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 .
Remarks
This Office Action is responsive to Applicants' Amendment filed on March 31, 2026, in which claims 1-5, 10-12, 16-18 are currently amended. Claims 1-20 are currently pending.
Response to Arguments
Applicant’s arguments with respect to rejection of claims 1-20 under 35 U.S.C. 103 based on amendment have been considered, however, are not persuasive.
With respect to Applicant’s arguments on p. 12 of the Remarks submitted 3/31/2026 that Tao does not disclose “generating a preprocessed subset of the plurality of historical event record profiles semantically proximate to the input temporal sequence of event codes in the multi-dimensional embedding space”, Examiner respectfully disagrees. Tao teaches ([p. 3] "Figure 2: Behavior sequence of game bots is similar to each other, while behavior sequence of human players shows diversity" [p. 5] “the goal is to design a modified word2vec model” [p. 6 §4.3] "When given an unknown user record, the model provides a probability as a reference to the operation team. The team decides whether to suspend the players after comparing the classification probability with a preset threshold which is usually chosen according to operation experience by the team. A higher threshold leads to a higher precision, while a lower one to a higher recall") where Tao explicitly discloses that the model is a modified word2vec (semantic proximity) model. For at least these reasons and those further detailed below, Examiner asserts that the prior art rejection in view of the combination of Tao and Drokin is reasonable and should be maintained.
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.
Claims 1-20 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.
Regarding claims 1, 10, and 16, "generating a preprocessed subset of the plurality of historical event record profiles semantically proximate to the input temporal sequence of event codes in the multi-dimensional embedding space" is indefinite. Specifically, one of ordinary skill in the art would not be able to determine the scope of "semantically proximate". For example, the phrase "I borrowed it without asking" appears to be semantically proximate to "I stole it", however, one of ordinary skill in the art would not readily be able to quantify the similarity. Examiner asserts that "proximate" alone is indefinite as it does not provide concrete boundaries for determining the scope of the claim. Similarly, One of ordinary skill in the art would not readily be able to apply or determine a semantic proximity between historical event records and an input temporal sequence of event codes, neither of which appear to have clear semantics. In the interest of further examination the claim is interpreted as simply "generating a subset of the plurality of historical event record profiles based at least in part on a first comparison".
The remaining claims are rejected with respect to their dependence on the rejected claims.
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 1, 2, 4, 8, 9, 10, 11, 16, and 17 are rejected under U.S.C. §103 as being unpatentable over the combination of Tao (“NGUARD: A Game Boy Detection Framework for NetEase MMORPGs”, 2018) and Drokin (US20200303072A1).
Regarding claim 1, Tao teaches A computer-implemented method comprising: identifying, by one or more processors, a plurality of event records associated with an event period, wherein an event record of the plurality of event records is associated with an event date and an event code([p. 4 §4] "For daily quests, we segment each player’s behavior sequence by date" [p. 5 §4.2.1] "In our framework, we use the Time-interval Event2vec as a pre training step to learn a vector representation for each EventID […] Each user activity is assigned to an EventID, and one record of a user’s continuous behavior sequence is represented as an EventID sequence {E1,. . .,En}. The model receives this sequence as input, followed by an embedding layer which transforms the input sequence to a sequence of vectors {e1,. . .,en}")
generating, by the one or more processors, an input temporal sequence of event codes extracted from the plurality of event records stored in the memory, ([p. 5 §4.2.1] " one record of a user’s continuous behavior sequence is represented as an Event ID sequence {E1,. . .,En}. The model receives this sequence as input")
wherein the input temporal sequence of event codes comprises the event code ordered based at least in part on the event date of the event record to capture intra-period relationships among the plurality of event records within the event period([p. 4 §4] "For daily quests, we segment each player’s behavior sequence by date" [p. 5 §4.2.1] "In our framework, we use the Time-interval Event2vec as a pre training step to learn a vector representation for each EventID […] Each user activity is assigned to an EventID, and one record of a user’s continuous behavior sequence is represented as an Event ID sequence {E1,. . .,En}. The model receives this sequence as input, followed by an embedding layer which transforms the input sequence to a sequence of vectors {e1,. . .,en}" Tao explicitly preprocesses segments in each player behavior sequence into date segmented periods to capture intra-period relationships)
