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 .
Response to Amendment
The Amendment filed March 5th, 2026 has been entered. Claims 1, 3-4, 6, 7, 9, and 13-15 remain pending in the application. Applicant’s amendments to the Specification, Drawings, and Claims have overcome each and every objection and 112(b) rejections previously set forth in the Non-Final Office Action mailed December 16, 2025.
The previous objections to the drawings are withdrawn in view of the 3/05/2026 amendments to the drawings. The objection to claim 13 has been withdraw in view of the 3/05/2026 claim amendments.
Response to Arguments:
Applicant’s arguments filed on 03-05-2026 have been fully considered.
In reference to Applicant’s arguments:
-Claim rejections under 35 U.S.C. 101.
Examiner’s response:
Regarding the applicant’s arguments on rejections under 35 U.S.C. 101, stating “independent Claims 1, 7, and 9 have been substantially amended to include significantly more detailed technical features that are well beyond mental steps. Specifically, the amended independent claims are directed to a specific, technology-focused improvement in artificial-intelligence-based outlier detection, not to an abstract idea. Each claim recites a concrete technical architecture-including a controller, memory, and communication unit- that performs multi-stage machine operations that cannot be carried out mentally. The claims require (i) collecting device-generated datasets, (ii) performing two distinct clustering stages based on device-level and function-level attribute information, (iii) training separate outlier detection models for each cluster, (iv) mapping new data into vector spaces and computing machine-implemented similarity measures such as cosine similarity, Euclidean distance, or Manhattan distance, (v) detecting previously unseen devices or functions when similarity thresholds are not met, and (vi) automatically reconfiguring or relearning outlier detection models in response. These steps together provide a concrete technical solution that improves the functioning of an AI-based anomaly-detection system by enabling more accurate and adaptive classification of heterogeneous device data in real time. The claims therefore integrate any alleged abstract idea into a practical application and recite significantly more than data analysis alone.”
From applicant’s arguments, the examiner respectfully disagrees and did not find the applicant’s arguments to be persuasive. In regards to performing clustering, this step can be performed mentally since clustering can be done by grouping data into categories using pen and paper. In addition, collecting datasets is considered a routine process part of data gathering and insignificant extra solution activity. Furthermore, training models per cluster is considered using a generic computer to apply the mental process of clustering. The additional limitations of mapping data to vector spaces and run similarity measures, detect unseen devices when similarity measures are not satisfied, and relearning models did not make the claims eligible under 35 U.S.C. 101 since these steps can be performed by any generic computer, and not improving the functionality. In addition, the applicant’s arguments on the improvement being “enabling more accurate and adaptive classification of heterogeneous device data in real time” is neither found in the specification, nor in the amended claim language. Please see examiner’s rejections under 35 U.S.C. 101.
In reference to Applicant’s arguments:
-Claim rejections under 35 U.S.C. 103.
Examiner’s response:
Applicant’s arguments regarding amended limitations have been fully considered and examiner finds the applicant’s arguments persuasive and decides to withdraw the rejections under 35 U.S.C. 103 for independent claims 1, 7, and 9, and their respective dependent claims.
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, 7 and 9 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, 7 and 9 recite as the limitation regarding “wherein each of the plurality of outlier detection models is independently trained using only training data belonging to a corresponding primary cluster and a corresponding secondary cluster,” and said limitation is considered to incorporate new matter in the claim which does not contain support in the original disclosure. Although the specification in [0022] describes training, but does not specify how the process was independently trained.
In addition, claims 1, 7, and 9 recite as limitation regarding “in a model-specific representation space distinct from the clustering used to partition the dataset", and said limitation is considered to incorporate new matter in the claim which does not contain support in the original disclosure. Although the specification mention clustering, the specification did not specify that the representation space is different from clustering, and the phrase "representation space" is nowhere mentioned in the specification. Even though the specification mentions "vector space" in [0095] and [0098], the specification does not state anywhere that the clustering and outlier models must have different vector spaces.
