensors employed, and whether the sensors include any ability to process captured data, computer 154 may provide initial processing of the provided sensor data. For instance, video data captured by a video camera sensing device may require some video data analysis pre-processing to determine whether the video data shows occupants traversing from one zone to another zone. Atrazhev at [0107].
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
wherein the plurality of sensors further includes at least one Wi-Fi-sensing sensor,
Fadell, which is analogous art, discloses:
wherein the plurality of sensors further includes at least one Wi-Fi-sensing sensor
According to technique 412, network connections (wifi, email or specific ports) are monitored for changes in traffic… Fadell, col. 10, lines 1-2.
wherein (E) comprises using…Wi-Fi signal data from the Wi-Fi-sensing sensors to evaluate the subset of the plurality of subsequent states.
According to technique 412, network connections (wifi, email or specific ports) are monitored for changes in traffic, which indicates a likelihood of internet usage and therefore occupancy. According to some embodiments physical layer traffic can be monitored for patterns indicating occupancy. Fadell, col. 10, lines 1-5.
Fadell is analogous art to the claimed invention because both are related to predicting occupancy of a location. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to use Wi-fi signals, as disclosed in Fadell, as one of the sensors in Atrazhev to result in a system that senses occupancy based on detected Wi-fi devices of occupants. Motivation to combine includes improving types of sensors that can be utilized, thus improving occupant tracking. Further, because Wi-fi serves another purpose and is likely already installed in a location, using Wi-fi signals does not require additional hardware installation, thus reducing costs to implement the system.
Claim 7
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
wherein the plurality of sensors includes at least one Wi-Fi access point, and wherein the sensor data includes data about communication of at least one device with the at least one Wi-Fi access point.
Fadell discloses:
wherein the plurality of sensors includes at least one Wi-Fi access point, and wherein the sensor data includes data about communication of at least one device with the at least one Wi-Fi access point.
According to some other embodiments, packets or traffic in any of the software layers (such as monitoring ports, or just looking for more traffic in general) can be sniffed. Fadell, col. 10, lines 7-9.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to include Wi-Fi access points, as disclosed in Fadell, as one of the sensors in Atrazhev to result in a system that senses occupancy based on data that is received by access points from mobile devices located in the vicinity. Motivation to combine includes expanding the types of sensors that can be utilized, thus adding additional ways to track occupants. Further, because Wi-fi serves another purpose and is likely already installed in a location, using Wi-fi signals does not require additional hardware installation, thus reducing costs to implement the system.
Claim 8
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
wherein the plurality of sensors includes at least one Wi-Fi sensor, and wherein the sensor data includes data about Wi-Fi signals detected by the at least one Wi-Fi sensor.
Fadell discloses:
wherein the plurality of sensors includes at least one Wi-Fi sensor, and wherein the sensor data includes data about Wi-Fi signals detected by the at least one Wi-Fi sensor.
According to technique 412, network connections (wifi, email or specific ports) are monitored for changes in traffic, which indicates a likelihood of internet usage and therefore occupancy. According to some embodiments physical layer traffic can be monitored for patterns indicating occupancy. Fadell, col. 10, lines 1-5.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to include Wi-Fi access points, as disclosed in Fadell, as one of the sensors in Atrazhev to result in a system that senses occupancy based on data that is received by access points from mobile devices located in the vicinity. Motivation to combine includes expanding the types of sensors that can be utilized, thus adding additional ways to track occupants. Further, because Wi-fi serves another purpose and is likely already installed in a location, using Wi-fi signals does not require additional hardware installation, thus reducing costs to implement the system.
Claim 11
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
wherein the plurality of sensors includes at least one carbon dioxide sensor, and wherein the sensor data includes carbon dioxide readings generated by the at least one carbon dioxide sensor.
Fadell discloses:
wherein the plurality of sensors includes at least one carbon dioxide sensor, and wherein the sensor data includes carbon dioxide readings generated by the at least one carbon dioxide sensor.
According to technique 434, gas composition can be sensed or monitored for certain components that tend to indicate the presence of occupants. For example, a CO2 sensor can be used to detect levels of CO2 that tend to indicate the presence of occupants. Fadell, col. 11, lines 17-24.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to use the disclosed carbon dioxide sensor of Fadell as a sensor for the system disclosed in Atrazhev to result in a system that estimates occupancy using carbon dioxide presence. Motivation to combine includes improving occupancy counting in locations where other types of sensors are not possible or practicable, such as in dark locations, locations that cannot have visual and/or electrical signals, areas that get too crowded for other techniques to be used, etc..
