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 Amendment
The following is a final office action in response to the request for continued examination filed on 12/16/2025 and the claims filed on 09/29/2025. Claims 41, 47 and 59 have been amended. Claims 42, 43, 50, 51 and 54 are cancelled. Claims 41, 44-49, 52-53 and 55-59 are currently pending and have been examined.
Response to Arguments
Applicant’s arguments and remarks filed on 09/29/2025 have been fully considered.
Applicant’s amendments overcome the U.S.C. §112(b) rejection of claim 41.
Applicant’s arguments provided for the U.S.C. §101 rejections of claims 41-58 have been considered but are not persuasive.
(A) Applicant argues, “The Office Action rejects claims 41, 44-49, and 52-59 under 35 U.S.C. § 101 because the claimed invention is allegedly directed to an abstract idea without significantly more. Office Action at pp. 2-7 and 9-20. Applicant respectfully submits that the Office Action's characterization oversimplifies the claims and improperly ignores features recited in the claims.
“As background, Applicant's Specification discloses that the current methods of inferring traffic conditions are either based on manual traffic counting, automatic traffic counting, or by collecting telemetry from smartphones and in-vehicle sensors such as GPS navigation systems and dedicated sensors in trams and buses. Manual traffic counting involves human observers who visually count traffic and report the tally either on a sheet or via an app or specialized handheld device. Manual methods also involve individuals reporting traffic incidents by contacting the local traffic authority. Automatic traffic counting includes installation of both permanent as well as temporary traffic counting devices. These electronic devices often involve sensors placed on the road surface used to detect and tally traffic. Off- road sensors can also be used, such as infrared beams, radar, and cameras. When it comes to smartphone and in-vehicle sensor data collection for traffic reporting purposes, it is generally done using GPS coordinates tracking of users and vehicles. In the case of smartphones, by estimating the speed at which the user is moving, combined with GPS coordinates of known roads, one can infer whether the user is inside a moving vehicle. Specification at 0003, 0004.
“An advantage of embodiments of the invention is the enabling of monitoring of traffic in areas without the need to provide specific infrastructure components or excessive human resources to accomplish such tasks. Specification at 0031. For example, an example of saving resources is that the environmental categorization can be obtained based on radio frequency characteristics or performance management counters that a wireless device is already collecting during operation (e.g., for the network operational support system (OSS)).
“To further clarify this advantage, independent claims 41 and 47 have been amended above to clarify that the radio frequency characteristics or the performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station and that the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system.
“Thus, independent claims 41 and 47 are not directed to mental process because the human mind does not receive a radio frequency characteristic or performance management counter from an operation support system, or a business support system, as claimed. The Office Action alleges that a human may read a data report or look at a graph to receive such characteristics into the human mind. Office Action at p. 3. While this may be accurate, it does not achieve the same benefits as the claimed steps. As described above, reading a data report or looking at a graph to receive such characteristics is equivalent to the described manual traffic counting that involves human observers who visually count traffic and report the tally either on a sheet or via an app or specialized handheld device. A benefit of particular embodiments is that the data physically collected and reported by the wireless device to the OSS may be repurposed to provide an enhanced computer system capable of monitoring traffic in areas without the need to provide specific infrastructure components or excessive human resources to accomplish such tasks. Thus, in the claimed embodiments, the receiving a radio frequency characteristic or performance management counter from an operation support system, or a business support system, is not an extra-solution activity that merely recites gathering and outputting data. Instead, the claim element refers to obtaining specific data from a specific source to achieve the optimizations described above. Thus, not all uses require such data gathering or data output, as suggested by the Office Action. As pointed out in the Office Action and in the background of the Specification, similar data may be retrieved from multiple avenues, but the particularly claimed steps offer advantages over the other avenues.
“Thus, claims 41 and 47, for example, are directed to an improved computer system for determining at least one area as a parameter associated with an environment categorization of an area, at a specified point in time or a specified time interval. The Federal Circuit has repeatedly emphasized the need "to avoid oversimplifying the claims by looking at them generally and failing to account for the specific requirements of the claims." McRo, Inc. v. Bandai Namco Games Am. Inc., No. 2015-1080, 2016 WL 4896481, at *7 (Fed. Cir. Sep. 13, 2016). Applicant's claims are more similar to claims found to be patent-eligible by the Federal Circuit in Enfish, LLC v. Microsoft Corp., where the Federal Circuit asked whether, under Alice, "the focus of the claims is on the specific asserted improvement in computer capabilities" or whether the focus is "on a process that qualifies as an abstract idea" and merely uses the computer as a tool. Enfish, 822 F.3d at 1336. The claims in Enfish directed towards particular means for storing and indexing data were found patent eligible by the Federal Circuit. Id., at 1338. Like the claims at issue in Enfish., Applicant's claims are not directed towards an abstract idea, but instead towards a technical improvement in a computer system for determining at least one area as a parameter associated with an environment categorization of an area. Thus, the computer system recited in claims 41 and 47 are not directed to a general purpose computer, but a specialized computer system with the technical advantages described above. For at least these reasons, the Office Action has not established that Applicant's claims 41-58 are directed to an abstract idea. Applicant respectfully requests withdrawal of the rejections,” (from remarks page 8-10).
As to point (A), Examiner respectfully disagrees. Applicant asserts that the computer system of claims 41 and 47 is directed to a specialized computer system because it represents an improvement over current methods of inferring traffic congestion, because it can be obtained without additional infrastructure or excessive human resources by using data already collected for the Operations Support System (OSS). Based on further consultation with a team of Quality Assurance Specialists, Examiner notes that the steps positively recited in the claim are to receive data from the OSS or business support system (BSS) and to obtain a parameter using this data. In particular, obtaining the radio frequency characteristics used to derive the parameter is not positively recited in the claim. An end-user such as an engineer may view output data from the OSS and BSS, and may perform a mental process or a calculation using pen and paper to obtain from this data a parameter associated with environmental characterization. Thus, the steps positively recited in the claim can be either performed as a mental process, or are data gathering, extra-solution activity, or equivalent to “apply it”, as discussed further below. The examiner suggests positively claiming the step of collecting the data used to obtain the parameter. When considered in combination with the other claim limitations, this suggested addition would potentially change the 101 analysis under Step 2B to determine whether the claim provides an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
Applicant’s arguments provided for the U.S.C. §103 rejections of claims 41, 44-49 and 52-59 have been considered but are but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claim 41 is objected to because of the following informalities: “he” in line 13 should be “the”. Appropriate correction is required.
Claim 47 is objected to because of the following informalities:
“he” in line 9 should be “the”.
