DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the application filed on 12/20/2023.
Claims 1-9 have been examined and rejected.
The IDS filed on 12/20/2023 and 9/3/2025 have been considered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “congestion detector, detecting congestion state”; “congestion measurer, measuring congestion state”; and “congestion state estimator, estimating congestion state” in claims 1 and 6.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The specification discloses at paragraph 24, that these functional blocks comprising (“congestion detector, detecting congestion state”; “congestion measurer, measuring congestion state”; and “congestion state estimator, estimating congestion state”) are realized by the cooperation of hardware resources, such as the central processing unit, memory, input devices, output devices, and peripheral devices connected to the computer, and software that is executed using them.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 9 is rejected under 35 U.S.C. 101 as covering non-statutory subject matter. The claim is directed to a computer readable medium (CRM) that covers transitory signals which is not statutory. This rejection can be overcome by adding “Non-transitory” before the limitation “computer-readable medium”.
Allowable Subject Matter
Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 5, 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Felemban (US 20220254162 A1).
Regarding Claim 1, Felemban discloses a communication control apparatus (see FIG. 1. illustrates a system for detecting and predicting crowd congestion in real-time) comprising at least one processor that performs:
by a communication congestion detector, detecting congestion state of communications in a communication cell (see FIG. 1. Congestion detection system (see FIG 2 for details), para 62, congestion detection system manipulates the image data to obtain motion data (e.g., trajectories) of individuals in a crowd, to determine the level of congestion in an area… congestion detection system divides the video stream/i.e., representing communication stream in a cell, from data intake system into temporal segments, extracts motion data (e.g., trajectories) of individuals of the crowd from each of the temporal segments, and converts the trajectories into two-dimensional (2D) images, for example spatial-temporal images (also referred to as trajectory images), to feed into training system);
by a congestion measurer, measuring congestion state of at least one of persons and objects in the communication cell (see para 62, Congestion detection system, see details in FIG. 2 score generator/i.e., representing the function of congestion measurer, then encodes the trajectories with a classification score, and generates a score map to visualize and localize areas of crowd congestion. The classification score may be based on a level of confidence of the degree of congestion of each trajectory); and
by a congestion state estimator, estimating congestion state in the communication cell, based on the congestion state of communications detected by the communication congestion detector and the congestion state of at least one of the persons and the objects measured by the congestion measurer (see para 65, Congestion prediction system (details in FIG. 3.) intakes a time series list of areas (e.g. localized regions) from the temporal segments (e.g., segmented video streams of crowd density in an area), predicts future congestion of the area, and visualizes the prediction (e.g., displays chart of degree of congestion at various future time intervals)… congestion prediction system may analyze the area of the temporal segment and normalize the area. Congestion prediction system may feed the time series list of the areas of each temporal segment into the LSTM learning model. The LSTM model may predict future congestion for each area of the time series list; also see para 11, LSTM is a long short-term memory (LSTM) model. The LSTM model may be trained to predict potential congestion based on, for example, the congestion patterns in the time series).
Felemban does not specify a “congestion state measurer”.
However, it teaches that the congestion detection system generates a score map of areas of crowd congestion, and performs the function of generating score by measurement of crowd congestion data.
It would have been obvious, to one having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Felemban, to specify the congestion state measurer as being implemented in the congestion detection system, since the congestion detection system is already performing the function of determining and generation score map of crowd congestion using the score generator (see Felemban, para 62).
Regarding Claim 5, Felemban discloses the communication control apparatus according to claim 1, wherein the congestion state estimator determines the state of at least one of the persons and the objects measured by the congestion measurer (see para 65, Congestion prediction system (details in FIG. 3.) intakes a time series list of areas (e.g. localized regions) from the temporal segments (e.g., segmented video streams of crowd density in an area)/i.e., crowd represents persons, predicts future congestion of the area, and visualizes the prediction (e.g., displays chart of degree of congestion at various future time intervals). In an embodiment, congestion prediction system may analyze the area of the temporal segment and normalize the area. Congestion prediction system may feed the time series list of the areas of each temporal segment into the LSTM learning model. The LSTM model may predict future congestion for each area of the time series list; also see para 11, LSTM is a long short-term memory (LSTM) model. The LSTM model may be trained to predict potential congestion based on, for example, the congestion patterns in the time series).
