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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall 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 shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim19 is 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 19 recites “if the predicted probability is above a predefined threshold” it is not clear whether the limitation after “if” is required by the claim or not.
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.
Claims 1, 4-5, 10, 13, 22, 25-26 and 51 are rejected under 35 U.S.C. 103 as being unpatentable over Muddu et al (US 10148677) and further in view of Peterson, jr (US 5,857,020).
For claim 1, Muddu et al teach a computer-implemented method for processing data items for use in training a machine learning model (e.g. column 39, lines 22-33: The machne learning models enable the ML-based CEP engine to perform many type of analysis, from various event data sources) to identify a relationship between the data items (e.g. figure 38, column 3, lines 64-67: relationship graphs: figure 8: Relationship Graph Generation 810), wherein the data items corresponding to one or more features of a telecommunications network (e.g. column 11, lines 35-40: event data corresponds to specific network activity), the method comprising:
for each feature of the one or more features, organising the corresponding data items into a sequence according to time to obtain at least one sequence of data items (e.g. figure 1, column 24, lines 26-51: the relationship graph generator 810. Specifically, after the entities are identified in the tokens, the relationship graph generator 810 is operable to identify a number of relationships between the entities, and to explicitly record these relationships between the entities. Some implementations of the relationship graph generator 810 generate a single relationship graph for each event;); and
wherein the encoded sequence of data items being for use in training the machine learning model to identify the relationship between the data items (e.g. column 39, lines 22-33: The machne learning models enable the ML-based CEP engine to perform many types of analysis, from various event data sources).
Muddu et al do not further disclose:
encoding a single sequence of data items comprising the at least one sequence of data items to obtain an encoded sequence of data items, wherein the single sequence of data items being encoded with information indicative of a position of data items in the single sequence of data items.
Peterson, Jr teaches:
encoding a single sequence of data items comprising the at least one sequence of data items to obtain an encoded sequence of data items, wherein the single sequence of data items being encoded with information indicative of a position of data items in the single sequence of data items (e.g. column 6, lines 49-51: an indication of its position in the sequence for the contiguous data blocks… The sequence indicator may be encoded into each data block). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Peterson, Jr into the teaching of Muddu et al to provide a new and improved method and apparatus for enabling access to secured content effectively in time (e.g. column 2, lines 18-30, Peterson, Jr).
Claim 51 is rejected for the same reasons as discussed in claim 1 above.
For claim 4, Muddu et al teach each feature of the one or more features has a time stamp for use in organising the corresponding data items into the sequence according to time (e.g. figure 14. “10:51”, “10:52”, “11:06”…).
For claim 5, Muddu et al do not further disclose the information indicative of the position of data items in the single sequence of data items comprises one or both: information indicative of a position of at least one of the data items in the single sequence of data items relative to at least one other data item in the single sequence of data items; and information indicative of a relative distance between at least two of the data items in the single sequence of data items. Peterson, Jr teach the information indicative of the position of data items in the single sequence of data items comprises one or both: information indicative of a position of at least one of the data items in the single sequence of data items relative to at least one other data item in the single sequence of data items; and information indicative of a relative distance between at least two of the data items in the single sequence of data items (e.g. column 6, lines 49-51: an indication of its position in the sequence for the contiguous data blocks… The sequence indicator may be encoded into each data block). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Peterson, Jr into the teaching of Muddu et al to provide a new and improved method and apparatus for enabling access to secured content effectively in time (e.g. column 2, lines 18-30, Peterson, Jr).
For claim 10, Muddu et al teach one or more of: the one or more features of the telecommunications network comprise one or more features of at least one network node of the telecommunications network; the at least one network node comprises at least one network node that is configured to replicate one or more resources of at least one other network node; and the data items are acquired from at least one network node of the telecommunications network (e.g. column 40: lines 36-45: Each data node serves blocks of data over a network using a file access protocol).
For claim 13, Muddu et al teach one or both: the data items correspond to a user equipment served by the telecommunications network; and an identifier that identifies the user equipment is assigned to the at least one sequence of data items; and the data items comprise information indicative of a quality of a connection between a user equipment and the telecommunications network, the connection between the user equipment and the telecommunications network being a connection between the user equipment and at least one network node of the telecommunications network (e.g. column 40: lines 36-45: The distributed filesystem 1514 includes at least a name node and a plurality of data nodes. Each data node serves blocks of data over a network using a file access protocol).