mapping, by the processors and using an embedding model, the input temporal sequence of event codes to a multi-dimensional embedding space associated with a plurality of historical event record profiles respectively associated with a particular event period prior to the event period to capture inter-period relationships between the event period and the particular event period([p. 5 §4.2.1] "we propose a model with similar approach called Time-interval Event2vec to deal with event vectorization. In the work of app2vec, the goal is to design a modified word2vec model which considers the weight between apps, where weight is measured by the time elapsed between two app sections. Similar to app2vec, our work of Time-interval Event2vec also considers the time elapsed between two events" Event2Vec interpreted as embedding model for mapping the input sequence of event codes to a multi-dimensional embedding space to capture inter-period relationships)
performing, by the one or more processors, data preprocessing for the input temporal sequence of event codes by generating a preprocessed subset of the plurality of historical event record profiles semantically proximate to the input temporal sequence of event codes in the multi-dimensional embedding space based at least in part on a first comparison between (i) a model-driven threshold value and ([p. 3] "Figure 2: Behavior sequence of game bots is similar to each other, while behavior sequence of human players shows diversity" [p. 5] “the goal is to design a modified word2vec model” [p. 6 §4.3] "When given an unknown user record, the model provides a probability as a reference to the operation team. The team decides whether to suspend the players after comparing the classification probability with a preset threshold which is usually chosen according to operation experience by the team. A higher threshold leads to a higher precision, while a lower one to a higher recall" See also FIG. 5 for SA-ABLSTM architecture. Similar interpreted as semantically proximate)
(ii) a plurality of cross-profile distance measures between the input temporal sequence of event codes and the plurality of historical event record profiles, ([p. 3 §3] "Our dataset is collected from a real-world MMORPG in NetEase, involving about 436 billion user logs from May 1st, 2016 to Dec 31st, 2017 which amount to 107TB" [p. 6 §4.3] "we propose a model combining Sequence Autoencoder and DBSCAN, namely SA-DBSCAN. DBSCAN is a data density-based clustering algorithm proposed by Martin Ester [10]. In our solution, we first extract the vector representation of EventID sequences from the well-trained Sequence Autoencoder. Next, perform DBSCAN on the vector representation of EventID sequence. Clusters of player groups with high behavioral similarity can be obtained by DBSCAN" Tao explicitly performs DBSCAN clustering on EventID sequences to determine distance (similarity). User log interpreted as synonymous with historical event record profile.)
wherein the model-driven threshold value is determined based at least in part on a statistical distribution of historical cross-profile distance measure values associated with a pre-runtime anomaly detection prediction generated by a pre-runtime version of an anomaly detection machine learning model, ([p. 6 §4.3] "When given an unknown user record, the model provides a probability as a reference to the operation team. The team decides whether to suspend the players after comparing the classification probability with a preset threshold which is usually chosen according to operation experience by the team. A higher threshold leads to a higher precision, while a lower one to a higher recall" In Tao the decision probability threshold is at least in part determined by the cross-profile similarity structure encoded in the learned embedding space, since the classifier's scores and the operational threshold are defined over representations whose geometry reflects distances between behavior profiles. This explicitly uses the pre-runtime (pretrained) event2vec model and pre-trained RNN cell matrix (See FIG. 5))
and wherein the model-driven threshold value is determined to enable the data preprocessing using cross-temporal inferences associated with the intra-period relationships and the inter-period relationships([p. 6 §4.3] "When given an unknown user record, the model provides a probability as a reference to the operation team. The team decides whether to suspend the players after comparing the classification probability with a preset threshold which is usually chosen according to operation experience by the team. A higher threshold leads to a higher precision, while a lower one to a higher recall")
generating, by the one or more processors and using a run-time version of the anomaly detection machine learning model, an anomaly prediction for the input temporal sequence of event codes based on a second comparison between the input temporal sequence of event codes and the preprocessed subset of the plurality of historical event record profiles([p. 4 §4] "Out proposed MMORPG bot detection framework, termed NGUARD is shown in Figure 4. NGUARD consists of a preprocessing module, an offline training module, an online inference module and an auto-iteration mechanism module" [p. 5 §4.2.3] "We pose the problem of identifying game bots as a binary classification problem. The model combining Sequence Autoencoder and Attention-based Bidirectional LSTM, namely SA-ABLSTM is proposed [...] Each user activity is assigned to an EventID, and one record of a user’s continuous behavior sequence is represented as an EventID sequence {E1,. . .,En}. The model receives this sequence as input, followed by an embedding layer which transforms the input sequence to a sequence of vectors {e1,. . .,en}. We then feed the sequence {e1, . . .,en} to a bidirectional lstm layer," See FIG. 4 and FIG. 5. Online inference module interpreted as runtime version. Bot interpreted as anomaly.).