Therefore, examiner cannot find support in the specification for these limitations.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-4, 6, 7, 9, and 13-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Claim 1:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A computer-implemented method for managing technical information based on artificial intelligence, executed by a technical information management apparatus designs an outlier detection model based on data collected from at least one device provided in a specific area, the method comprising ...” A method is one of the four statutory categories of invention. In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
generating a plurality of primary clusters by primarily clustering the collected dataset based on first attribute information corresponding to a device through which data passes, (This recites a mental process since a person can mentally evaluate a dataset collected and make a judgement to create primary clusters by grouping that data based on first attribute information of a device which contain that data, see (MPEP 2106.04(a)(2)(III)),
generating a plurality of secondary clusters to be subdivided by secondarily clustering data included in each of the plurality of primary clusters based on second attribute information including function information executed in the device corresponding to each data, (mental process- a person can evaluate a dataset collected and make a judgement to create secondary clusters by grouping from primary clusters, see (MPEP 2106.04(a)(2)(III)),
generating … a plurality of outlier detection models respectively for the plurality of primary clusters and for the plurality of secondary clusters, each outlier detection model being learned using only the dataset included in the corresponding cluster; (mental process- a person can evaluate the grouped primary clusters and secondary clusters and make a judgement of generating outlier detection models that represents the clusters, see (MPEP 2106.04(a)(2)(III)),
for newly introduced data comprising a first attribute information field and a second attribute information field, mapping a device characteristic value and a function characteristic value extracted from the respective fields to vector spaces and computing similarities using at least one of cosine similarity, Euclidean distance, or Manhattan distance; (This recites a mental process since a person can mentally evaluate and map a device characteristic value and a function characteristic value from viewing values extracted from vector spaces and similarities, see (MPEP 2106.04(a)(2)(III)),
selecting a primary outlier detection model and a secondary outlier detection model whose computed similarities with the newly introduced data satisfy corresponding preset thresholds (This recites a mental process – since a person can mentally choose two outlier detection models which the similarities of those models with newly introduced data meet preset thresholds - see (MPEP 2106.04(a)(2)(III)),
in response to similarity with any primary outlier detection model failing to satisfy a preset threshold, determining that the newly introduced data is obtained via a new device, adding attribute information corresponding to the new device to each of the first attribute information and the second attribute information, … (This recites a mental process – since a person can mentally evaluate if newly introduced data fails to meet a preset threshold to similarity of an outlier detection model, then this defines the data is related to a new device, a person then mentally add first and second attribute information that relates to the new device using pen and paper, see (MPEP 2106.04(a)(2)(III)),
determining whether the newly introduced data is classified into one of the plurality of secondary clusters …, (This recites a mental process— since a person can mentally evaluate if newly introduced data is grouped into any of the secondary clusters, see (MPEP 2106.04(a)(2)(III)),
and determining the newly introduced data as non-clustered data and as an outlier with a possibility of technology leakage in response to the newly introduced data being not classified into any of the plurality of secondary clusters, (Mental process- a person can evaluate and observe if newly introduced data has non-clustered data is present from each secondary cluster and make a judgement to flag that as an outlier and evaluate if that has a possibility of technology leakage from using the outlier detection models for newly input data, see (MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A computer-implemented method for managing technical information based on artificial intelligence, executed by a technical information management apparatus designs an outlier detection model based on data collected from at least one device provided in a specific area, … (This recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f))
and training a plurality of outlier detection models, (This recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
…through the selected outlier detection model; (This recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
collecting, via the communication unit, a dataset corresponding to at least one device provided in the specific area; (In step 2A, prong2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g),).
wherein the dataset includes data transmitted, received, generated, changed, and stored by the device; (In step 2A, prong2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g),).
and reconfiguring or relearning at least one outlier detection model; (This recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f))
wherein each of the plurality of outlier detection models is independently trained using only training data belonging to a corresponding primary cluster and a corresponding secondary cluster, and the trained model internally groups similar training data in a model-specific representation space distinct from the clustering used to partition the dataset. (This recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements ix, x, xi, xiv, and xv recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more. The additional elements xii and xiii recite mere data gathering, and are considered insignificant extra-solution activities. In step 2B, these insignificant extra-solution activities are well understood routine and conventional activities, which include receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016), – see MPEP 2106.05(d) (II)(i)),
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 3:
Regarding claim 3, claim 3 is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1.