“and a or pedestrian vehicle type” in the final 2 lines should be “and a vehicle or pedestrian type”.
Appropriate correction is required.
Claim 59 is objected to because of the following informalities: “he” in line 11 should be “the”. Appropriate correction is required.
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 41,44-49,52-53 and 55-59 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Below, supporting analysis follows the Subject Matter Eligibility Test described in MPEP § 2106.
101 Analysis: Step 1
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter.
Independent claims 41, 47 and 59 are directed towards a method, a computer system, and a computer program product with nontransitory computer readable means, respectively. Therefore, each of the independent claims 41, 47 and 59, and the corresponding dependent claims 42-46 and 48-58 are directed to a statutory category of invention under Step 1.
101 Analysis: Step 2A, Prong 1
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong 1, examiners evaluate whether the claim recites a judicial exception.
Regarding Prong 1, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 41 includes limitations that recite an abstract idea (emphasized below):
A method performed by a computer system comprising one or more network nodes for determining at least one parameter associated with an environmental categorization of an area, the environmental categorization representing the area at a specified point in time or a specified time interval, the method being:
training a machine learning model using a set of training data representing a first set of radio frequency characteristics or a performance management counter, wherein the radio frequency characteristics or the performance management counter is derived from radio waves off signals propagated from at least one wireless communication device to a radio base station;
receiving at least one radio frequency characteristic or a performance management counter, wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system, and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station;
obtaining the parameter associated with the environmental categorization, by using the machine learning model after the training on the received radio frequency characteristic or the received performance management counter; and
sending or initiating the sending of the parameter associated with the environmental categorization of the area obtained using the machine learning model to any one of an information storage, a communication device, and a network node of the computer system wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count, and a vehicle or pedestrian type.
Independent claim 47 includes limitations that recite an abstract idea (emphasized below):
A computer system comprising one or more network nodes for determining at least one area as a parameter associated with an environment categorization of an area, at a specified point in time or a specified time interval; the network nodes being configured to:
receive at least one radio frequency characteristic or performance management counter, wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system, and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station;
obtain the parameter associated with the environmental categorization from a machine learning model that has been applied on the received radio frequency characteristic or the received performance management counter; and
send or initiate the sending of the parameter associated with the environmental categorization, of the area, to any one of at least one information storage, a communication device, and a network node of the computer system and wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count, and a vehicle or pedestrian type.
These limitations, as drafted, are a system that, under broadest reasonable interpretation, covers performance of the limitation as a mental concept. That is, nothing in the claim elements preclude the steps from practically being performed as a mental process. For example, “…obtain the parameter associated with the environmental categorization… wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion and/or pedestrian traffic congestion in the area and the traffic congestion parameter is at least any one of a vehicle flow rate, a vehicle count, and a vehicle type” may be interpreted as a mental determination made according to observable data, such as determining the density of vehicles in an area. The mere recitation of generic computing components does not take the claim out of the mental process grouping. Thus, the claim recites an abstract idea.
Independent claim 59 includes limitations that recite an abstract idea (emphasized below):
A computer program product comprising a computer program for a computer system for determining at least one parameter associated with an environmental categorization of an area, the environmental categorization representing the area at a specified point in time or a specified time interval, and a nontransitory computer readable means on which the computer program is stored, wherein the computer program comprising comprises computer program code, which, when run on the computer system, causes the computer system to perform the steps of:
receiving at least one radio frequency characteristic or a performance management counter, wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system, and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station;
obtaining, by using the received radio frequency characteristic or performance management counter, and a machine learning model, the parameter associated with the environmental categorization of the area, and
sending or initiating the sending of the parameter associated with the environmental categorization, obtained by the machine learning model to any one of: at least one information storage, a communication device, and a network node of the computer system and wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count, and a vehicle or pedestrian type.
These limitations, as drafted, are a system that, under broadest reasonable interpretation, covers performance of the limitation as a mental concept. That is, nothing in the claim elements preclude the steps from practically being performed as a mental process. For example, “…obtaining, by using the received radio frequency characteristic or performance management counter… the parameter associated with the environmental categorization of the area… wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count, and a vehicle or pedestrian type” may be interpreted as a mental determination made according to observable data, such as an end-user review data made available by an OSS or BSS. The mere recitation of generic computing components does not take the claim out of the mental process grouping. Thus, the claim recites an abstract idea.
101 Analysis: Step 2A, Prong 2
If the claim recites a judicial exception in Step 2A, Prong 1, the claim requires further analysis in Step 2A, Prong 2. In Step 2A, Prong 2, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Regarding Prong 2, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in MPEP § 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra-solution activity, or generally linking the use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In claim 41, the additional elements beyond the above-noted idea are as follows (where the underlined portions are the “additional elements” while the bolded portions continue to represent the “abstract idea”):
A method performed by a computer system comprising one or more network nodes for determining at least one parameter associated with an environmental categorization of an area, the environmental categorization representing the area at a specified point in time or a specified time interval, the method being:
training a machine learning model using a set of training data representing a first set of radio frequency characteristics or a performance management counter, wherein the radio frequency characteristics or the performance management counter is derived from radio waves off signals propagated from at least one wireless communication device to a radio base station;
receiving at least one radio frequency characteristic or a performance management counter, wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system, and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station;
obtaining the parameter associated with the environmental categorization, by using the machine learning model after the training on the received radio frequency characteristic or the received performance management counter; and
sending or initiating the sending of the parameter associated with the environmental categorization of the area obtained using the machine learning model to any one of an information storage, a communication device, and a network node of the computer system wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion and pedestrian traffic congestion in the area and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count, and a vehicle or pedestrian type.
In claim 47, the additional elements beyond the above-noted idea are as follows (where the underlined portions are the “additional elements” while the bolded portions continue to represent the “abstract idea”):
A computer system comprising one or more network nodes for determining at least one area as a parameter associated with an environment categorization of an area, at a specified point in time or a specified time interval; the network nodes being configured to:
receive at least one radio frequency characteristic or performance management counter wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system, and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station;
obtain the parameter associated with the environmental categorization from a machine learning model that has been applied on the received radio frequency characteristic or the received performance management counter; and
send or initiate the sending of the parameter associated with the environmental categorization, of the area, to any one of at least one information storage, a communication device, and a network node of the computer system and wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count, and a vehicle or pedestrian type.