Regarding Claim 7, Felemban discloses the communication control apparatus according to claim 1, wherein the congestion measurer measures the congestion state of at least one of the persons and the objects in the communication cell, through the communication radio wave of a base station that provides the communication cell (see para 65, Congestion prediction system (details in FIG. 3.) intakes a time series list of areas (e.g. localized regions) from the temporal segments (e.g., segmented video streams of crowd density in an area)/i.e., crowd represents persons, predicts future congestion of the area, and visualizes the prediction (e.g., displays chart of degree of congestion at various future time intervals). In an embodiment, congestion prediction system may analyze the area of the temporal segment and normalize the area. Congestion prediction system may feed the time series list of the areas of each temporal segment into the LSTM learning model. The LSTM model may predict future congestion for each area of the time series list; also see para 11, LSTM is a long short-term memory (LSTM) model. The LSTM model may be trained to predict potential congestion based on, for example, the congestion patterns in the time series).
Felemban does not specify a “congestion state measurer”.
However, it teaches that the congestion detection system generates a score map of areas of crowd congestion, and performs the function of generating score by measurement of crowd congestion data.
It would have been obvious, to one having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Felemban, to specify the congestion state measurer as being implemented in the congestion detection system, since the congestion detection system is already performing the function of determining and generation score map of crowd congestion using the score generator (see Felemban, para 62).
Regarding Claim 8, Felemban discloses a communication control method performing:
detecting congestion state of communications in a communication cell (see FIG. 1. Congestion detection system (see FIG 2 for details), para 62, congestion detection system manipulates the image data to obtain motion data (e.g., trajectories) of individuals in a crowd, to determine the level of congestion in an area… congestion detection system divides the video stream/i.e., representing communication stream in a cell, from data intake system into temporal segments, extracts motion data (e.g., trajectories) of individuals of the crowd from each of the temporal segments, and converts the trajectories into two-dimensional (2D) images, for example spatial-temporal images (also referred to as trajectory images), to feed into training system);
measuring congestion state of at least one of persons and objects in the communication cell (see para 62, Congestion detection system, see details in FIG. 2 score generator/i.e., representing the function of congestion measurer, then encodes the trajectories with a classification score, and generates a score map to visualize and localize areas of crowd congestion. The classification score may be based on a level of confidence of the degree of congestion of each trajectory); and
estimating congestion state in the communication cell, based on the congestion state of communications detected by the communication congestion detector and the congestion state of at least one of the persons and the objects measured by the congestion measurer (see para 65, Congestion prediction system (details in FIG. 3.) intakes a time series list of areas (e.g. localized regions) from the temporal segments (e.g., segmented video streams of crowd density in an area), predicts future congestion of the area, and visualizes the prediction (e.g., displays chart of degree of congestion at various future time intervals)… congestion prediction system may analyze the area of the temporal segment and normalize the area. Congestion prediction system may feed the time series list of the areas of each temporal segment into the LSTM learning model. The LSTM model may predict future congestion for each area of the time series list; also see para 11, LSTM is a long short-term memory (LSTM) model. The LSTM model may be trained to predict potential congestion based on, for example, the congestion patterns in the time series).
Felemban does not specify a “congestion state measurer”.
However, it teaches that the congestion detection system generates a score map of areas of crowd congestion, and performs the function of generating score by measurement of crowd congestion data.
It would have been obvious, to one having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Felemban, to specify the congestion state measurer as being implemented in the congestion detection system, since the congestion detection system is already performing the function of determining and generation score map of crowd congestion using the score generator (see Felemban, para 62).