Further, claim 3 recites the following additional element: “adding attribute information corresponding to a new added device to each of the first attribute information and the second attribute information, whenever the new device is added to the specific area.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4:
Regarding claim 4, since claim 4 is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites the following abstract ideas:
matching the newly input data with one of the plurality of outlier detection models, based on values in a first attribute information field or a second attribute information field of the newly input data; (mental process- a person can evaluate and judge if newly input data can match with the outlier detection models created based on first or second attribute information from the newly input data, see MPEP 2106.04(a)(2)(III)),
determining whether the newly input data is classified into one of the plurality of secondary clusters through the matched outlier detection model; (mental process- a person can evaluate and then judge to decide if newly input data is grouped into any of the secondary clusters created using the matched outlier detection models, see MPEP 2106.04(a)(2)(III))
and determining the newly input data as an outlier, when the newly input data is not classified into any of the plurality of secondary clusters. (mental process- a person can evaluate and then judge to decide if newly input data is an outlier if that data is not grouped into any of the secondary clusters, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 6:
Regarding claim 6, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 6 recites the following mental process:
Claim 6 recites “… each of the plurality of outlier detection models is generated by learning dataset included in the corresponding cluster among the collected dataset. (This recites a mental process – a person can mentally evaluate and create models using a dataset from a respective cluster among collected data, see MPEP 2106.04(a)(2)(III)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 7:
Regarding claim 7, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A computer-implemented method for managing technical information based on artificial intelligence, executed by a technical information management apparatus which designs and executes an outlier detection model based on data collected from at least one device provided in a specific area, the method comprising...” A method is one of the four statutory categories of invention. In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
generating a plurality of clusters by clustering an integrated dataset, (This recites a mental process since a person can mentally evaluate an integrated dataset and generate clusters by grouping that data, see (MPEP 2106.04(a)(2)(III)),
generating a plurality of outlier detection models corresponding to the plurality of clusters by learning each of the plurality of clusters; (mental process- a person can mentally evaluate and create outlier detection models that correspond to clusters by learning about the clusters, see (MPEP 2106.04(a)(2)(III)),
matching newly introduced data with any one of the plurality of outlier detection models, by mapping attribute-derived characteristic values to a vector space and computing at least one of cosine similarity, Euclidean distance, or Manhattan distance to satisfy a preset threshold, (this recites a mental process – since a person can evaluate, compare newly introduced data with any of the models by mapping the attribute values to a vector space, and mentally calculate similarity that meets a preset threshold, see (MPEP 2106.04(a)(2)(III)),
determining whether there is an outlier with a possibility of technology leakage for the newly introduced data through the matched outlier detection model including determining whether the newly introduced data is classified into a corresponding cluster and, when not classified, determining the newly introduced data as the outlier, (Mental process- a person can evaluate and observe if an outlier exists in newly introduced data and judge to flag that as an outlier and evaluate if that has a possibility of technology leakage if the newly introduced data is not grouped into a cluster, see (MPEP 2106.04(a)(2)(III)),
in response to the matched model selection failing to satisfy a preset threshold at a device level, adding attribute information for a newly detected device …(This recites a mental process – since a person can mentally evaluate if a model fails to meet a preset threshold at a device level, a person then mentally adds attribute information that relates to the new device using pen and paper, see (MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A computer-implemented method for managing technical information based on artificial intelligence, executed by a technical information management apparatus which designs and executes an outlier detection model based on data collected from at least one device provided in a specific area, … (This recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f))
collected in the specific area based on attribute information comprising at least one of (i) first attribute information identifying a device through which data passes or (ii) second attribute information comprising function information executed in the device; (In step 2A, prong2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g),).
and reconfiguring or relearning at least one outlier detection model; (This recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f))
wherein each of the plurality of outlier detection models is independently trained using only training data belonging to a corresponding primary cluster and a corresponding secondary cluster, and the trained model internally groups similar training data in a model-specific representation space distinct from the clustering used to partition the dataset. (This recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements vi, viii, and ix, recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more. The additional element vii recites mere data gathering, and is considered insignificant extra-solution activity. In step 2B, this insignificant extra-solution activity is a well understood routine and conventional activity, which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016), – see MPEP 2106.05(d) (II)(i)),
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 9:
Regarding claim 9, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites
“An apparatus for managing technical information based on artificial intelligence, the apparatus configured to design an outlier detection model based on data collected from at least one device provided in a specific area …,” and an apparatus is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
… configured to generate a plurality of primary clusters by primarily clustering a dataset collected in the specific area based on first attribute information corresponding to a device through which data passes; (mental process, a person can mentally evaluate and cluster a dataset based on attribute information that relates to a device , see MPEP 2106.04(a)(2)(III)),