In claim 59, the additional elements beyond the above-noted idea are as follows (where the underlined portions are the “additional elements” while the bolded portions continue to represent the “abstract idea”):
A computer program product comprising a computer program for a computer system for determining at least one parameter associated with an environmental categorization of an area, the environmental categorization representing the area at a specified point in time or a specified time interval, a nontransitory computer readable means on which the computer program is stored, wherein the computer program comprising comprises computer program code, which, when run on the computer system, causes the computer system to perform the steps of:
receiving at least one radio frequency characteristic or a performance management counter, wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system, and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station;
obtaining, by using the received radio frequency characteristic or performance management counter, and a machine learning model, the parameter associated with the environmental categorization of the area, and
sending or initiating the sending of the parameter associated with the environmental categorization, obtained by the machine learning model to any one of: at least one information storage, a communication device, and a network node of the computer system and wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count, and a vehicle or pedestrian type.
For the following reasons, the examiner submits that the above identified additional elements do not integrate the above-noted abstract idea into a practical application.
Regarding the additional elements of claim 41, “receiving at least one radio frequency characteristic,” and “sending or initiating the sending of the parameter” amount to extra-solution activity.
The limitations “training a machine learning model” and “using the machine learning model” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The judicial exception of “obtaining the parameter associated with the environmental categorization” is performed “using the machine learning model.” The machine learning model is used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of “obtaining the parameter” and do not include any details about how the “obtaining” is accomplished. See MPEP 2106.05(f).
The recitation of “using the machine learning model” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using the machine learning model” limits the identified judicial exceptions “obtaining a parameter”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Regarding the further additional elements of claim 47, “a computer system comprising one or more network nodes” are merely generic components which allow the abstract idea to be applied (MPEP § 2106.05(f)(2)). The examiner submits that these elements are mere computers or other machinery used as a tool to perform the existing process.
Regarding the further additional elements of claim 59, “a computer program product for a computer system, wherein the computer program comprises computer program code” comprises merely generic components which allow the abstract idea to be applied (MPEP § 2106.05(f)(2)). The examiner submits that these elements are mere computers or other machinery used as a tool to perform the existing process.
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis: Step 2B
If the additional elements do not integrate the exception into a practical application in step 2A Prong 2, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether it provides an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, the additional elements of “training a machine learning model”, “using the machine learning model” and “a computer system comprising one or more network nodes” amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
As discussed above, the communication involved in the “receiving at least one radio frequency characteristic,” and “sending or initiating the sending of the parameter” amounts to extra-solution activity. MPEP § 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Also, the Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP § 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
The various metrics/limitations of claims 44-45, 50 and 53 narrow the limitations of the previously-recited data gathering, which merely amounts to extra-solution activity (see MPEP § 2106.05(g)). For the reasons described above with respect to claims 41 and 47, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
The various metrics/limitations of claims 46 and 48-49 narrow the limitations of the previously-recited communication between components, which merely amounts to extra-solution activity (see MPEP § 2106.05(g)). For the reasons described above with respect to claims 41 and 47, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
The various metrics/limitations of claims 55-58 merely narrow the previously recited abstract idea limitations (i.e., further characterization of the determining of the parameter and associated time interval). For the reasons described above with respect to claims 41 and 47, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
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.
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.
Claims 41, 44, 46-49, 52 and 55-59 are rejected under 35 U.S.C. 103 as being unpatentable over Nakamura et al. (US-20220014285-A1; hereinafter, Nakamura) in view of Wootton et al. (US-20190297459-A1; hereinafter, Wootton) further in view of Yang et al. (US-20190208428-A1; hereinafter, Yang).
Regarding claim 41, Nakamura discloses [Note: what Nakamura fails to disclose is strike-through]
A method performed by a computer system (see at least Fig. 1, estimation apparatus 101) comprising one or more network nodes (see at least Fig. 1 showing transmitter and receiver nodes. See also [0007]; “A first aspect of the invention is an estimation method of estimating a congestion degree of blocking entities in a communication region between a transmitter and a receiver performing radio communication.”) for determining at least one parameter associated with an environmental categorization of an area (see at least [0007]; “A first aspect of the invention is an estimation method of estimating a congestion degree of blocking entities in a communication region between a transmitter and a receiver performing radio communication”), the environmental categorization representing the area at a specified point in time or a specified time interval (see at least [0007]; “The estimation method includes: acquiring received power; calculating propagation losses between the transmitter and the receiver, based on the received power acquired in the acquiring of the received power; calculating a median of a plurality of the propagation losses calculated in the calculating of the propagation losses in a predetermined time period determined in advance, and outputting the median as propagation losses to be used for subsequent processing…”), the method being:
receiving at least one radio frequency characteristic (see at least [0007]; “The estimation method includes: acquiring received power…”) or a performance management counter,
obtaining the parameter associated with the environmental categorization, by using a model (see at least [0024] – [0025]; “FIG. 1 illustrates an example of an estimation model for shadowing losses due to human bodies when a communication region between a transmitter Tx and a receiver Rx is blocked by a plurality of human bodies…In the example of FIG. 1, the propagation losses vary due to the traffic of a plurality of humans between the transmitter Tx and the receiver Rx, and the received power in the receiver Rx thus varies as well. In view of this, in the present embodiment, the estimation apparatus 101 acquires the received power in the receiver Rx, and calculates the propagation losses between the transmitter Tx and the receiver Rx. Then, the estimation apparatus 101 calculates a distance from the blocking entities based on the propagation losses, and calculates a congestion degree in the target area.”)
(see at least [0006]; “An object of the present invention is to provide an estimation method, an estimation apparatus, and an estimation program that allow for estimation of a congestion degree of humans, vehicles, or the like in a target area by acquiring propagation losses of radio communication in the target area.”), wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area (see at least [001]; “The present invention relates to a technique of estimating a degree of congestion of humans, vehicles, or the like in a measurement environment by acquiring propagation losses of radio communication.”) and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count (see at least [0052]; “FIG. 6 illustrates an example of blocking entities detected in chronological order. Here, in FIG. 6, the congestion degree calculation unit 206 acquires the number of blocking entities in a predetermined range (rectangular box region in FIG. 6) with the same distance from the transmitter Tx or the receiver Rx.”), and a vehicle or pedestrian type.
However, Nakamura does not teach training or using a machine learning model, nor does Nakamura explicitly teach wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system, and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station and sending or initiating the sending of the parameter associated with the environmental categorization of the area obtained using the machine learning model to any one of: an information storage, a communication device, and a network node of the computer system.
