DETAILED ACTION
Status of Claims
Claims 1-13, 16-19, 26, 38, 40 are currently pending and have been examined in this application. This NON-FINAL communication is the first action on the merits.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed application filed in GR 20220100418 on 05/20/2022 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification sCella conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification sCella conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites “the first information indicating a presence of the one or more first objects and the one or more second objects in a space wherein the transmission of the first signal is to be produced”. It is unclear how the presence of an object can be indicated prior to transmitting the first signal. The examiner has interpreted the limitation as “the first information indicating a presence of the one or more first objects and the one or more second objects in a space based on the transmission of the first signal”.
Claim 11 recites “another indication indicating whether or not to pre-emptively puncture traffic”. The term “another” is indefinite and it is unclear what details are involved in how to “pre-emptively puncture traffic”. The examiner has interpreted the limitation as “indicating a network priority”.
Claim 12 is rejected under 35 U.S.C. 112(b) due to its dependency on Claim 11.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person sCella be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1-13, 16-19, 26, 38, 40 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cella (US 20220187847).
Regarding Claims 1, 19, Cella discloses the following limitations:
A computer-implemented method, performed by a first node, the method being for handling transmission of a first signal, the first node operating in a communications system, the method comprising: (Cella - [0138] Aspects provided herein include a method for recognizing an object, comprising receiving at a sensor, real time adjustable data streams representing visual and contextual information about an object of interest; generating by an image processing system, an object concept including contextual intelligence about the object and its environment; adjusting by a control system, optical parameters of a conformable liquid lens; revising by a machine learning system the object concept in response to the adjustment of optical parameters of the conformable liquid lens; determining by an artificial intelligence system, the object attributes including object classification, depth, location, orientation and motion where the object concept is constantly revised in response to the adjustment of optical parameters of the conformable liquid lens and used as an input to train a machine learning model, which dynamically learns on a training set of outcomes, parameters and data collected from the conformable liquid lens. [1275] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feed-forward, back-propagation multi-layer perceptron (MLP) neural network, such as for handling one or more remote sensing applications, such as for taking inputs from sensors distributed throughout various human-inhabited environments, including home and work environments, business environments, and the like. In embodiments, the MLP neural network may be used for classification of physical environments. This may include fuzzy classification. [1529] FIG. 105 illustrates an example neural network with multiple layers. Neural network 8840 may include an input layer, a hidden layer, and an output layer with each layer comprising a plurality of nodes or neurons that respond to different combinations of inputs from the previous layers. The connections between the neurons have numeric weights that determine how much relative effect an input has on the output value of the node in question. Input layer may include a plurality of input nodes 8842, 8844, 8846, 8848 and 8850 that may provide information from the outside world or input data (e.g., sensor data, image data, text data, audio data, etc.) to the neural network 8840. [2102] In embodiments, the dynamic vision system 11300 may integrate with one or more third party systems 11930 through connectivity facilities including… wireless communication links,)
(Claim 19) A first node, for handling transmission of a first signal, the first node being configured to operate in a communications system, the first node being further configured to: (Cella – [0138], [1257], [1529], [2102])
- determining one or more characteristics of the first signal to be transmitted by a radio network node to enable detection of one or more first objects within a distance of one or more second objects so that a collision of the one or more first objects with the one or more second objects is estimated, (Cella - [2102], [0130] Aspects provided herein include a robotic vision system comprising: an optical assembly including one or more sensors, [2346] In some embodiments, the MPS motion planning system 12158 may take as input one or more images and other sensor data from a vision and sensing system as well as information indicative of the “start state” and the “goal state” (e.g., from the navigation system 12154 or other suitable component). In embodiments, the MPS 12158 may then build a motion plan for the robot. In some embodiments, the motion plan is a motion planning graph that represents the geometric structure of the environment with the states of the MPR 12100 as nodes and transitions between the states as edges of the graph). In embodiments, a graph search may be performed to find a path between the nodes representing the “start state” and the “goal state”. The MPS 12158 may also perform collision assessment determining the probability of collision between the MPR 12100 and one or more obstacles in the path and assign cost values to edges of the graph based on the probability of collision for the corresponding transition. The MPS 12158 may perform a least cost analysis on the motion planning graph to determine a set of transitions or path from the “start state” to the “goal state”. In embodiments, the MPS 12158 may coordinate with intelligence layer 12140 and navigation system 12154 to implement a navigation policy with the identified set of transitions or path. The MPS 12158 may also coordinate with controller 12160 to generate control instructions to actuate one or more actuators or motors in the MPR 12100 so as to execute the motion plan.)