However, Tao does not explicitly teach and initiating, by the one or more processors, performance of a display of an anomaly detection user interface based at least in part on the anomaly prediction.
Drokin, in the same field of endeavor, teaches and initiating, by the one or more processors, performance of a display of an anomaly detection user interface based at least in part on the anomaly prediction([¶0108] "through the Ontology Lookup Service, which provides web service interface for query of many ontologies from one place with a unified data output format" [¶0210] "pretraining of medical terms (concepts) is executed using software tool for analyzing natural languages semantics Word2vec using an ontology for regularization").
Tao as well as Drokin are directed towards anomaly detection using vector embedding models and machine learning. Therefore, Tao as well as Drokin are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Tao with the teachings of Drokin by either substituting the Word2Vec embeddings in Drokin with the Time2Vec (explicitly inspired by Word2Vec) time-interval embeddings in Tao over medical events, or stacking both models together. This combination would amount to directing the NGUARD system in Tao to the medical data in Drokin. Drokin provides as additional motivation for using vector embedding systems for medical data ([¶0004] "This technical solution enables to create a patient mathematical model whereby it possible to increase accuracy of diagnosis and make an analysis and prognosis of disease course for a particular patient."). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 2, the combination of Tao and Drokin teaches The computer-implemented method of claim 1, wherein the input temporal sequence of event codes comprises one or more respective event codes from the plurality of event records (Drokin [¶0097] "for a text in natural language there could be assigned a handler which forms a sequence of medical facts from the text by its mapping to the terms of medical ontology and/or dictionary of medical terms." [¶0099] "when mapping the text each medical fact is annotated (marked) with date and/or time corresponding with the date and/or time of the current record from the health record." [¶0270] "Therefore, if the model receives an ordered sequence of events and does not take the appointment time into consideration, then, when training this model, diagnoses, medicines and procedures inside one appointment are rearranged in random manner, and then, “appointments” are united into a sequence." [¶0271] "Since such sequence of events will have different length for different patients, and long-ago events will make less contribution to prediction of diagnoses, in some embodiments the models are learned from the latest N events (in case of less events in the patient health record they are added with zeros). The whole sequence of visits for each 1 year long window is considered for the model only.").
Regarding claim 4, the combination of Tao and Drokin teaches The computer-implemented method of claim 1, wherein generating the input temporal sequence of event codes further comprises: generating one or more event code documents for an event record profile, wherein an event code document of the one or more event code documents describes respective temporal sequence of event codes extracted from an event record of the plurality of event records; and generating the input temporal sequence of event codes based at least in part on the one or more event code documents (Drokin [¶0044] "the electronic health record comprises patient records including at least the following data: record adding date, codes of diagnoses, symptoms, procedures and medicines, text description of case history in natural language, biomedical images associated with the case history, results of patient study and analyses" [¶0099] "when mapping the text each medical fact is annotated (marked) with date and/or time corresponding with the date and/or time of the current record from the health record." [¶0278] "For the purpose of obtaining the contracted representation at its simplest the so-called embedding-matrix is used, by which the sparse vector of the health record is multiplied. Several matrices have been considered:" [¶0279] "Word2Vec: as a matrix we took a coefficient matrix obtained on the basis of the analysis of secondary diagnoses, directions and medicines, which were present within one appointment. For training we used word2vec model with skipgram mechanism so as to obtain the medical concept vector of a certain length for any diagnosis, medical procedure or prescribed medicine (corresponding to the embedding-matrix column)." [¶0108] "through the Ontology Lookup Service, which provides web service interface for query of many ontologies from one place with a unified data output format" [¶0210] "pretraining of medical terms (concepts) is executed using software tool for analyzing natural languages semantics Word2vec using an ontology for regularization").