Claim 12
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
wherein the plurality of sensors includes at least one sound sensor, and wherein the sensor data includes sound level readings generated by the at least one sound sensor.
Fadell discloses:
wherein the plurality of sensors includes at least one sound sensor, and wherein the sensor data includes sound level readings generated by the at least one sound sensor.
According to technique 416, a microphone is used to monitor sounds (within the audible range) that reflect likelihoods of occupancy, such as footsteps, voices, doors closing. Fadell at col. 10, lines 17-19.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the sound sensors disclosed in Fadell with the system of Atrazhev to result in a system that estimates occupancy based on sound. Motivation to combine includes improved occupancy estimation by allowing additional types of input, thus improving accuracy of estimation in areas that may otherwise be difficult to estimate using other types of sensors.
Claim 15
Atrazhev, Lynen, and Shi-Nash does not appear to disclose:
wherein (E) comprises using the sensor data and data about events that are planned in the particular space to evaluate the subset of the plurality of subsequent states.
Fadell discloses:
wherein (E) comprises using the sensor data and data about events that are planned in the particular space to evaluate the subset of the plurality of subsequent states.
According to some embodiments, the prediction engine 120 looks for different periodicities such as daily, weekly, monthly, seasonally, and yearly, in the inputs from some or all of patterns 114, occupancy inputs 130, calendar data 132 and sensor data 110. According to some embodiments, the events and/or patterns from the near past can be more heavily weighted than events and/or patterns from the more distant past. Fadell at col. 7, lines 18-25.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the statistical profiles of Fadell with the sensors of Atrazhev to predict occupancy based on both occupancy information gathered from the sensors as well as known events and/or statuses for future states. Motivation to combine includes improving prediction accuracy by taking into account special circumstances which may affect the occupancy of a location.
Claim 16
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
wherein (E) comprises using the sensor data and predictions about weather in the particular space's location to evaluate the subset of the plurality of subsequent states.
Fadell discloses:
wherein (E) comprises using the sensor data and predictions about weather in the particular space's location to evaluate the subset of the plurality of subsequent states.
For example, dwellings in locations experiencing very cold or very hot seasonal weather or seasonal precipitation can reflect likelihoods of increased occupancy. In another example, dwellings in locations experiencing temperate seasonal weather can reflect likelihoods of decreased occupancy. In another example, dwellings in locations which seasonally experience temperate weather during the day but very cold weather at night can reflect likelihoods of decreased daily but increased evening occupancy. An in another example, dwellings in locations which seasonally experience very hot weather during the day but temperate weather at night can reflect likelihoods of increased daily but decreased evening occupancy. Fadell at col. 8, line 61-col. 9, line 6.
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine Fadell with Atrazhev to result in a system that takes into account the current weather to predict future occupancy of locations. Motivation to combine includes improve reliability of the system by taking into account real time data regarding the conditions at the tracked locations, thereby increasing the flexibility of the system to predict what would otherwise be unexpected changes in occupancy of locations when, for example, it is raining outside (i.e., interior locations and/or locations near entrances may be more likely to have increased traffic).
Claim 31
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
using the sensor data to evaluate the subset of the plurality of subsequent states comprises combining information about individual occupants' likely locations in the particular space over time with anonymous data within the sensor data.
Fadell discloses:
using the sensor data to evaluate the subset of the plurality of subsequent states comprises combining information about individual occupants' likely locations in the particular space over time with anonymous data within the sensor data.
Profiles 220 are examples of statistical profiles for prediction occupancy based on occupant type. Such occupant types include: roommates, families, seniors, and type of company or business. For example, dwellings with one or more preschool age children can reflect the following likelihoods: (1) few regularly scheduled 4+ hour periods when the home isn't occupied; (2) few regular 4+ hour night periods when every occupant is asleep; and (3) that one or more occupants will both retire and rise relatively early each day. Atrazhev at col. 8, lines 14-22.
“Individual occupant” information can include categorization of the occupants based on a likely status of the individual (e.g., child, roommate, elderly).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the categorizations of a location and/or occupants disclosed in Fadell as a source of data to predict occupancy, as disclosed in Atrazhev. Motivation to combine includes improving prediction accuracy by using more than anonymous location data for occupants. Thus, by treating particular occupants as different (e.g., child vs. retired person), future locations can be estimated more accurately, thus improving the capabilities of the system.