Regarding Claim 9, Felemban discloses a computer-readable medium storing a communication control program causing a computer to perform:
detecting congestion state of communications in a communication cell (see FIG. 1. Congestion detection system (see FIG 2 for details), para 62, congestion detection system manipulates the image data to obtain motion data (e.g., trajectories) of individuals in a crowd, to determine the level of congestion in an area… congestion detection system divides the video stream/i.e., representing communication stream in a cell, from data intake system into temporal segments, extracts motion data (e.g., trajectories) of individuals of the crowd from each of the temporal segments, and converts the trajectories into two-dimensional (2D) images, for example spatial-temporal images (also referred to as trajectory images), to feed into training system);
measuring congestion state of at least one of persons and objects in the communication cell (see para 62, Congestion detection system, see details in FIG. 2 score generator/i.e., representing the function of congestion measurer, then encodes the trajectories with a classification score, and generates a score map to visualize and localize areas of crowd congestion. The classification score may be based on a level of confidence of the degree of congestion of each trajectory); and
estimating congestion state in the communication cell, based on the congestion state of communications detected by the communication congestion detector and the congestion state of at least one of the persons and the objects measured by the congestion measurer (see para 65, Congestion prediction system (details in FIG. 3.) intakes a time series list of areas (e.g. localized regions) from the temporal segments (e.g., segmented video streams of crowd density in an area), predicts future congestion of the area, and visualizes the prediction (e.g., displays chart of degree of congestion at various future time intervals)… congestion prediction system may analyze the area of the temporal segment and normalize the area. Congestion prediction system may feed the time series list of the areas of each temporal segment into the LSTM learning model. The LSTM model may predict future congestion for each area of the time series list; also see para 11, LSTM is a long short-term memory (LSTM) model. The LSTM model may be trained to predict potential congestion based on, for example, the congestion patterns in the time series).
Felemban does not specify a “congestion state measurer”.
However, it teaches that the congestion detection system generates a score map of areas of crowd congestion, and performs the function of generating score by measurement of crowd congestion data.
It would have been obvious, to one having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Felemban, to specify the congestion state measurer as being implemented in the congestion detection system, since the congestion detection system is already performing the function of determining and generation score map of crowd congestion using the score generator (see Felemban, para 62).
Claim(s) 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Felemban in view of Xu (US 20220165150 A1).
Regarding Claim 2, Felemban discloses the communication control apparatus according to claim 1, wherein the at least one processor performs, by a congestion state sharer, sharing the congestion state in the communication cell estimated by the congestion state estimator with a service provider that provides a service in the communication cell (see paras 65-66, FIG. 3 for details, the congestion prediction system intakes a time series list of areas (e.g. localized regions) from the temporal segments (e.g., segmented video streams of crowd density in an area), predicts future congestion of the area, and visualizes the prediction (e.g., displays chart of degree of congestion at various future time intervals) … Visualization system may comprise a display to present information associated with the congestion detection system and/or congestion prediction system. For example, a score map generated by congestion detection system may be displayed with color coding to indicate regions of crowd congestion or normal crowd density. Visualization system may comprise a user interface (e.g., interactive dashboard) to allow a user to provide feedback); also see FIG. 1 depicting the congestion prediction system connected to the network).
Felemban does not disclose details regarding sharing the congestion state with a service provider.
In the same field of endeavor, Xu teaches this limitation: see FIG. 7, paras 122-123, it may be tested whether the lane of the road segment is congested or not. The control moves to non-congestion state, else for congestion state. The processor (see FIGs. 1-2, para 80, a system used for determining road capacity pattern data of a lane) may be configured to test whether the lane of the road segment is congested or not. The traffic condition on the lane of the road segment may correspond to a congestion state or a non-congestion state… congestion message to the traffic service backend server/i.e., representing the traffic server for the service provider, may be sent.
It would have been obvious, to one having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Felemban, to share the congestion state with a service provider as taught by Xu, since there is a need for a reliable system for safety reasons to identify data associated with dynamic traffic change and road capacity for an autonomous vehicle status change update so that it is beneficial for a driver of the autonomous vehicle, a customer or agencies. (see Xu, para 5).
Regarding Claim 3, Felemban discloses the communication control apparatus according to claim 2, wherein the service provider includes an emergency agency that provides an emergency service in response to the congestion state in the communication cell (see paras 4-5, The congestion detection system may be used to monitor a crowd to prevent or mitigate crowd disasters/i.e., representing emergency situation service use case. Moreover, the congestion detection system may be used by crowd management entities to timely detect congested regions and manage the crowd efficiently).
Regarding Claim 4, Felemban discloses the communication control apparatus according to claim 2, wherein the service provider provides the service at a store in the communication cell (see para 5, elements of the present invention may be applied to other situations involving large crowds or high densities of people occupying a space/i.e., representing a store, including, but not limited to, traffic management, urban development, public health and disease prevention, transportation management, animal migration, video game players, etc.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEEPA BELUR whose telephone number is (571)270-3722. The examiner can normally be reached M-F 8 am - 4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kevin Bates can be reached at 571-272-3980. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEEPA BELUR/Primary Examiner, Art Unit 2472
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