Nakamura is directed to congestion degree estimation using propagation loss of wireless communication, and Wootton discloses detecting the presence of a biological mass in a communications network. Wootton teaches:
A method performed by a computer system (see at least Fig. 1, detection network 103) comprising one or more network nodes (see at least Fig. 1, plurality od nodes 107) for determining at least one parameter associated with an environmental categorization of an area (see at least Abs; “The system as a whole can be used for a wide variety of applications, ranging from occupancy sensing, as might be used for lighting control and/or security, to counting the number of people in a space as might be needed for a heat and/or traffic map, to a system that tracks individual humans moving through a space.”), the environmental categorization representing the area at a specified point in time or a specified time interval (see at least [0026]; “In another embodiment of the method, the method further comprises: the computer server storing a plurality of historical data records indicative of whether a human was present in the detection area over a period of time, each of the historical data records comprising an indication of the number of humans detected in the detected area and the date and time of when the number of humans was detected in the detection area; and the computer server making the historical data records available to one or more external computer systems via an interface.”), the method being:
training a machine learning model using a set of training data (see at least [0077]; “In an embodiment, in order to allow the detection network (103) to detect the presence or absence of a particular biological mass, the system includes a training aspect or step. This aspect may comprise, after the baseline is established, one or more humans are deliberately interposed in the network at one or more locations in the network, and one or more additional sets of baseline data are collected and stored. This second baseline may be used for comparison purposes to improve accuracy in detecting the size, shape, and/or other characteristics of a biological mass interposed in the network, and/or for improving the accuracy of location determination. Such training may use supervised or unsupervised learning, and/or may utilize techniques known to one skilled in the art of machine learning.”) representing a first set of radio frequency characteristics (see at least [0067]; “To detect a change, generally a baseline of communication is developed against which recently transmitted signals are compared. This baseline of signal characteristics between nodes (107) is generally established prior to the use of the detection network (103) as a detector.”) or a performance management counter, wherein the radio frequency characteristics or the performance management counter in the set of training data is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station (see at least [0063]; “In the depicted embodiment of FIG. 1, node (107A) is a wireless router, and the other nodes (107B), (107C) and (107D) are wireless access points. However, this is just one possible configuration. Further, it is not necessary that any given node (107) be a particular type of wireless device. Any number of nodes (107) may comprise a router, access point, beacon, or other type of wireless transceiver.”);
receiving at least one radio frequency characteristic (see at least [0091]; “Generally speaking, as described elsewhere herein, these algorithms include comparing newly gathered signal characteristic profiles (215) to baseline signal characteristic profiles (211) to identify a change and determine whether, based on the nature of the change, the change is indicative of the presence of a human.”) or a performance management counter, (see at least [0063]; “In the depicted embodiment of FIG. 1, node (107A) is a wireless router, and the other nodes (107B), (107C) and (107D) are wireless access points. However, this is just one possible configuration. Further, it is not necessary that any given node (107) be a particular type of wireless device. Any number of nodes (107) may comprise a router, access point, beacon, or other type of wireless transceiver.”);
obtaining the parameter associated with the environmental categorization, by using the machine learning model after the training on the received radio frequency characteristic or the received performance management counter (see at least [0091]; “This determination may be done at least in part using training data developed through machine learning as described elsewhere herein.”); and
sending or initiating the sending of the parameter associated with the environmental categorization of the area obtained using the machine learning model to any one of: an information storage, a communication device, and a network node of the computer system (see at least [0026]; “In another embodiment of the method, the method further comprises: the computer server storing a plurality of historical data records indicative of whether a human was present in the detection area over a period of time, each of the historical data records comprising an indication of the number of humans detected in the detected area and the date and time of when the number of humans was detected in the detection area; and the computer server making the historical data records available to one or more external computer systems via an interface.”) wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area (see at least [0102]; “Since the communication network and the network performing the detection may be the same network, the invention described herein extends the traditional functionality of a communication network to include human detection and/or location sensing without requiring additional sensing hardware.”) and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count (see at least [0104]; “The system as a whole can be used for a wide variety of applications, ranging from occupancy sensing, as might be used for lighting control and/or security, to counting the number of people in a space as might be needed for a heat and/or traffic map, to a system that tracks individual humans moving through a space.”), and a vehicle or pedestrian type.
Nakamura teaches using the signal attenuation between a transmitter and receiver to detect the presence of pedestrians. Wootton similarly teaches using signal absorption and backscatter in a wireless network to detect the presence and position of one or more human bodies, and Wootton trains and uses machine learning to aid in the data analysis. Due to the similarity of the two techniques, it would have been obvious to one of ordinary skill in the art at the time of the claimed invention to adapt the machine learning of Wootton to the method of Nakamura. One of ordinary skill would be motivated to use the machine learning of Wootton in order to improve detection capabilities (see Wootton at least [0111]; “Using machine learning algorithms, the system can improve the accuracy of location predicting algorithms based on the known location from the transceiver.”).
However, neither Nakamura nor Wootton explicitly teach wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system.
Nakamura is directed to congestion degree estimation using propagation loss of wireless communication, and Yang discloses systems and methods for a self-organizing network based on user equipment information. Yang teaches:
wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station (see at least [0123]; “The process of FIG. 8 may include obtaining pathloss information for UE device associated with a base station (block 810). For example, SON system 150 may obtain pathloss information for UE devices 110 associated with base station 130 from base station 130, and/or another device in access network 120, via OSS 160.”).
Nakamura teaches a transmitter and receiver without giving a specific context, and makes calculations and determinations based on the propagation losses between the transmitter and receiver. Yang teaches a self-organizing network of transceivers that makes calculations and determinations based on pathloss information, information which may optionally be obtained via the Operations Support System (OSS). It would have been obvious to one of ordinary skill in the art to obtain the propagation loss information required for the method of Nakamura via an OSS, as taught by Yang. One of ordinary skill would be motivated to acquire information from the OSS to take advantage of the OSS network support functionalities taught by Yang (see at least [0045]; “OSS 160 may include one or more devices, such as computer devices and/or server devices, which perform operations support functions for provider network 140. Operations support functions may include provisioning of services, service delivery and fulfillment, network configuration and management, fault management, and/or other type of functionalities. OSS 160 may collect metric values, such as particular KPIs, associated with UE devices 110 and/or base stations 130. For example, OSS 160 may receive, at particular intervals, metric values associated with UE devices 110 attached to base station 130. OSS 160 may provide the obtained information to SON system 150.”).
Regarding claim 44, Nakamura in view of Wootton and Yang teaches the method according to claim 41. Nakamura further teaches
wherein the received radio frequency characteristic or the performance management counter (see at least [0007]; “The estimation method includes: acquiring received power; calculating propagation losses between the transmitter and the receiver, based on the received power acquired in the acquiring of the received power; calculating a median of a plurality of the propagation losses calculated in the calculating of the propagation losses in a predetermined time period determined in advance, and outputting the median as propagation losses to be used for subsequent processing…”) includes at least one uplink pathloss value (see at least [0006]; “An object of the present invention is to provide an estimation method, an estimation apparatus, and an estimation program that allow for estimation of a congestion degree of humans, vehicles, or the like in a target area by acquiring propagation losses of radio communication in the target area.”).
