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 office action is in response to the Applicant’s communication filed on 01/22/2026. Claims 1 – 20 are currently pending in this application.
The applicant’s arguments have been considered but are moot in view of new ground(s) of rejections necessitated by the applicant’s amendment.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 – 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 8 and 15, as amended, each recites the following limitations:
“capturing activity data from the physical environment;
identifying an event of interest in the activity data, wherein the event of interest comprises an interference between two or more of the spectrum zones...”
As may be seen, the claim requires the event of interest, being an interference, to be identified from the activity data which, in turn, is captured from the physical environment.
The Applicant states that support for the amendment may be found in paragraph 0041. However, paragraph 0041 does not seem to provide support for this sequence of steps as required by the claim. Here is the most relevant portion of paragraph 0041 (emphasis by the Examiner):
“In another embodiment of this invention, events of interest may be determined from the spectrum zone configuration in tandem with an understanding of the physical environment.” …
In another example, an understanding of the current spectrum configuration at specific moments may represent an event of interest. For instance, if two zones are in close proximity such that interference may result, the wireless network controller, e.g., RIC, can receive an alert which may shift the location of one or more zones to improve the overall performance of the wireless network.
As may be seen from this paragraph, the event of interest, such as interference, is not identified from the physical environment or activity in the physical environment. In fact, the event of interest, such as interference, is determined from the current spectrum configuration which is described as something “in tandem” with the physical environment, but not because of it. Thus, according to the specification as filed, the event of interest, such as interference, is not determined from the activity data from the physical environment. It is determined from the current spectrum configuration.
Therefore, the examiner considers claims 1, 8 and 15 as containing new matter. To overcome this rejection, the applicant is required to point out the exact place in the specification as filed which would provide support for the subject matter of claims 1, 8 and 15 as explained above, or to amend the claims to bring them in conformance with the specification.
Claims 2 – 7, 9 – 14 and 16 – 20 are rejected as being dependent from the rejected base claims.
Claims 1 – 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
As explained above, independent claims 1, 8 and 15, as amended, each recites the following limitations:
“capturing activity data from the physical environment;
identifying an event of interest in the activity data, wherein the event of interest comprises an interference between two or more of the spectrum zones...”
As may be seen, the claim requires the event of interest, being an interference, to be identified from the activity data which, in turn, is captured from the physical environment.
The specification as filed does not appear to have any enablement for the feature of identifying the event of interest, being an interference, from the activity data in the physical environment. Thus, a person of ordinary skill in the art trying to implement the invention will not be able to do so without undue experimentation.
Claims 2 – 7, 9 – 14 and 16 – 20 are rejected as being dependent from the rejected base claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 6 – 8, 13 – 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200396645 (GRINSHPUN) in view of US 20190222339 (Badic).