wherein the transmission of the first signal by the radio network node is to detect a reflection of the first signal from the one or more first objects or the one or more second objects, and (Cella – [Fig. 142], [0130], [2102], [0011] Conventional machine vision systems are made of a combination of optics, lighting, sensors and software and aim to replicate the function of human eye. Such systems create an image of an object by capturing and analyzing the reflected light from the object. [2404] FIG. 142 illustrates an example vision and sensing system 12112 according to some embodiments of the present disclosure. Vision and sensing system 12112 include a range of sensors 12602 configured to receive information from the environment 12604 of the multi-purpose robot 12100 and enable the MPR 12100 to interact with one or more objects 12606 in its environment. For example, vision sensors may capture image data within a field of view which may assist the MPR 12100 with environment recognition and navigation. Some examples of sensors may include one or more cameras, LIDARs, RADARs,)
- providing an indication of the determined one or more characteristics to the radio network node or to a second node operating in the communications system. (Cella – [Fig. 142], [0130], [2102], [2346])
Regarding Claim 2, Cella further discloses:
wherein the indication is a second indication and wherein the method further comprises: (Cella – [2346])
- obtaining a first indication from one of a memory and the second node, the first indication indicating a function determined by the second node using machine learning, (Cella – [2346], [2318] The processors in the data processing system 12142 may communicate with a number of peripheral devices via a bus system. The peripheral devices may include a data stores including for example, a memory subsystem for storage of instructions and data and a file storage subsystem providing persistent storage for program and data files, [2389] In some implementations, machine learning models can perform or be subjected to one or more reinforcement learning techniques such as Markov decision processes; dynamic programming; Q functions or Q-learning; value function approaches; deep Q-networks; differentiable neural computers; asynchronous advantage actor-critics; deterministic policy gradient; etc.)
wherein the function enables to determine the one or more characteristics, based on first information input to the function, (Cella – [2346], [2389])
the first information indicating a presence of the one or more first objects and the one or more second objects in a space wherein the transmission of the first signal is to be produced, and (Cella – [2346])
- obtaining the first information to be input to the function, and wherein the determining is performed by inputting the obtained first information into the obtained function. (Cella – [2346], [2389])
Regarding Claim 3, 10, Cella further discloses:
wherein the determining further comprises determining an angle of a beam used for the transmission. (Cella – [1602] In embodiments, the physical input interface 9102 receives one or more inputs 9192 to the physical orientation determination chip 9100 as described herein. The inputs 9192 may be transmitted to the physical input interface 9102 by other chips, circuits, modules, and/or other components of the mobile system. For example, the input data may come from sensors, sensor-processing chips/modules/circuits, antennae, storage devices, network interfaces, or any other source of data for the chip(s) 9100 as described herein.)