… configured to generate a plurality of secondary clusters by secondarily cluster data included in each of the plurality of primary clusters based on second attribute information including function information executed in the device corresponding to each data; (mental process, a person can mentally evaluate and cluster data that was in the primary cluster by secondarily clustering based on a second attribute information in the device, see MPEP 2106.04(a)(2)(III)),
generate … a plurality of outlier detection models …(mental process, a person can mentally evaluate the grouped primary clusters and secondary clusters and then generate outlier detection models that represents the clusters, see (MPEP 2106.04(a)(2)(III)),
configured to: map characteristic values from newly input data to vector spaces, (mental process, since a person can mentally evaluate and map a device characteristic value and a function characteristic value from viewing values extracted from vector spaces with pen and paper, see MPEP 2106.04(a)(2)(III))
compute at least one of cosine similarity, Euclidean distance, or Manhattan distance, (This recites a mental process, since a person can mentally evaluate and compute (or calculate) similarity measures such as cosine similarity, Euclidean distance, or Manhattan distance using pen and paper, see MPEP 2106.04(a)(2)(III)),
select outlier detection models whose computed similarities satisfy preset thresholds, (mental process, since a person can mentally evaluate and select models from viewing if similarities satisfy preset thresholds or not, see MPEP 2106.04(a)(2)(III))
in response to a threshold being not satisfied at a device level, determine that the newly input data is obtained via a new device and add attribute information for the new device … (mental process, a person can mentally evaluate and look at newly input data and observe if that data relates to new information for a new device from not satisfying a threshold, and a person then mentally add attribute information that relates to the new device using pen and paper, see MPEP 2106.04(a)(2)(III)),
determine whether the newly input data is classified into one of the plurality of secondary clusters through the selected secondary outlier detection model, (This recites a mental process, a person can mentally evaluate and determine if newly input data is grouped into secondary clusters, see MPEP 2106.04(a)(2)(III)),
and determine the newly input data as an outlier with a possibility of technology leakage in response to the newly input data being not classified into any of the plurality of secondary clusters, (This recites a mental process, a person can mentally evaluate and observe if newly introduced data has non-clustered data is present from each secondary cluster and judge to label that as an outlier and evaluate if that has a possibility of technology leakage from using the outlier detection models for newly input data, see (MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
An apparatus for managing technical information based on artificial intelligence, the apparatus configured to design an outlier detection model based on data collected from at least one device provided in a specific area, the apparatus comprising… (Mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
a primary cluster generating module …(Mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
a secondary cluster generating module … (Mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
an outlier detection model generating module… (Mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
…configured to … and train a plurality of outlier detection models respectively for the plurality of primary clusters and for the plurality of secondary clusters using only data of corresponding clusters; (Training models recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
and an outlier detection model execution module… (Mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
..and reconfigure or relearn at least one outlier detection model, (Mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
wherein each of the plurality of outlier detection models is independently trained using only training data belonging to a corresponding primary cluster and a corresponding secondary cluster, and the trained model internally groups similar training data in a model-specific representation space distinct from the clustering used to partition the dataset. (Mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element x, xi, xii, xiii, xiv, xv, xvi, and xvii recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 13:
Regarding claim 13, it is dependent upon claim 9, and thereby incorporates the limitations of, and corresponding analysis applied to claim 9. Further, claim 13 recites the following additional element:
“The apparatus of claim 9, wherein the outlier detection model generating module is configured to train the plurality of outlier detection models so that the plurality of outlier detection models group the collected dataset into one of plurality of groups related with the primary clusters and the secondary clusters by performing the primary clustering and the secondary clustering based on the first attribute information or the second attribute information.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 14:
Regarding claim 14, this claim is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis applied to claim 13. Further, claim 14 recites the following abstract idea:
“… is configured to generate each of the plurality of outlier detection models by learning dataset included in the corresponding cluster among the collected dataset.” (This is considered a mental process – a person can mentally evaluate and create models using a dataset from a respective cluster among collected data, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Further, claim 14 recites the following additional elements:
“wherein the outlier detection model generating module…”, (In step 2A, prong 2, the outlier detection model generating module is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 15:
Regarding claim 15, it is dependent upon claim 9, and thereby incorporates the limitations of, and corresponding analysis applied to claim 9. Further, claim 15 recites the following additional element:
“The apparatus of claim 9, further comprising: a technology leakage prevention policy execution unit configured to determine one of a plurality of preset technology leakage prevention policies according to the determination of the outlier detection model execution module regarding whether new data has an outlier or not, thus execute the determined policy for the new data.” (This is considered an additional element, and in step 2A, prong 2, this is considered mere instructions to implement an abstract idea using generic computer – see MPEP 2106.05(f)). (In step 2B, this is also considered mere instructions to implement an abstract idea using generic computer – see MPEP 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
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).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WENWEI ZENG whose telephone number is (571)272-7111. The examiner can normally be reached Monday-Friday, 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached at (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WenWei Zeng/Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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