Regarding claims 1, 8 and 15, GRINSHPUN teaches “A computer-implemented method for providing differentiated services to spectrum zones based on user activity (FIG 3 with corresponding description), the method comprising:
obtaining a current spectrum zone configuration for a physical environment from a server (paragraphs 0077 – 0081: recited by this limitation “a server” correspond to Network Data Analysis (NDA) agents 40. Particularly, NDA agents 40 extract the Key Performance Indicator data on a: per cell basis e.g. cell id coupled with geographic location, overall cell traffic volume, overall number of active sessions, cell congestion level number of handovers to/from the cell/sector coupled with the respective flow ids, per flow, e.g. flow ids together with wireless flow characteristics, application type and behavioral characteristics that can be derived from such flow characteristics, traffic volume per slice data for the existing slices, including respective flows of the slice, per mobile device data (e.g. mobility information and running application data). The processor of the NDA agents 40 process this data by applying programmed filters and send (Step 2) the processed data digests together with the respective NDA agent ID to the processor 210 to be saved in the Network Anomaly Detection Module 10c. this last operation correspond to the recited “obtaining a current spectrum zone configuration for a physical environment”);
capturing activity data from the physical environment (paragraph 0076: The processor of the Video Analysis (VA) agents 30 receives and preprocess data from cameras 20a/20b, where the cameras 20a/20b may include a street video surveillance camera, a mobile camera mounted on car dashboard, a mobile camera mounted on drone, a body camera of law enforcement officers, etc., and send the preprocessed data to the processor 210 to be processed using instructions in the AI Video Anomaly Detection module 10a within ANSDM 210a. Each VA agent 30 receives raw video data from the camera, decodes it and computes Scene Activity Vectors (Dwell, Density, Direction, Velocity) for individual pixels and averaged over one or more cropped areas of the video frame. In step (2) the processor of the VA agents 30 periodically (e.g. every 1 second) sends the respective computed data to the processor 210 of node 200 together with an agent identifier.);
identifying an event of interest in the activity data…” “…wherein the two or more of the spectrum zones have network requirements (paragraph 0082: The processor 210 applies pattern recognition and clustering technique to the received averaged Scene Activity Vector values to detect video anomaly in each VA agent 30 report. The processor 210 then combines processed data from multiple VA agents 30 to determine if Video Anomaly is detected. Upon video anomaly detection (“an event of interest in the activity data”) the processor 210 sends video anomaly trigger (step 3) to the Event Detection Module 10b within the ANSDM 210a. Paragraph 0083: The processor 210 also performs network anomaly detection, based upon unusual patterns in traffic volume, mobility (e.g. may be large number of people running away from the burning stadium instead of moving towards it), high number of voice calls, unusual number of video uploads and social network chatting. Upon network anomaly detection the processor 210 sends network anomaly trigger (step 3) to the Event Detection Module 10b of ANSDM 210a. Paragraph 0060: Identifying network resources needed for the new slice, based upon the identified in (a) type of the event and identified in (b) scope of the event. Since a new slice is to be created in addition to already existing, this represents “the two or more of the spectrum zones” are either already present or will be present. Also paragraphs 0079 – 0080: the existing slices (in plural). They also at least implicitly have “network requirements”. Paragraph 0043 lists “network requirements” for slices explicitly: A network slice is a complete end-to-end virtual network with associated service level guarantees such as session reliability, available network throughput, end-to-end latency, simultaneous number of sessions, etc.);
determining that the network requirements of the event of interest are not satisfied by the current spectrum zone configuration for the physical environment (paragraph 0084: The processor 210 reacts to the triggers for both Video and Network Anomaly in the same geographic area unit. When the event requiring dynamic Network Slice instantiation is detected by the processor 210 using instructions from the Event Detection Module 10b, the processor 210 sends a trigger with an area unit identifier and event type identifier to the Slice Dimensioning Module 10d of ANSDM 210a. Determination that dynamic Network Slice instantiation is required means that “the network requirements of the event of interest are not satisfied by the current spectrum zone configuration”. Paragraph 0060: Identifying network resources needed for the new slice, based upon the identified in (a) type of the event and identified in (b) scope of the event. Par. 0068.)…”
GRINSHPUN does not disclose “wherein the event of interest comprises an interference between two or more of the spectrum zones”, that the network requirements are not satisfied “due to the interference; and performing an action selected from a group consisting of changing a location of or a frequency band of the two or more of the spectrum zones to accommodate the network requirements.”