For claim 22, Muddu et al the machine learning model is trained to identify the relationship between the data items in the encoded sequence of data items using a multi-head attention mechanism (e.g. column 52, line 60-column 53, line 4: the batch event processing engine can first locate the composite relationship graph that is associated with the historic event data. Then, based on the requirement of a particular machine learning model, the batch event processing engine can obtain a projection of the composite relationship graph. The composite relationship graph can include information from the data intake and preparation stage).
For claim 25, Muddu et al teach the telecommunication network is a content delivery network, CDN (e.g. column 40: lines 36-45: Each data node serves blocks of data over a network using a file access protocol).
For claim 26, Muddu et al teach training the machine learning model to identify the relationship between the data items in an encoded sequence of data items (e.g. column 52, line 60-column 53, line 4: the batch event processing engine can first locate the composite relationship graph that is associated with the historic event data. Then, based on the requirement of a particular machine learning model, the batch event processing engine can obtain a projection of the composite relationship graph. The composite relationship graph can include information from the data intake and preparation stage).
Claims 8, 23, 24 are rejected under 35 U.S.C. 103 as being unpatentable over Muddu et al and Peterson, jr, as applied to claims 1, 4-5, 10, 13, 22, 25-26 and 51 above and further in view of Zhao (US 2021/0133535).
For claim 8, Muddu et al and Peterson, Jr do not further disclose embedding the at lease one sequence of data items into the single sequence of data items and each of the at least one data item is in the form of a vector. Zhao teaches embedding the at least one sequence of data items into the single sequence of data items and each of the at least one data item is in the form of a vector (e.g. paragraph 26: A transformer model is a deep learning model that can take an input (typically sequential data such as natural language text) in the form of a sequence of vectors). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zhao into the teaching of Muddu et al and Peterson, jr to allow transformers to be trained more efficiently on larger data sets (e.g. paragraph 5, Zhao).
For claim 23, Muddu et al and Peterson, Jr do not further disclose the machine learning model is a machine learning model that is suitable for natural language processing; and the machine learning model is a deep learning model. Zhao teaches the machine learning model is a machine learning model that is suitable for natural language processing; and the machine learning model is a deep learning model. (e.g. paragraph 26: A transformer model is a deep learning model that can take an input (typically sequential data such as natural language text) in the form of a sequence of vectors). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zhao into the teaching of Muddu et al and Peterson, jr to allow transformers to be trained more efficiently on larger data sets (e.g. paragraph 5, Zhao).
For claim 24, Muddu et al and Peterson, Jr do not further disclose the deep learning model is a transformer. Zhao teaches the deep learning model is a transformer (e.g. paragraph 26: A transformer model is a deep learning model that can take an input (typically sequential data such as natural language text) in the form of a sequence of vectors). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zhao into the teaching of Muddu et al and Peterson, jr to allow transformers to be trained more efficiently on larger data sets (e.g. paragraph 5, Zhao).
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Muddu et al and Peterson, jr, as applied to claims 1, 4-5, 10, 13, 22, 25-26 and 51 above and further in view of Lee et al (US 2021/0312783).
For claim 2, Muddu et al and Peterson, jr do not further disclose initiating the training of the machine learning model to identify the relationship between the data items in the encoded sequence of data items, wherein the relationship is identified based on the information indicative of the position of data items in the single sequence of data items. Lee et al teach initiating the training of the machine learning model to identify the relationship between the data items in the encoded sequence of data items, wherein the relationship is identified based on the information indicative of the position of data items in the single sequence of data items (e.g. abstract: “expected relationship data indicating expected relationships between a plurality of entities…” figures 2A-2D, paragraph 23: generate data indicating a respective trajectory, or “trajectory data,” for each entity within a geographic area 120 based on the data stream received during a training period… Electronic computing device 110 may track each position of the entity, e.g., using a bounding box, in relation to the temporal relationship between consecutive frames, e.g., using a timestamp, to generate trajectory data for the node representing the entity.). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Lee et al into the teaching of Muddu et al and Peterson, jr to allow transformers to be trained more efficiently on larger data sets.
For claim 3, Muddu et al and Peterson, jr do not further disclose periodically initiating a retraining of the machine learning model to identify the relationship between the data items in the encoded sequence of data items. Lee et al teach periodically initiating a retraining of the machine learning model to identify the relationship between the data items in the encoded sequence of data items (e.g. abstract: “expected relationship data indicating expected relationships between a plurality of entities…” figures 2A-2D, paragraph 23: generate data indicating a respective trajectory, or “trajectory data,” for each entity within a geographic area 120 based on the data stream received during a training period… Electronic computing device 110 may track each position of the entity, e.g., using a bounding box, in relation to the temporal relationship between consecutive frames, e.g., using a timestamp, to generate trajectory data for the node representing the entity.). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Lee et al into the teaching of Muddu et al and Peterson, jr to allow transformers to be trained more efficiently on larger data sets.