Claim 13 is rejected under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Hollar (U.S. Patent Pub. No. 2021/0304577).
Claim 13
Atrazhev does not appear to disclose:
wherein the plurality of sensors includes at least one ultra-wideband sensor, and wherein the sensor data includes object detection data generated by the at least one ultra-wideband sensor.
Hollar, which is analogous art to the claimed invention, discloses:
wherein the plurality of sensors includes at least one ultra-wideband sensor, and wherein the sensor data includes object detection data generated by the at least one ultra-wideband sensor.
The one or more UWB sensors and the one or more image capture sensors are integrated into at least one location device. The at one location device includes a UWB location device, a combination UWB/camera location device and/or a camera location device. A location of the tagged item is tracked using the at least one location device. Hollar at [0004].
Hollar is analogous art to the claimed invention because both are related to tracking objects and/or individuals in a location using sensors. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the UWB sensors of Hollar with the occupancy predictions of Atrazhev to result in a system that tracks individuals using UWB technology. Motivation to combine includes improving sensor data by more reliably detecting the arrival and departure of occupants, thus improving future occupancy predictions. As disclosed in Hollar, “UWB's inherent wideband signal allows for sharp transitions in the time domain. UWB receivers can then detect signal arrival times with a high level of accuracy, producing precise timestamps that translate to distances with centimeter-level accuracy.” Hollar at [0003].
Claim 14 is rejected under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash Blanch (U.S. Patent Pub. No. 2022/0272064, hereinafter “Blanch”).
Claim 14
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
wherein the plurality of sensors includes at least one access control badge sensor, and wherein the sensor data includes badge detection events generated by the at least one badge detection sensor.
Blanch, which is analogous art to the claimed invention, discloses:
wherein the plurality of sensors includes at least one access control badge sensor, and wherein the sensor data includes badge detection events generated by the at least one badge detection sensor.
In another example, sensor(s) 118 may be able to detect a swipe of an ID badge at the entry of a building or other location. Blanch at [0021].
Blanch is analogous art to the claimed invention because both are related to monitoring flow of occupants between areas. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to use the badge sensors of Blanch with the system of Atrazhev to improve accuracy of flow estimations between areas in a location. Motivation to combine includes improving the accuracy of when occupants enter and leave a room because badge swiping to enter or exit an area is more accurate than other techniques (video analysis, RFID tracking) and can include using other personal information about individuals that are associated with a badge to improve accuracy of tracking, thus improving the operability of future predictions.
Claims 17 and 28 are rejected under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Felemban, et al., (U.S. Patent No. 11,151,668, hereinafter “Felemban”).
Claim 17
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
using the sensor data and public transportation schedules governing arrivals to and departures to locations near the particular space to evaluate the subset of the plurality of subsequent states.
Felemban, which is analogous art to the claimed invention, discloses:
using the sensor data and public transportation schedules governing arrivals to and departures to locations near the particular space to evaluate the subset of the plurality of subsequent states.
FIG. 3 illustrates an example process 300 for providing itinerary assignments (also referred to as scheduling assignments or routing solutions) to facilitate crowd management in accordance with various embodiments…The process for itinerary assignments begins with data collection 302. In an example, data associated with a crowd and the crowd's location may be gathered from a plurality of sources, including itinerary assignments, user preferences such as cultural or religious customs, local laws and regulations, building and structural specifications, transportation sources, video or other media sources, environmental sources such as weather monitors, among others. Felemban at col. 13, lines 48-65.
Itinerary assignments may be validated by analysis 308 in real-time, where collected data is constantly updated with real-time data, such as streaming video footage of the crowd along the segment. In another embodiment, itinerary assignments may be validated by analysis 308 based on static data, such as a building's structural occupancy threshold and predicting that crowd density would exceed the threshold. Felemban at col. 15, lines 4-10.
Felemban is analogous art to the claimed invention because both are related to predicting behavior of a group of people within an area or location. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the use of transportation schedules of Felemban with the occupancy prediction of Atrazhev to result in a system that utilizes known scheduling to predict the arrival and departure of occupants from a location. Motivation to combine includes improving accuracy of predicted occupancy by taking into account known arrival times of occupants and potential departure times of occupants, thereby improving the usability of the system for more scenarios.
Claim 28
Atrazhev does not appear to disclose:
generating the probability distribution based on prior knowledge about occupants' typical goals and preferred navigation paths in the particular space.