Badic in FIG 3 and paragraph 0050 teaches a block diagram of a network infrastructure 301 supporting various census tracts including a plurality of neighboring cells, such as cells 310A-G, that are associated with a census tract to use specific spectrum. These individual cells or census tracts correspond to individual spectrum zones of instant claim. Paragraph 0048: the interference management system 225 of SAS 220 collects data on communication interference by interacting with network equipment as well as UEs 270-274 to receive interference measurements based on various interference events. Paragraph 0049: Based on the obtained measurements or other information, such as current network configuration setting, provided by the components, the interference management system 225 identifies interference-free channels and creates SAS protection bands that can be used for interference-free transmissions within system 200. The protection bands are used to protect equipment used in neighboring cells from interfering with each other. Paragraph 0053: an SAS entity may select suitable frequency blocks as well as PAL or GAA usage time slots for specific cell-edges to mitigate interference therein between. Based on the obtained measurements the SAS identifies interference-free channels and creates SAS protection bands which can be used for interference-free transmission. Paragraph 0055: the configuration data assigns interference-free channels to equipment in a census tract experiencing severe interference. Paragraph 0056: the SAS may assign a LSA frequency for each cell edge to use in a particular protection band. The protection bands 330-335 are arranged so that each cell is on a different non-overlapping LSA frequency to protect the bands from interfering with each other. Paragraph 0060: the SAS may identify interfering events through certain measurements and the corresponding geographic areas where interference events are substantial (“interfering zone”). In the “interfering zone”, the SAS may apply a different spectrum allocation by allocating a different PAL or GAA slot.
In other words, Badic teaches “identifying an event of interest”, “wherein the event of interest comprises an interference between two or more of the spectrum zones” (represented by the census tracts and/or individual cells), “and performing an action selected from a group consisting of changing … a frequency band of the two or more of the spectrum zones to accommodate the network requirements”, the network requirements being minimal interference, and claimed changing “a frequency band” corresponding to allocation of different spectrum and/or interference free channels within the network/spectrum slice/zone.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by Badic interference management techniques between different spectrum zones by assigning different spectrum (“frequency band”), in the system of GRINSHPUN. Doing so would have not only allowed to manage network/spectrum slice/zone creation based on the detected activity in the physical environment, as in GRINSHPUN, but also to manage spectrum allocation within the slices/zones based on the detected interference events between different slices/zones.
Regarding claims 6, 13 and 20, GRINSHPUN teaches “wherein the capturing the activity data in the physical environment further comprises: monitoring the physical environment with a camera (paragraph 0058: video anomaly analysis using live video streams from static video surveillance cameras as well as optional dashboard cameras of the vehicles. Also paragraphs 0064 and 0076); and identifying a visual cue in the physical environment, wherein the visual cue is selected from a group consisting of: a user movement, a movement of an object (paragraph 0064: Video anomaly detection in individual camera 20a/20b feeds may be based upon Deep Neural Network analysis of video feeds from individual cameras. Input data may be in the form of Scene Activity Vectors (Dwell, Density, Direction, Velocity) computed for individual pixels and averaged over one or more selected cropped areas of the video frame. Disclosed Scene Activity Vectors including Direction and Velocity correspond to movements of either users or objects in the scene through the pixels in the picture representing the scene. Also par. 0066), and text displayed in captured video.”
Regarding claims 7 and 14, GRINSHPUN teaches “wherein the network requirements include an increase in consumption of network resources above a threshold (paragraphs 0045 – 0046 explicitly disclose “increase in consumption of network resources” associated with certain events. Paragraph 0083: network anomaly detection, based upon unusual patterns in traffic volume, mobility, high number of voice calls, unusual number of video uploads and social network chatting. This all represents “increase in consumption of network resources”. Although GRINSHPUN does not explicitly mention any comparison with a threshold so that the increase is “above a threshold”, in the previous office action the Examiner took an Official Notice that comparing a parameter with a threshold to determine a course of action to take in case the parameter is above the threshold is well known in the art. Since the applicant failed to properly traverse the Official Notice, this common knowledge or well-known in the art statement is now taken to be admitted prior art. Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize some form of threshold or thresholds to which such parameters as traffic volume, mobility, the number of voice calls and video uploads and/or social network chatting are compared to be able to determine the further course of the action depending on whether the parameter is below or above the corresponding threshold), wherein the increase is caused by one or more of: a scheduled event and user activity (see paragraphs 0045 – 0046 which disclose certain sporting, social and entertainment events which represent both “a scheduled event and user activity” that require additional network resources).”