Claims 16, 18, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Muddu et al and Peterson, jr, as applied to claims 1, 4-5, 10, 13, 22, 25-26 and 51 above and further in view of Hirseh (US 2021/0020060).
For claim 16, Muddu et al and Peterson, jr do not further disclose initiating training of the machine learning model to predict a probability of an event occurring in the telecommunications network and periodically initiating a retraining of the machine learning model to predict the probability of the event occurring in the telecommunications network. Hirseh teaches initiating training of the machine learning model to predict a probability of an event occurring in the telecommunications network and periodically initiating a retraining of the machine learning model to predict the probability of the event occurring in the telecommunications network (e.g. paragraphs 21, 39: The training engine 120 may use a predictive model (e.g., a machine learning model, etc.) that utilizes multiple risk factors and/or variables (learned using training data) to predict occurrence of risk events. A predictive model refers to a set of algorithmic routines and parameters that can predict an output(s) of a real-world process (e.g., readmission rates, a diagnosis or treatment of a patient, a suitable recommendation based on a user search query, etc.) based on a set of input features, without being explicitly programmed). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Hirseh into the teaching of Muddu et al and Peterson, jr to allow transformers to be trained more efficiently on larger data sets.
For claim 18, Muddu et al and Peterson, Jr do not further disclose initiating use of the trained machine learning model to predict a probability of the event occurring in the telecommunications network Hirseh teaches initiating use of the trained machine learning model to predict a probability of the event occurring in the telecommunications network (e.g. paragraph 39: The training engine 120 may use a predictive model (e.g., a machine learning model, etc.) that utilizes multiple risk factors and/or variables (learned using training data) to predict occurrence of risk events. A predictive model refers to a set of algorithmic routines and parameters that can predict an output(s) of a real-world process (e.g., readmission rates, a diagnosis or treatment of a patient, a suitable recommendation based on a user search query, etc.) based on a set of input features, without being explicitly programmed). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Hirseh into the teaching of Muddu et al and Peterson, jr to allow transformers to be trained more efficiently on larger data sets.
For claim 19, Muddu et al and Peterson, Jr do not further disclose if the predicted probability is above a predefined threshold, initiating an action in the telecommunications network to prevent or minimise an impact of the event, the action being an adjustment to at least one network node of the telecommunications network. Hirseh teaches if the predicted probability is above a predefined threshold, initiating an action in the telecommunications network to prevent or minimise an impact of the event, the action being an adjustment to at least one network node of the telecommunications network (e.g. paragraph 22: The system may also be configured to determine whether the identified likelihood of occurrence of the risk event with respect to the entity corresponds to a trigger event. Optionally, the system may generate an alert on a user device in response to determining that the identified likelihood of occurrence of the risk event with respect to the entity corresponds to the trigger event.). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Hirseh into the teaching of Muddu et al and Peterson, jr to allow transformers to be trained more efficiently on larger data sets.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Muddu et al, Peterson, jr, and Hirseh as applied to claims 1, 4-5, 10, 13, 16,18, 19, 22, 25-26 and 51 above and further in view of Lee et al (US 2021/0312783).
For claim 21, Muddu et al, Peterson, jr and Hirseh do not further disclose the event is any one or more of: a failure of a communication session in the telecommunications network; a failure of a network node of the telecommunications network; and an anomaly in a behaviour of the telecommunications network. Lee et al teach the event is any one or more of: a failure of a communication session in the telecommunications network; a failure of a network node of the telecommunications network; and an anomaly in a behaviour of the telecommunications network (e.g. abstract: etecting anomalies in a geographic area include receiving, from an electronic computing device, expected relationship data indicating expected relationships between a plurality of entities within the geographic area) It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Lee et al into the teaching of Muddu et al and Peterson, jr to allow transformers to be trained more efficiently on larger data sets.
Allowable Subject Matter
Claims 6-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAQUAN ZHAO whose telephone number is (571)270-1119. The examiner can normally be reached M-Thur: 7:00 am-5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thai Tran can be reached on 571-272-7382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Email: daquan.zhao1@uspto.gov.
Phone: (571)270-1119
/DAQUAN ZHAO/Primary Examiner, Art Unit 2484
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