Regarding claim 46, Nakamura in view of Wootton and Yang teaches the method according to claim 41. Wootton further teaches:
wherein the computer system comprises a first network node for receiving the radio frequency characteristic or performance management counter (see at least [0069]; “In an embodiment, after the baseline signatures have been detected and collected, the detection network (103) will generally continue to operate in the same or similar fashion, but is now able to detect the presence of a biological mass. This is done by detecting and collecting additional signal characteristics, generally in real-time, as the detection network (103) operates in a normal mode of transmitting and receiving data packets. These newly generated real-time signal characteristic profiles are also generally characteristics of signals between two particular nodes (107) in the detection network (103), and thus can be compared to a corresponding baseline signal characteristic profile for the same two particular nodes (107).”);
a second network node for obtaining the parameter associated with the environmental categorization of the area using the machine learning model on the received radio frequency characteristic or received performance management counter (see at least Fig. 1, server 109 taught in claim 1 to host the machine learning capabilities.); and
sending or initiating the sending of the parameter associated with the environmental categorization of the area is performed by either the first network node, the second network node (see at least [0026]; “In another embodiment of the method, the method further comprises: the computer server storing a plurality of historical data records indicative of whether a human was present in the detection area over a period of time, each of the historical data records comprising an indication of the number of humans detected in the detected area and the date and time of when the number of humans was detected in the detection area; and the computer server making the historical data records available to one or more external computer systems via an interface.”), or a third network node comprised in the computer system.
It would have been obvious to combine Nakamura and Wootton for the reasons given regarding claim 41.
Regarding claim 47, Nakamura discloses [Note: what Nakamura fails to disclose is strike-through]:
A computer system (see at least Fig. 1, estimation apparatus 101) comprising one or more network nodes (see at least Fig. 1 showing transmitter and receiver nodes. See also [0007]; “A first aspect of the invention is an estimation method of estimating a congestion degree of blocking entities in a communication region between a transmitter and a receiver performing radio communication.”) for
determining at least one area as a parameter associated with an environment categorization of an area (see at least [0007]; “A first aspect of the invention is an estimation method of estimating a congestion degree of blocking entities in a communication region between a transmitter and a receiver performing radio communication”), at a specified point in time or a specified time interval (see at least [0007]; “The estimation method includes: acquiring received power; calculating propagation losses between the transmitter and the receiver, based on the received power acquired in the acquiring of the received power; calculating a median of a plurality of the propagation losses calculated in the calculating of the propagation losses in a predetermined time period determined in advance, and outputting the median as propagation losses to be used for subsequent processing…”); the network nodes being configured to:
receive at least one radio frequency characteristic (see at least [0007]; “The estimation method includes: acquiring received power…”) or performance management counter,
obtain the parameter associated with the environmental categorization from a (see at least [0024] – [0025]; “FIG. 1 illustrates an example of an estimation model for shadowing losses due to human bodies when a communication region between a transmitter Tx and a receiver Rx is blocked by a plurality of human bodies…In the example of FIG. 1, the propagation losses vary due to the traffic of a plurality of humans between the transmitter Tx and the receiver Rx, and the received power in the receiver Rx thus varies as well. In view of this, in the present embodiment, the estimation apparatus 101 acquires the received power in the receiver Rx, and calculates the propagation losses between the transmitter Tx and the receiver Rx. Then, the estimation apparatus 101 calculates a distance from the blocking entities based on the propagation losses, and calculates a congestion degree in the target area.”) or the received performance management counter; and
least one traffic congestion parameter (see at least [0006]; “An object of the present invention is to provide an estimation method, an estimation apparatus, and an estimation program that allow for estimation of a congestion degree of humans, vehicles, or the like in a target area by acquiring propagation losses of radio communication in the target area.”), wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area (see at least [001]; “The present invention relates to a technique of estimating a degree of congestion of humans, vehicles, or the like in a measurement environment by acquiring propagation losses of radio communication.”) and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count (see at least [0052]; “FIG. 6 illustrates an example of blocking entities detected in chronological order. Here, in FIG. 6, the congestion degree calculation unit 206 acquires the number of blocking entities in a predetermined range (rectangular box region in FIG. 6) with the same distance from the transmitter Tx or the receiver Rx.”), and a vehicle or pedestrian type.
However, Nakamura does not teach using a machine learning model, nor does Nakamura explicitly teach wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system, and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station and send or initiate the sending of the parameter associated with the environmental categorization of the area obtained using the machine learning model to any one of at least one information storage, a communication device, and a network node of the computer system.
Nakamura is directed to congestion degree estimation using propagation loss of wireless communication, and Wootton discloses detecting the presence of a biological mass in a communications network. Wootton teaches:
obtain the parameter associated with the environmental categorization from a machine learning model that has been applied on the received radio frequency characteristic (see at least [0091]; “Generally speaking, as described elsewhere herein, these algorithms include comparing newly gathered signal characteristic profiles (215) to baseline signal characteristic profiles (211) to identify a change and determine whether, based on the nature of the change, the change is indicative of the presence of a human. This determination may be done at least in part using training data developed through machine learning as described elsewhere herein.”) or the received performance management counter; and
send or initiate the sending of the parameter associated with the environmental categorization, of the area, to any one of at least one information storage, a communication device, and a network node of the computer system (see at least [0102] – [0103]; “In an embodiment, a detection network (103) implementing the systems and methods described herein may further comprise elements for taking action (219) based on the detected presence and/or location of a human. This may be done, for example, by sending control signals over the network using the computers to first determine the presence and/or location of a human on the network, and then to determine an action to take based on the presence and/or location of a human on the network, and to send a message over that network to take that action…The computer elements on the network necessarily perform additional calculations and may craft communication signals. This may ease the calculation burden on the computers; however, the network may still function as a command and control network, independently of the network as a detection network.”).
Wootton furthermore teaches:
wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station (see at least [0063]; “In the depicted embodiment of FIG. 1, node (107A) is a wireless router, and the other nodes (107B), (107C) and (107D) are wireless access points. However, this is just one possible configuration. Further, it is not necessary that any given node (107) be a particular type of wireless device. Any number of nodes (107) may comprise a router, access point, beacon, or other type of wireless transceiver.”).