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200396645 (GRINSHPUN) in view of US 20190222339 (Badic) as applied to claims 1, 8 and 15 above, and further in view of US 20220101556 (Zhang).
Regarding claims 2, 9 and 16, GRINSHPUN teaches “wherein a machine learning model that classifies detected human activity…” “…is additionally used to identify the event of interest in the activity data (paragraph 0052: The system 100 dynamically instantiates/deploys network slice resources based upon an Artificial Intelligence (AI) Engine which necessarily includes “a machine learning model”. Paragraphs 0057 – 0058: Autonomic and automatic AI engine functions consist of: dynamically in real time detecting public event that requires deployment of a new dynamic slice, using a combination of video anomaly analysis (live video streams from static video surveillance cameras as well as optional dashboard cameras of the vehicles) and Network anomaly analysis (using cell, flow, slice, and device data exposed for analysis by analytics agents). Paragraph 0083: detection of large number of people running away from the burning stadium instead of moving towards it. Paragraph 0085: determination of the scope of the Public Safety Event (e.g. small (car accident) vs medium (building fire), vs bigger event (affecting multiple streets or city block) vs even bigger (affecting multiple city blocks) representing identification of “the event of interest in the activity data”.).”
GRINSHPUN does not teach that the learning model is “based on importance.” In GRINSHPUN, the information on the event is collected by using plurality of cameras creating images and/or videos.
On the other side, Zhang teaches computer automated interactive activity recognition (see abstract). Paragraphs 0034 – 0035 disclose Graph Convolutional Neural Network (GCN) that extracts spatial-temporal features that can be used to train a classification model of the interactive activity classification module 220. GCNs provide an image classification method including a neural network architecture for machine learning on graphs. The CNN model extracts the most important information from the sequence of image frames 240 to classify the sequence (i.e. “based on importance”), the GCN model passes a filter over the graph, looking for essential vertices and edges (i.e., keypoints) that can help classify nodes within the graph. The result from the GCN model includes final object and human keypoints that will be used as input for the classification model of the interactive activity classification module 220.
In other words, Zhang teaches “a machine learning model that classifies detected human activity based on importance”.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by Zhang machine learning model to classify detected human activity based on importance, in the system of GRINSHPUN to further improve detection of the people location and/or movement. Doing so would have allowed to better understand the behavior of people in pictures or videos (see Zhang, paragraph 0002), such as those taken by the cameras of GRINSHPUN.
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200396645 (GRINSHPUN) in view of US 20190222339 (Badic) as applied to claims 1, 8 and 15 above, and further in view of US 20230370887 (Parker).
Regarding claims 3, 10 and 17, GRINSHPUN teaches “wherein a machine learning model…” “…is additionally used to determine that the network requirements are not satisfied by the current spectrum zone configuration (paragraph 0052: The system 100 dynamically instantiates/deploys network slice resources based upon an Artificial Intelligence (AI) Engine which necessarily includes “a machine learning model”. Paragraphs 0057, 0060 and 0061: Autonomic and automatic AI engine functions consist of: Identifying network resources needed for the new slice, based upon the identified in (a) type of the event and identified in (b) scope of the event. Sending to a slice manager a trigger to create a new slice together with the identified RAN and Core resources to be utilized. Paragraphs 0067 – 0068: Based upon the event location and scope, AI engine 10 performs assessment of exact network resources in the area that would be needed to handle the event. Since the new slice is to be created and additional network resources would be needed to handle the event, it means that at least implicitly it is determined “that the network requirements are not satisfied by the current spectrum zone configuration.”).”
GRINSHPUN does not teach that the machine learning model also “predicts an impact of a modification to a wireless network.”