Regarding Claim 4, Cella further discloses:
wherein the indication schedules the transmission of the first signal and is sent to the radio network node, and wherein the method further comprises: (Cella – [0130], [2102], [1619] In embodiments, a network enhancement chip 9200 may use such information to optimize a network by, for example, predicting which configurations of the network may optimize a particular network characteristic and then reconfiguring a host device and/or other devices on the network accordingly (e.g., switch protocols, switch networks, configure a schedule for transmission of data, configure data priorities,)
- obtaining a third indication from the radio network node, the third indication indicating second information indicating the presence of the one or more first objects and the one or more second objects in the space, the second information being based on a transmission of the scheduled first signal by the radio network node, (Cella – [0130], [1619], [2102], [2346])
- determining a risk of the collision based on the obtained third indication, and (Cella – [2346])
– sending a fourth indication to the radio network node or to another node operating in the communication system, the fourth indication indicating the determined risk of collision. (Cella – [2346])
Regarding Claims 5, 16, Cella further discloses:
wherein the one or more characteristics comprise: i) a time period during which, and ii) a frequency at which, a transmission to reception cancellation is to be produced by the radio network node to enable the detection of the one or more first objects within the distance of the one or more second objects. (Cella – [0130], [2102], [2346], [1619] FIG. 110 illustrates a network enhancement chip 9200, one or more of which may be used to enhance the operation and/or performance of communication network(s) as described herein. The chip 9200 may be used by any value chain entity that leverages communication networks. In embodiments, the chip(s) 9200 may use artificial intelligence (AI) and other techniques to analyze, predict, optimize, and reconfigure the communication network(s). In some of these embodiments, the network enhancement chip 9200 can leverage (e.g., generate, access, update, process, render, and/or otherwise leverage) a network digital twin to analyze, predict, optimize, and reconfigure the network. A network digital twin can provide a virtual representation of the physical communication network(s) that a network device has access to and the current state of those network(s) and/or network devices, as explained in more detail below. For example, the network digital twin may indicate a set of available communication networks (e.g., LAN networks, WIFI networks, cellular networks (e.g., 4G, 5G, and the like), satellite networks, Bluetooth networks, RFID networks, and/or the like) to a device or set of devices, the respective networks to which the device or respective devices are connected or have connected to in the past, real-time data relating to each respective network (e.g., current data flows, current bandwidth metrics, current throughput metrics, current error rates, current traffic types, etc.), historical data relating to each respective network (e.g., past data flows, historic bandwidth metrics, historic throughput metrics, historic error rates, historic traffic types, etc.), and/or the like. In embodiments, a network enhancement chip 9200 may use such information to optimize a network by, for example, predicting which configurations of the network may optimize a particular network characteristic and then reconfiguring a host device and/or other devices on the network accordingly (e.g., switch protocols, switch networks, configure a schedule for transmission of data, configure data priorities, configure compression of certain data, configure reformatting of certain data, up-sampling and/or down-sampling of certain data, configure dropping, buffering, or scheduling of certain data, and/or the like).)
Regarding Claims 6, 17, Cella further discloses:
wherein the radio network node transmits the first signal with one or more antennas enabled to perform communication and sensing. (Cella – [0011], [0130], [1275], [2102])
Regarding Claims 7, 18, Cella further discloses:
wherein at least one of: i. at least one of: a) the one or more first objects and b) the one or more second objects are mobile, (Cella – [2346])
ii. the one or more first objects consume broadband traffic and the one or more second objects consume machine-type of communication traffic, and (Cella – [1619], [2346])
iii. the one or more first objects are human and the one or more second objects are robots. (Cella – [2346], [0296] interactions of workers with physical entities like machines and equipment, and interactions of physical entities with other physical entities, including, without limitation, by use of video and still image cameras, motion sensing systems (such as including optical sensors, LIDAR, IR and other sensor sets), robotic motion tracking systems (such as tracking movements of systems attached to a human or a physical entity))
Regarding Claims 8, 26, Cella discloses the following limitations:
A computer-implemented method, performed by a second node, the method being for handling transmission of a first signal, the second node operating in a communications system, the method comprising: (Cella – [0138], [1257], [1529], [2102])
(Claim 26) A second node, for handling transmission of a first signal, the second node being configured to operate in a communications system, the second node being further configured to: (Cella – [0138], [1257], [1529], [2102])
- determining a function, using machine learning, wherein the function enables to determine one or more characteristics of the first signal to be transmitted by a radio network node to enable detection of one or more first objects within a distance of one or more second objects so that a collision of the one or more first objects with the one or more second objects is estimated, (Cella – [0130], [2102], [2346], [2389])
wherein the transmission of the first signal by the radio network node is to detect a reflection of the first signal from the one or more first objects or the one or more second objects, and (Cella – [Fig. 142], [0011], [0130], [2102], [2404])
- providing a first indication of the determined function to a first node operating in the communications system. (Cella – [Fig. 142], [0130], [2102], [2346])
Regarding Claim 9, Cella further discloses:
wherein the determining is performed using reinforcement learning with one of: single agent reinforcement learning and multi agent reinforcement learning. (Cella – [2389], [2390] Reinforcement Learning is a machine learning technique for learning optimal behavior in an environment by taking actions and getting feedback, similar to how humans and animals learn by interacting with their environments. The typical reinforcement learning approach includes an agent (say robot control system 12150) that observes its environment, evaluates its current state (e.g., robot velocity, distance to an object in front), and selects an action (e.g., provide control instruction to actuator or motor, adjust velocity, change direction and the like). Upon carrying out an action, the agent is presented with, in addition to its new state, a reward (e.g., +10 for allowing sufficient space between the robot and an obstacle in front of it and −10 for allowing insufficient space) which provides some indication of the success of the action. The goal for a reinforcement learning agent is to learn an optimal policy or behavior that maximizes the expected cumulative reward.)