Regarding claim 8, the combination of Tao and Drokin teaches The computer-implemented method of claim 1, wherein the event record comprises a medical service delivery record.(Drokin [¶0079] "Codes of diagnoses, symptoms, procedures and medicines can be presented in MKB format (e.g. MKB-10), SNOMED-CT, CCS (Clinical Classifications Software), etc. Selection of format does not affect the essence of the technical solution.")
and wherein the event code comprises a diagnosis code.(Drokin [¶0079] "Codes of diagnoses, symptoms, procedures and medicines can be presented in MKB format (e.g. MKB-10), SNOMED-CT, CCS (Clinical Classifications Software), etc. Selection of format does not affect the essence of the technical solution.").
Regarding claim 9, the combination of Tao and Drokin teaches The computer-implemented method of claim 1, wherein the memory for storing the plurality of event records is a volatile memory.(Drokin [¶0297] " it is obvious to a person skilled in the art that in typical operation environment there could be used other types of computer-readable means which can store data accessible from the computer, such as magnetic cassettes, flash memory drives, digital video disks, Bernoulli cartridges, random access memories (RAM)" RAM interpreted as volatile memory.).
Regarding claims 10-11, claims 10-11 are directed towards a system for performing the method of claims 1 and 4, respectively. Therefore, the rejection applied to claims 1 and 4 also applies to claims 10-11. Claims 10-11 recite additional elements one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising (Drokin [¶0294] "FIG. 8 illustrates the example of the general purpose computer system in which the current technical solution can be implemented, and which comprises a multipurpose computing unit in the form of a computer 20 or server comprising a processor 21, system memory 22 and system bus 23, which links different system components, including the system memory with the processor 21.").
Regarding claims 16-17, claims 16-17 are substantially similar to claims 10-11. Therefore, the rejections applied to claims 10-11 also apply to claims 16-17.
Claims 3 and 15 are rejected under U.S.C. §103 as being unpatentable over the combination of Tao and Drokin and in further view of Thornton (“Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data”, 2014).
Regarding claim 3, the combination of Tao and Drokin teaches The computer-implemented method of claim 2.
However, the combination of Tao and Drokin doesn't explicitly teach wherein the event record is associated with a provider identifier.
Thornton, in the same field of endeavor, teaches the event record is associated with a provider identifier ([Abstract] "Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program." [p. 698 §4.4] "Provider 23481, plotted in the left top corner, was one of the providers that attracted attention, because of its severe outlying behavior, and would be an interesting candidate for further analysis to find the cause of higher average of this provider on reimbursements.").
The combination of Tao and Drokin as well as Thornton are directed towards machine learning outlier detection. Therefore, the combination of Tao and Drokin as well as Thornton are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Tao and Drokin with the teachings of Thornton by including a provider identifier in the medical event records. Thornton provides as additional motivation for combination ([p. 693 §6] "We structure our design science contribution according to the Hevner et al. (2004) framework and address a relevant problem in healthcare fraud detection. This paper offers an artifact and a description of a method for applying outlier detection to healthcare fraud along with an evaluation of this model in practice to a state-wide database of actual healthcare claims with over 500 providers. The model is evaluated by applying it in practice to actual healthcare data and having experts review the results of the analysis. The paper contributes to the literature by providing a roadmap for future applications of outlier detection in healthcare and potentially other corollary domains").