Nakamura teaches using the signal attenuation between a transmitter and receiver to detect the presence of pedestrians. Wootton similarly teaches using signal absorption and backscatter in a wireless network to detect the presence and position of one or more human bodies, and Wootton trains and uses machine learning to aid in the data analysis. Due to the similarity of the two techniques, it would have been obvious to one of ordinary skill in the art at the time of the claimed invention to adapt the machine learning of Wootton to the method of Nakamura. One of ordinary skill would be motivated to use the machine learning of Wootton in order to improve detection capabilities (see Wootton at least [0111]; “Using machine learning algorithms, the system can improve the accuracy of location predicting algorithms based on the known location from the transceiver.”).
However, neither Nakamura nor Wootton explicitly teach wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system.
Nakamura is directed to congestion degree estimation using propagation loss of wireless communication, and Yang discloses systems and methods for a self-organizing network based on user equipment information. Yang teaches:
wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station (see at least [0123]; “The process of FIG. 8 may include obtaining pathloss information for UE device associated with a base station (block 810). For example, SON system 150 may obtain pathloss information for UE devices 110 associated with base station 130 from base station 130, and/or another device in access network 120, via OSS 160.”).
Nakamura teaches a transmitter and receiver without giving a specific context, and makes calculations and determinations based on the propagation losses between the transmitter and receiver. Yang teaches a self-organizing network of transceivers that makes calculations and determinations based on pathloss information, information which may optionally be obtained via the Operations Support System (OSS). It would have been obvious to one of ordinary skill in the art to obtain the propagation loss information required for the method of Nakamura via an OSS, as taught by Yang. One of ordinary skill would be motivated to acquire information from the OSS to take advantage of the OSS network support functionalities taught by Yang (see at least [0045]; “OSS 160 may include one or more devices, such as computer devices and/or server devices, which perform operations support functions for provider network 140. Operations support functions may include provisioning of services, service delivery and fulfillment, network configuration and management, fault management, and/or other type of functionalities. OSS 160 may collect metric values, such as particular KPIs, associated with UE devices 110 and/or base stations 130. For example, OSS 160 may receive, at particular intervals, metric values associated with UE devices 110 attached to base station 130. OSS 160 may provide the obtained information to SON system 150.”).
Regarding claim 48, Nakamura in view of Wootton and Yang teaches the computer system according to claim 47. Wootton further teaches:
comprising a first network node for receiving the radio frequency characteristic or performance management counter (see at least [0069]; “In an embodiment, after the baseline signatures have been detected and collected, the detection network (103) will generally continue to operate in the same or similar fashion, but is now able to detect the presence of a biological mass. This is done by detecting and collecting additional signal characteristics, generally in real-time, as the detection network (103) operates in a normal mode of transmitting and receiving data packets. These newly generated real-time signal characteristic profiles are also generally characteristics of signals between two particular nodes (107) in the detection network (103), and thus can be compared to a corresponding baseline signal characteristic profile for the same two particular nodes (107).”),
a second network node for obtaining the parameter associated with the environmental categorization of the area using the machine learning model on the received radio frequency characteristic or performance management counter (see at least Fig. 1, server 109 taught in claim 1 to host the machine learning capabilities.).
It would have been obvious to combine Nakamura and Wootton for the reasons given regarding claim 41.
Regarding claim 49, Nakamura in view of Wootton and Yang teaches the computer system according to claim 48. Wootton further teaches:
wherein the sending or the initiation of the sending of the transmission of the obtained parameter associated with the environmental categorization is performed in either the first network node, the second network node (see at least [0026]; “In another embodiment of the method, the method further comprises: the computer server storing a plurality of historical data records indicative of whether a human was present in the detection area over a period of time, each of the historical data records comprising an indication of the number of humans detected in the detected area and the date and time of when the number of humans was detected in the detection area; and the computer server making the historical data records available to one or more external computer systems via an interface.”), or a third network node.
It would have been obvious to combine Nakamura and Wootton for the reasons given regarding claim 41.
Regarding claim 52, Nakamura in view of Wootton and Yang teaches the computer system according to claim 47. Nakamura further teaches:
wherein the received radio frequency characteristic or the performance management counter (see at least [0007]; “The estimation method includes: acquiring received power; calculating propagation losses between the transmitter and the receiver, based on the received power acquired in the acquiring of the received power; calculating a median of a plurality of the propagation losses calculated in the calculating of the propagation losses in a predetermined time period determined in advance, and outputting the median as propagation losses to be used for subsequent processing…”) includes at least one uplink pathloss value (see at least [0006]; “An object of the present invention is to provide an estimation method, an estimation apparatus, and an estimation program that allow for estimation of a congestion degree of humans, vehicles, or the like in a target area by acquiring propagation losses of radio communication in the target area.”).
Regarding claim 55, Nakamura in view of Wootton and Yang teaches the computer system according to claim 47. Wootton further teaches:
wherein the specified point in time is a current point in time (see at least [0069] – [0070]; “In an embodiment, after the baseline signatures have been detected and collected, the detection network (103) will generally continue to operate in the same or similar fashion, but is now able to detect the presence of a biological mass. This is done by detecting and collecting additional signal characteristics, generally in real-time, as the detection network (103) operates in a normal mode of transmitting and receiving data packets. These newly generated real-time signal characteristic profiles are also generally characteristics of signals between two particular nodes (107) in the detection network (103), and thus can be compared to a corresponding baseline signal characteristic profile for the same two particular nodes (107). A statistically significant difference in certain characteristics between the two profiles may then be interpreted as being caused by the presence of a significant biological mass, such as a human. The comparison operations may be performed by appropriate hardware in a given node (107), or the real-time signal characteristic profiles may be transmitted to a server (109) for processing and comparison. In a further embodiment, both are done so that a copy of the real-time data is also stored and accessible via the server, effectively providing a history of signal characteristic profiles.” See also [0049] for a definition of “real time”.).
It would have been obvious to combine Nakamura and Wootton for the reasons given regarding claim 41.
Regarding claim 56, Nakamura in view of Wootton and Yang teaches the computer system according to claim 47. Wootton further teaches:
wherein the specified time interval is any interval of a number of minutes and/or a number of hours (see at least [0037]; “In another embodiment of the method, the method further comprises: the computer server storing a plurality of historical data records indicative of whether a human was present in the detection area over a period of time, each of the historical data records comprising an indication of the number of humans detected in the detected area and the date and time of when the number of humans was detected in the detection area…” Examiner notes that any period of time may be expressed as a number of minutes or hours.).
It would have been obvious to combine Nakamura and Wootton for the reasons given regarding claim 41.