Felemban discloses:
generating the probability distribution based on prior knowledge about occupants' typical goals and preferred navigation paths in the particular space.
The sources can include, for example, user input, video/image devices, local authorities, weather monitors, and structural inspection reports. Examples of user input may include individual participants' itineraries specifying their desired destinations and visit times, or user preferences or characteristics influencing the itineraries, such as cultural or religious customs. For example, during the Hajj pilgrimage, user preferences or characteristics may include a participant's school of Islamic jurisprudence, which establishes guidelines for which destinations and visit times within Mecca should be included in the participant's itinerary. In certain embodiments, a configuration file or other instructions can be obtained that specifies one or more geographic locations and/or crowds.
“Desired destinations and visit times” are analogous to “typical goals and preferred navigation paths.”
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the itinerary information of Felemban with the occupancy prediction of Atrazhev to result in a system that takes into account known information regarding specific occupants when determining future locations. Motivation to combine includes improving prediction accuracy by leveraging known information regarding occupants to predict future locations of the occupants, thus improving overall operability of the system. Felemban at col. 7, lines 17-30.
Claim 22 is rejected under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Blaiotta (U.S. Patent Pub No. 2020/0283016).
Claim 22
Atrazhev, Lynen, and Shi-Nash do not appear to disclose:
wherein the simulating in (D) comprises simulating the occupancy of the space using Sequential Monte Carlo simulation.
Blaiotta, which is analogous art to the claimed invention, discloses:
wherein the simulating in (D) comprises simulating the occupancy of the space using Sequential Monte Carlo simulation.
In an embodiment, the interaction model may be a directed acyclic graphical models (DAGs), e.g., used to build a Bayesian generative network that serves to encode the motion dynamics of a multi-agent system. In an embodiment, the probabilistic interaction model is a Bayesian network. In order make inferences an embodiment can exploit sampling based inference schemes, e.g., sequential Monte Carlo algorithms. Blaiotta at [0168].
Blaiotta is analogous art to the claimed invention because both are related to tracking the movements of occupants in a location. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to use the Sequential Monte Carlo methods of Blaiotta to infer the movements of occupants in the tracking and prediction system of Atrazhev to result in a system that uses Sequential Monte Carlo Methods to construct models of occupant movements. The use of a Sequential Monte Carlo method to construct models improves the usability of the framework of Atrazhev by improving the versatility of the types of models that may be constructed, based on the types of sensor data and occupancy information that is available.
Claim 23 is rejected under 35 U.S.C. 103 as being obvious over Atrazhev in view of Lynen, Shi-Nash, and Sharma, et al., (“Artificial intelligence agents for crowd simulation in an immersive environment for emergency response”, hereinafter “Sharma”).
Claim 23
Atrazhev, Lynen, Shi-Nash do not appear to disclose:
wherein the simulating in (C) is informed by hyperparameters derived from offline simulations that model individual, artificially intelligent agents that move within the particular space.
Sharma, which is analogous art to the claimed invention, discloses:
wherein the simulating in (C) is informed by hyperparameters derived from offline simulations that model individual, artificially intelligent agents that move within the particular space.
AI agents are computer-simulated agents. Disaster situations are observed to cause panic, stress, anger and wandering behaviors in a crowd. These behavior levels vary among individuals and it is always unpredictable to specify their reaction in crowds. The CVE has two types of AI agents added to the environment: regular agents and colorful agents. Regular agents have humanlike skin tones and are dressed casually, and AI agents have a color, which determines the agent’s personality. These personality traits were programmed using C# programming language to differentiate how the AI agent will communicate with emergency response officers (refer figure 4). Sharma at pg. 176-4, paragraph 2.
Sharma is analogous art to the claimed invention because both are related to modeling the behavior of occupants in moving between areas of a location. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to utilize the artificially intelligent agents of Sharma in place of statistical simulations to result in a system that models and predicts the movements of occupants using an artificial intelligence method. Motivation to combine includes improving the accuracy of predictions by utilizing artificial intelligence to predict behavior of individuals, thereby improving the versatility of the system by allowing different types of people (e.g., children, adults, elderly) to be modeled differently within the simulation, thus improving accuracy of predictions.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Liao, et al., “An integrated approach to occupancy modeling and estimation in commercial buildings.”
Pazhoohesh, et al., “A Comparison of Methods for Missing Data Treatment in Building Sensor Data.”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Communication
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JOSEPH MORRIS
Examiner
Art Unit 2188
/JOSEPH P MORRIS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188