Regarding claim 15, claim 15 is directed towards a system for performing the method of claim 3. Therefore, the rejection applied to claim 3 also applies to claim 15. Claims 15 also recite additional elements one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising (Drokin [¶0294] "FIG. 8 illustrates the example of the general purpose computer system in which the current technical solution can be implemented, and which comprises a multipurpose computing unit in the form of a computer 20 or server comprising a processor 21, system memory 22 and system bus 23”).
Claims 5, 12, and 18 are rejected under U.S.C. §103 as being unpatentable over the combination of Tao and Drokin and Lev (US20160188672A1).
Regarding claim 5, the combination of Tao and Drokin teaches The computer-implemented method of claim 4, wherein generating the one or more event code documents comprises: generating a term-frequency-inverse-document-frequency (TF-IDF) score for the event code with respect to the event code document; and(Drokin [¶0285] "TFI-DF encoding: this model was built on the basis of logistic regression to which input the array, which slots were associated with patient diseases, was transmitted. It was built analogously to the previous model except for the fact that the number of disease occurrence in the health record was taken into consideration, and then, the input features were processed by TF-IDF algorithm to associate larger weight with diagnoses of rare occurrence in general, but of frequent occurrence in a particular patient." Larger weight interpreted as synonymous with larger TF-IDF score for each event code (associated patient disease with corresponding code).).
However, the combination of Tao and Drokin doesn't explicitly teach based on a TF-IDF score of the event code document failing to satisfy a TF-IDF score threshold value, updating the event code document by removing from the event code document the event code.
Lev, in the same field of endeavor, teaches based on a TF-IDF score of the event code document failing to satisfy a TF-IDF score threshold value, updating the event code document by removing from the event code document the event code([¶0122] "unimportant or uninteresting events can be eliminated or suppressed by applying statistical methods such as setting a threshold on the number of occurrences of the event or setting a threshold on the calculated TF-IDF value.").
The combination of Gao and Drokin as well as Lev are directed towards machine learning outlier detection. Therefore, the combination of Gao and Drokin as well as Lev are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Gao and Drokin with the teachings of Lev by removing elements whose TF-IDF score fails to reach a threshold. Lev provides as additional motivation for combination ([¶0122] “By applying the technique to a single sentence rather than a large collection of documents, the technique can be applied more rapidly, e.g., in real time or nearly real time”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 12, claim 12 is directed towards a system for performing the method of claim 5. Therefore, the rejection applied to claim 5 also applies to claim 12. Claims 12 also recite additional elements one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising (Drokin [¶0294] "FIG. 8 illustrates the example of the general purpose computer system in which the current technical solution can be implemented, and which comprises a multipurpose computing unit in the form of a computer 20 or server comprising a processor 21, system memory 22 and system bus 23, which links different system components, including the system memory with the processor 21.").
Regarding claim 18, claim 18 is substantially similar to claim 12. Therefore, the rejections applied to claim 12 also applies to claim 18.
Claims 6, 7, 13, 14, 19, and 20 are rejected under U.S.C. §103 as being unpatentable over the combination of Tao and Drokin and in further view of Stefik (US20110270830A1).
Regarding claim 6, the combination of Tao and Drokin teaches The computer-implemented method of claim 1, wherein generating the anomaly prediction comprises: (Drokin see claim 1).
However, the combination of Tao and Drokin doesn't explicitly teach for a target code of one or more target codes that is associated with at least one event record profile in the preprocessed subset of the plurality of historical event record profiles, determining an inclusion ratio of a count of event record profiles in the preprocessed subset of the plurality of historical event record profiles that are associated with the target code to a total count of event record profiles in the preprocessed subset; and generating the anomaly prediction based at least in part on the inclusion ratio.