Regarding claim 57, Nakamura in view of Wootton and Yang teaches the computer system according to claim 47. Wootton further teaches:
wherein determining the representation of the area as the parameter associated with the environmental categorization of the area further determines a forecast of the area as a second set of parameters associated with a second environmental categorization of the area (see at least [0111]; “Similarly, machine learning can continue to improve the detection and false alarm rate. By way of example and not limitation, data concerning prior traffic patterns at a facility can be used to establish defaults, presumptions, or expectations concerning the range of times or days during which a particular facility is generally occupied or generally empty. Such data can be used by the system to improve its performance.”).
It would have been obvious to combine Nakamura and Wootton for the reasons given regarding claim 41.
Regarding claim 58, Nakamura in view of Wootton and Yang teaches the computer system according to claim 57. Wootton further teaches:
wherein the forecast comprises a specific time interval, wherein the specific time interval is any interval of a number of minutes and/or a number of hours (see at least [0111]; “Similarly, machine learning can continue to improve the detection and false alarm rate. By way of example and not limitation, data concerning prior traffic patterns at a facility can be used to establish defaults, presumptions, or expectations concerning the range of times or days during which a particular facility is generally occupied or generally empty. Such data can be used by the system to improve its performance.” Examiner notes that ranges of times and days can be expressed as a number of minutes or hours.).
It would have been obvious to combine Nakamura and Wootton for the reasons given regarding claim 41.
Regarding claim 59, Nakamura discloses [Note: what Nakamura fails to disclose is strike-through]:
A computer program product comprising a computer program for a computer system (see at least [0034]; “Here, the estimation apparatus 101 according to the present embodiment is described as an apparatus including each of the blocks illustrated in FIG. 2. However, the estimation apparatus 101 can be also implemented by a computer that executes a program corresponding to the processing performed by each of the blocks.”) for determining at least one parameter associated with an environmental categorization of an area (see at least [0007]; “A first aspect of the invention is an estimation method of estimating a congestion degree of blocking entities in a communication region between a transmitter and a receiver performing radio communication”), the environmental categorization representing the area at a specified point in time or a specified time interval (see at least [0007]; “The estimation method includes: acquiring received power; calculating propagation losses between the transmitter and the receiver, based on the received power acquired in the acquiring of the received power; calculating a median of a plurality of the propagation losses calculated in the calculating of the propagation losses in a predetermined time period determined in advance, and outputting the median as propagation losses to be used for subsequent processing…”), and a nontransitory computer readable means on which the computer program is stored, wherein the computer program comprises computer program code, which, when run on the computer system, causes the computer system to perform the steps of (see at least [0034]; “However, the estimation apparatus 101 can be also implemented by a computer that executes a program corresponding to the processing performed by each of the blocks. Note that the program may be provided being recorded on a recording medium, or may be provided via a network.”):
receiving at least one radio frequency characteristic (see at least [0007]; “The estimation method includes: acquiring received power…”) or a performance management counter
obtaining, by using the received radio frequency characteristic or performance management counter, and a environmental categorization of the area (see at least [0024] – [0025]; “FIG. 1 illustrates an example of an estimation model for shadowing losses due to human bodies when a communication region between a transmitter Tx and a receiver Rx is blocked by a plurality of human bodies…In the example of FIG. 1, the propagation losses vary due to the traffic of a plurality of humans between the transmitter Tx and the receiver Rx, and the received power in the receiver Rx thus varies as well. In view of this, in the present embodiment, the estimation apparatus 101 acquires the received power in the receiver Rx, and calculates the propagation losses between the transmitter Tx and the receiver Rx. Then, the estimation apparatus 101 calculates a distance from the blocking entities based on the propagation losses, and calculates a congestion degree in the target area.”), and
(see at least [0006]; “An object of the present invention is to provide an estimation method, an estimation apparatus, and an estimation program that allow for estimation of a congestion degree of humans, vehicles, or the like in a target area by acquiring propagation losses of radio communication in the target area.”), wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion or pedestrian traffic congestion in the area (see at least [001]; “The present invention relates to a technique of estimating a degree of congestion of humans, vehicles, or the like in a measurement environment by acquiring propagation losses of radio communication.”) and the traffic congestion parameter is at least any one of a vehicle or pedestrian flow rate, a vehicle or pedestrian count (see at least [0052]; “FIG. 6 illustrates an example of blocking entities detected in chronological order. Here, in FIG. 6, the congestion degree calculation unit 206 acquires the number of blocking entities in a predetermined range (rectangular box region in FIG. 6) with the same distance from the transmitter Tx or the receiver Rx.”), and a vehicle or pedestrian type.
However, Nakamura does not teach using a machine learning model, nor does Nakamura explicitly teach wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system, and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station and sending or initiating the sending of the parameter associated with the environmental categorization of the area obtained using the machine learning model to any one of at least one information storage, a communication device, and a network node of the computer system.
Nakamura is directed to congestion degree estimation using propagation loss of wireless communication, and Wootton discloses detecting the presence of a biological mass in a communications network. Wootton teaches:
obtaining, by using the received radio frequency characteristic or performance management counter, and a machine learning model, the parameter associated with the environmental categorization of the area (see at least [0091]; “Generally speaking, as described elsewhere herein, these algorithms include comparing newly gathered signal characteristic profiles (215) to baseline signal characteristic profiles (211) to identify a change and determine whether, based on the nature of the change, the change is indicative of the presence of a human. This determination may be done at least in part using training data developed through machine learning as described elsewhere herein.”) or the received performance management counter; and
sending or initiating the sending of the parameter associated with the environmental categorization, of the area, to any one of at least one information storage, a communication device, and a network node of the computer system (see at least [0102] – [0103]; “In an embodiment, a detection network (103) implementing the systems and methods described herein may further comprise elements for taking action (219) based on the detected presence and/or location of a human. This may be done, for example, by sending control signals over the network using the computers to first determine the presence and/or location of a human on the network, and then to determine an action to take based on the presence and/or location of a human on the network, and to send a message over that network to take that action…The computer elements on the network necessarily perform additional calculations and may craft communication signals. This may ease the calculation burden on the computers; however, the network may still function as a command and control network, independently of the network as a detection network.”).
Wootton furthermore teaches:
wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station (see at least [0063]; “In the depicted embodiment of FIG. 1, node (107A) is a wireless router, and the other nodes (107B), (107C) and (107D) are wireless access points. However, this is just one possible configuration. Further, it is not necessary that any given node (107) be a particular type of wireless device. Any number of nodes (107) may comprise a router, access point, beacon, or other type of wireless transceiver.”).