Parker teaches “a machine learning model (paragraph 0035: A management data analytics function (MDAF) may monitor performance of the network configuration with respect to the intent to predict a deficiency. The MDAF may include a machine-learning model) that predicts an impact of a modification to a wireless network is used to determine that the network requirements are not satisfied by the current spectrum zone configuration (abstract: A network management system receives an intent for a network slice constituent. The network management system configures computing resources for the network slice constituent to satisfy the intent based on expected performance of the computing resources. Paragraph 0035: A management data analytics function (MDAF) may monitor performance of the network configuration with respect to the intent to predict a deficiency (maps to “the network requirements are not satisfied”). For example, a deficiency may be predicted based on a likelihood that the network configuration will not satisfy the intent. The network management function may modify the configuration of the computing resources (maps to “the current spectrum zone configuration”) based on the feedback and the predicted deficiency to satisfy the intent.).”
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by Parker usage of machine learning model to predict a deficiency based on anticipated intent, in the system of GRINSHPUN to determine that the change in network configuration is required upon detection of an event. Doing so would have allowed providing intent based network slice management using a management data analytics function (MDAF) to predict deficiencies (see Parker, abstract).
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200396645 (GRINSHPUN) in view of US 20190222339 (Badic) as applied to claims 1, 8 and 15 above, and further in view of US 20140113676 (Hamalainen).
Regarding claims 4, 11 and 18, GRINSHPUN does not teach “wherein the current spectrum zone configuration includes one or more of a direction of an antenna and an output power level of an antenna.”
In the rejection of claim 1 above, it was shown that with respect to the limitation “obtaining a current spectrum zone configuration”, paragraphs 0077 – 0081 of GRINSHPUN disclose NDA agents 40 extracting the Key Performance Indicator (KPI) data comprising current configuration information. This information is needed for decision-making regarding activation of a new network slice.
On the other side, Hamalainen in paragraph 0073 teaches that in addition to the information represented by KPIs, information including policies, data from a configuration management function, including antenna transmission power (corresponding to the claimed “an output power level of an antenna”), and geo-location information, may be logged and/or available and for use in decision-making.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to include disclosed by Hamalainen antenna transmission power, into the current spectrum zone configuration to be obtained within the system of GRINSHPUN,. Doing so would have provided an additional data that may be useful for the decision-making.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200396645 (GRINSHPUN) in view of US 20190222339 (Badic) as applied to claims 1, 8 and 15 above, and further in view of US 8934015 (Chi).
Regarding claims 5, 12 and 19, GRINSHPUN does not teach “wherein the capturing the activity data in the physical environment further comprises: monitoring the physical environment with a microphone; and identifying an audio cue in the physical environment, wherein the audio cue is selected from a group consisting of: one or more spoken keywords and acoustic vibrations from user movements.”
As was explained in the rejection of claims 1 and 6 above, detection of events in GRINSHPUN, including car crashes and other public safety events (see paragraphs 0056, 0066 and 0085) is performed by using cameras (see paragraphs 0058 and 0076).
On the other side, Chi in col. 7 lines 4 – 16 teaches a computing device that can determine and/or indicate that the emergency situation exists using such sensors as microphones (corresponds to the claimed “monitoring the physical environment with a microphone”) and video cameras. For example, wearable computer 100 can infer that the emergency situation exists based on detected sounds and/or utterances, such as sounds/utterances related to accidents or vehicle crashes, sounds above a threshold decibel level, utterances with words such as "Help" or "Call the police!", and so on (corresponds to the claimed “identifying an audio cue in the physical environment, wherein the audio cue is selected from a group consisting of: one or more spoken keywords”). As another example, wearable computer 100 can infer that the emergency situation exists based on detected images, such as images of accidents, explosions, falling people, and so on.
In other words, Chi teaches usage of microphones to detect safety events in addition to cameras.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by Chi usage of microphones to detect specific keywords, in the system of GRINSHPUN in addition to explicitly disclosed by GRINSHPUN cameras. Doing so would have provided an additional type of information (audio) regarding any public safety events which is uncorrelated with the information type (video) disclosed by GRINSHPUN, thus increasing the reliability of detection of an event and determination of its nature.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/GENNADIY TSVEY/ Primary Examiner, Art Unit 2648