Stefik, in the same field of endeavor, teaches for a target code of one or more target codes that is associated with at least one event record profile in the preprocessed subset of the plurality of historical event record profiles, determining an inclusion ratio of a count of event record profiles in the preprocessed subset of the plurality of historical event record profiles that are associated with the target code to a total count of event record profiles in the preprocessed subset; and generating the anomaly prediction based at least in part on the inclusion ratio([¶0058] " A measure or score is assigned to each characteristic word using, for instance, TF-IDF weighting, which identifies the ratio of frequency of occurrence of each characteristic word in the selective sampling of articles to the frequency of occurrence of each characteristic word in the baseline (step 55). The score of each characteristic word can be adjusted (step 56) to enhance, that is, boost, or to discount, that is, deemphasize, the importance of the characteristic word to the topic" Stefik explicitly teaches that the TF-IDF analysis identifies an inclusion ratio TF-IDF score for each element.).
The combination of Tao and Drokin as well as Stefik are directed towards machine learning anomaly detection. Therefore, the combination of Tao and Drokin as well as Stefik are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Tao and Drokin with the teachings of Stefik by using TF-IDF as a ratio determination. Stefik explicitly teaches that TF-IDF itself is inherently a ratio determination method and provides as additional motivation for combination ([¶0058] “The score of each characteristic word can be adjusted (step 56) to enhance, that is, boost, or to discount, that is, deemphasize, the importance of the characteristic word to the topic.”).
Regarding claim 7, the combination of Tao and Drokin teaches initiating the performance of one or more prediction-based actions based at least in part on the priority score.(Drokin [Abstract] "determining a diagnosis, and also conducting an analysis and predicting the most probable disease development with respect to the patient according to the set of facts presented." [¶0285] "TFI-DF encoding: this model was built on the basis of logistic regression to which input the array, which slots were associated with patient diseases, was transmitted. It was built analogously to the previous model except for the fact that the number of disease occurrence in the health record was taken into consideration, and then, the input features were processed by TF-IDF algorithm to associate larger weight with diagnoses of rare occurrence in general, but of frequent occurrence in a particular patient.").
However, the combination of Tao and Drokin doesn't explicitly teach The computer-implemented method of claim 1, further comprising: determining a priority score for the anomaly prediction based at least in part on an inclusion ratio for a respective event record profile of the plurality of historical event record profiles.
Stefik, in the same field of endeavor, teaches The computer-implemented method of claim 1, further comprising: determining a priority score for the anomaly prediction based at least in part on an inclusion ratio for a respective event record profile of the plurality of historical event record profiles([¶0058] " A measure or score is assigned to each characteristic word using, for instance, TF-IDF weighting, which identifies the ratio of frequency of occurrence of each characteristic word in the selective sampling of articles to the frequency of occurrence of each characteristic word in the baseline (step 55). The score of each characteristic word can be adjusted (step 56) to enhance, that is, boost, or to discount, that is, deemphasize, the importance of the characteristic word to the topic" Stefik explicitly teaches that the TF-IDF analysis identifies an inclusion ratio such that the TF-IDF score for each element in Randall is interpreted as a priority score for the anomaly prediction based at least in part on an inclusion ratio.).
The combination of Tao and Drokin as well as Stefik are directed towards machine learning anomaly detection. Therefore, the combination of Tao and Drokin as well as Stefik are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Tao and Drokin with the teachings of Stefik by using TF-IDF as a ratio determination. Stefik explicitly teaches that TF-IDF itself is inherently a ratio determination method and provides as additional motivation for combination ([¶0058] “The score of each characteristic word can be adjusted (step 56) to enhance, that is, boost, or to discount, that is, deemphasize, the importance of the characteristic word to the topic.”).
Regarding claims 13-14, claims 13-14 are directed towards a system for performing the method of claims 6-7, respectively. Therefore, the rejection applied to claims 6-7 also applies to claims 13-14. Claims 13-14 also recite additional elements one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising (Drokin [¶0294] "FIG. 8 illustrates the example of the general purpose computer system in which the current technical solution can be implemented, and which comprises a multipurpose computing unit in the form of a computer 20 or server comprising a processor 21, system memory 22 and system bus 23, which links different system components, including the system memory with the processor 21.").
Regarding claims 19-20, claims 19-20 are substantially similar to claims 13-14. Therefore, the rejections applied to claims 13-14 also apply to claims 19-20.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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