Nakamura teaches using the signal attenuation between a transmitter and receiver to detect the presence of pedestrians. Wootton similarly teaches using signal absorption and backscatter in a wireless network to detect the presence and position of one or more human bodies, and Wootton trains and uses machine learning to aid in the data analysis. Due to the similarity of the two techniques, it would have been obvious to one of ordinary skill in the art at the time of the claimed invention to adapt the machine learning of Wootton to the method of Nakamura. One of ordinary skill would be motivated to use the machine learning of Wootton in order to improve detection capabilities (see Wootton at least [0111]; “Using machine learning algorithms, the system can improve the accuracy of location predicting algorithms based on the known location from the transceiver.”).
However, neither Nakamura nor Wootton explicitly teach wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system.
Nakamura is directed to congestion degree estimation using propagation loss of wireless communication, and Yang discloses systems and methods for a self-organizing network based on user equipment information. Yang teaches:
wherein the received radio frequency characteristic or performance management counter is received from an operation support system, or a business support system and wherein the received radio frequency characteristic or performance management counter is derived from radio waves of signals propagated from at least one wireless communication device to a radio base station (see at least [0123]; “The process of FIG. 8 may include obtaining pathloss information for UE device associated with a base station (block 810). For example, SON system 150 may obtain pathloss information for UE devices 110 associated with base station 130 from base station 130, and/or another device in access network 120, via OSS 160.”).
Nakamura teaches a transmitter and receiver without giving a specific context, and makes calculations and determinations based on the propagation losses between the transmitter and receiver. Yang teaches a self-organizing network of transceivers that makes calculations and determinations based on pathloss information, information which may optionally be obtained via the Operations Support System (OSS). It would have been obvious to one of ordinary skill in the art to obtain the propagation loss information required for the method of Nakamura via an OSS, as taught by Yang. One of ordinary skill would be motivated to acquire information from the OSS to take advantage of the OSS network support functionalities taught by Yang (see at least [0045]; “OSS 160 may include one or more devices, such as computer devices and/or server devices, which perform operations support functions for provider network 140. Operations support functions may include provisioning of services, service delivery and fulfillment, network configuration and management, fault management, and/or other type of functionalities. OSS 160 may collect metric values, such as particular KPIs, associated with UE devices 110 and/or base stations 130. For example, OSS 160 may receive, at particular intervals, metric values associated with UE devices 110 attached to base station 130. OSS 160 may provide the obtained information to SON system 150.”).
Claims 45 and 53 are rejected under 35 U.S.C. 103 as being unpatentable over Nakamura in view of Wootton and Yang, further in view of Chandrasekhar et al. (US-20190277957-A1; hereinafter, Chandrasekhar).
Regarding claim 45, Nakamura in view of Wootton and Yang teaches the method according to claim 41. However, Nakamura does not teach:
wherein the received radio frequency characteristic or the performance management counter includes at least one doppler shift spread value.
Nakamura is directed to congestion degree estimation using propagation loss of wireless communication, and Chandrasekhar is directed to estimations of the mobility of mobile client devices using measurements of signals from those devices. Chandrasekhar teaches:
wherein the received radio frequency characteristic (see at least [0004]; “This disclosure provides an Artificial Intelligence assisted approach to categorize or determine the speed of a UE based on uplink SRS channel measurement inputs.”) is from radio frequency characteristics of radio waves of signals (see at least [0003]; “Knowledge of either the terminal speed or the category in which the terminal speed falls is vital for optimizing various radio resource management functions such as handover, mobility load balancing, and transmission scheduling at the network.”) propagated from at least one wireless communication device (see at least Abs; “An apparatus for performing a wireless communication includes a communication interface configured to measure uplink (UL) Sounding Reference Signals (SRSs) transmitted from a mobile client device…”) to a radio base station (see at least Fig. 1, base station 102 receives signals).
Nakamura teaches a transmitter and receiver without giving a specific context, and makes calculations and determinations based on the propagation losses between the transmitter and receiver. Wootton similarly teaches using signal absorption and backscatter in a wireless network to detect the presence and position of one or more human bodies, and Wootton uses machine learning to enhance the classification process and enhancing location predictions using machine learning algorithms (see [0111]). Wootton furthermore teaches that these methods may be enhanced by detecting and locating a mobile transceiver device such as a mobile phone (see [0109]). In light of Wootton’s teachings that measurements of a mobile phone may enhance the method, it would have been obvious to one of ordinary skill in the art to modify the detection mechanism of Nakamura/Wootton to incorporate teachings of Chandrasekhar, who estimates mobility of a mobile client device by feeding measurements of uplink Sounding Reference Signals to a machine learning classifier. Wootton predicts the path of tracked targets (see [0093] and [0111]), and Chandrasekhar estimates a UE speed based on Doppler power spectrum measurements (see [0035]). It would have been obvious to modify the path tracking of Wootton to incorporate Doppler power spectrum measurements, as taught by Chandrasekhar. Such a modification would allow for speed estimation relevant to the prediction of the path of tracked targets, as performed by Wootton.
Regarding claim 53, Nakamura in view of Wootton and Yang teaches the computer system according to claim 47. However, Nakamura does not teach:
wherein the received radio frequency characteristic or the performance management counter includes at least one doppler shift spread value.
Chandrasekhar teaches:
wherein the received radio frequency characteristic or the performance management counter (see at least [0004]; “This disclosure provides an Artificial Intelligence assisted approach to categorize or determine the speed of a UE based on uplink SRS channel measurement inputs.”) includes at least one doppler shift spread value (see at least [0035]; “Mobile speed classification methods described herein rely on obtaining the speed class through estimating the Doppler spread of the underlying mobile radio channel.”).
Nakamura teaches a transmitter and receiver without giving a specific context, and makes calculations and determinations based on the propagation losses between the transmitter and receiver. Wootton similarly teaches using signal absorption and backscatter in a wireless network to detect the presence and position of one or more human bodies, and Wootton uses machine learning to enhance the classification process and enhancing location predictions using machine learning algorithms (see [0111]). Wootton furthermore teaches that these methods may be enhanced by detecting and locating a mobile transceiver device such as a mobile phone (see [0109]). In light of Wootton’s teachings that measurements of a mobile phone may enhance the method, it would have been obvious to one of ordinary skill in the art to modify the detection mechanism of Nakamura/Wootton to incorporate teachings of Chandrasekhar, who estimates mobility of a mobile client device by feeding measurements of uplink Sounding Reference Signals to a machine learning classifier. Wootton predicts the path of tracked targets (see [0093] and [0111]), and Chandrasekhar estimates a UE speed based on Doppler power spectrum measurements (see [0035]). It would have been obvious to modify the path tracking of Wootton to incorporate Doppler power spectrum measurements, as taught by Chandrasekhar. Such a modification would allow for speed estimation relevant to the prediction of the path of tracked targets, as performed by Wootton.
Conclusion
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/ASHLEY BROWN RAYNAL/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648