Regarding Claim 11, Cella further discloses:
wherein the determining comprises training the function, wherein the training comprises, in one iteration round: (Cella – [0138], [2346])
- obtaining third information indicating a presence of the one or more first objects and the one or more second objects in a space wherein the transmission to reception cancellation is to be produced, (Cella – [2346])
- obtaining, using a heuristic search algorithm, (Cella – [2350] optimal search algorithms (such as Dijkstra's algorithm, A* algorithm) and bioinspired planning algorithms.)
a first training frequency, of a plurality of training frequencies, a first training period, of a plurality of training periods, and (Cella – [0130], [0138], [1619], [2102], [2346])
another indication indicating whether or not to pre-emptively puncture traffic during the first training period, (Cella – [0138], [1619], [2346])
- triggering transmission of the first signal by the radio network node with the first training frequency, while puncturing existing transmissions at the beginning of the first training period according to the another indication, (Cella – [0138], [1619], [2346])
- triggering scanning, by the radio network node or a second radio network node operating in the communications system, for reception of the reflection of the first signal in response to the transmitted first signal during the first training period, (Cella – [1619], [2404], [2346])
- feeding a response of the scanning to the function, and- updating the function with the fed response, and - performing additional iteration rounds until the function achieves a desired performance level. (Cella – [2389], [2346])
Regarding Claim 12, Cella further discloses:
wherein at least one of: a. the third information indicates a state space comprising: (Cella - [0138]
static robots, moving robots, static humans and moving humans, and (Cella - [0138], [0296], [2346])
b. the first training frequency, the first training period, and (Cella – [1619], [2404], [2346])
the another indication for every iteration round correspond to an action in an action space. (Cella – [0138], [1619], [2346])
Regarding Claim 13, Cella further discloses:
wherein the radio network node is a first radio network node of a plurality of radio network nodes having overlapping radio coverage in a space wherein the transmission of the first signal is to be produced, (Cella – [0130], [2346])
the first radio network node serving one or more first cells and each of the other radio network nodes in the plurality of radio network nodes serving, respectively, one or more second cells, and (Cella – [0130], [1619], [2346])
wherein the method further comprises: - determining a respective number of the one or more second objects detected in each of the one or more first cells, (Cella – [0130], [0138], [1619], [2346])
- optionally, receiving, the respective number of the one or more second objects detected in each of the one or more second cells from each of the other radio network nodes in the plurality of radio network nodes, and (Cella – [0130], [0138], [1619], [2346])
- obtaining a further indication, the further indication indicating which cell, out of the one or more first cells and the one or more second cells has a larger number of the one or more second objects, (Cella – [1619], [2346])
wherein transmission of the first signal by any radio network node comprised in the plurality of radio network nodes other than a radio network node controlling the indicated cell is to be refrained until reception of an additional indication indicating transmission of the first signal is enabled. (Cella – [1619], [2102], [2346])
Regarding Claims 38, 40, Cella further discloses:
A computer program product comprising a non-transitory computer-readable storage medium, having stored thereon a computer program, comprising instructions which, when executed on processing circuitry, cause the processing circuitry to carry out the methods. (Cella – [2780-2781] Examples of processing hardware include a central processing unit (CPU), [2784] A computer-readable medium in this disclosure is therefore non-transitory,)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure or directed to the state of art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Wu (US 20220026531) teaches time series of channel information (TSCI) may be extracted from a wireless signal with a sampling frequency and sampling period.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON JAMES HENSON whose telephone number is (703)756-1841. The examiner can normally be reached Monday-Friday 9:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha H. Desai can be reached at (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON JAMES HENSON/Examiner, Art Unit 3648
/BERNARR E GREGORY/Primary Examiner, Art Unit 3648
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