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 Arguments
Applicant’s arguments with respect to the rejections under 35 USC 101 have been fully considered but are not persuasive. Applicant argues that “The concepts of each of the independent claims are applied to and in the context of a network,” which “is a specific area of application that limits the scope of the present claims, providing significant improvement…by eliminating the need for the parser script.” However, a mere field-of-use limitation is not sufficient to make a claim eligible (MPEP 2106.05(h)). Moreover, merely “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept (MPEP 2106.05(f).
Applicant’s arguments with respect to the rejections under 35 USC 103 have been fully considered and are persuasive in light of the amendments. Accordingly, the rejections are withdrawn. However, upon further consideration, new grounds of rejection are made.
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 1-9 and 11-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Although each claim nominally falls within at least one of the four eligible categories under step 1 of the 101 analysis, the claims are directed to an abstract idea (which is a judicial exception to the four categories) without significantly more.
First, with respect to prong one of step 2A of the analysis, each of independent claims 1 and 11 is directed to the abstract idea of analyzing network configuration data. Claim 1 will be treated as representative. The idea is recited in the following aspects of claim 1 (and in the corresponding aspects of claim 11):
“…parsing named entity attributes from text of the unstructured network configuration data and mapping the named entity attributes to a common information model having a predetermined data structure, wherein the named entity attributes comprise at least device identifier information associated with a device of the NE” and
“one or more of automating, managing, controlling, or analyzing the network comprising the NE and the device of the NE that is initiated or modified using the common information model.”
The idea amounts to a process that, under its broadest reasonable interpretation, covers performance in the mind or with a pen and paper but for the recitation of generic computer components. For example, but for the generic computer components, the claimed process encompasses a human reading over printouts of network configuration data, writing attributes of the data down in a particular format, and thinking about the data in order to analyze the network and a device. If a claim, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, each of the independent claims recites an abstract idea.
Next, with respect to prong two of step 2A, this abstract idea is not integrated into a practical application in each of the independent claims. In particular, besides the abstract idea itself, each claim recites generic computer functionality at a high level of generality such that it amounts to no more than mere instructions to apply the abstract idea using generic computer components. Simply invoking general-purpose computers or computer components as a tool to perform the abstract idea, or claiming the improved speed or efficiency inherent with applying the abstract idea on a computer, is not enough to transform the claims into a patent-eligible application, and does not provide an inventive concept. See MPEP 2106.05(f). Moreover, to the extent that the claims require such as gathering and transmitting data over a network, or outputting, storing, or displaying data, these features amount to insignificant extra-solution activity, which is not indicative of integration into a practical application. See MPEP 2106.05(g). Still further, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h).
As specific examples, the claims recite the following aspects which are not sufficient to integrate the abstract idea into a practical application:
that certain aspects of the invention are performed at or by a “network inventory discovery engine,” which amounts to invoking general-purpose computers or computer components as a tool to perform the abstract idea;
“receiving unstructured network configuration data associated with a network element (NE) of a network responsive to a device of the NE being initiated or modified,” which amounts to insignificant extra-solution activity in the form of data-gathering, and which is triggered by an “initiation” such as having been installed in the first place, which amounts to invoking general-purpose computers or computer components as a tool to perform the abstract idea;
“using a trained machine learning (ML) model of the network inventory discovery engine” to perform the parsing “without a parser script,” which amounts to invoking general-purpose computers or computer components as a tool to perform the abstract idea;
“a non-transitory computer readable medium” (in claim 11), which amounts to invoking general-purpose computers or computer components as a tool to perform the abstract idea; and
In light of the above, the claimed invention clearly does not pertain to an improvement in the functioning of the computer itself or to any other technology or technical field. Rather than presenting a technological solution to a technological problem, each claim represents merely an abstract idea that is implemented using computers as tools. Therefore, the claims clearly cannot be said to represent a technological improvement. Accordingly, these additional elements do not integrate the abstract idea into a practical application. Because the claims recite an abstract idea but do not integrate the abstract idea into a practical application, each claim is directed to an abstract idea.
Next, with respect to 2B, each of the independent claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements a network discovery engine, receiving configuration data, using a trained ML model, a computer readable medium, an NER or LLM, etc., amount to mere instructions to apply the idea, insignificant extra-solution activity, or mere field-of-use limitations. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Moreover, as discussed above, to the extent that the claims imply features such as gathering and transmitting data over a network, or outputting, storing, or displaying data, these features amount to insignificant extra-solution activity, which cannot amount to significantly more to the abstract idea. Finally, upon reevaluating the elements previously determined to be insignificant extra-solution activity, they cannot be considered unconventional. Considering the additional elements individually and in combination, each of the claims as a whole does not recite additional elements that amount to significantly more than the judicial exception.
For the reasons given above, each of the independent claims is directed to an abstract idea without significantly more, and therefore the claims are not patent eligible under 35 USC 101.
Dependent claims 2-9 and 12-18 are rejected under the same rationale as given above. Each of these claims include further details of the abstract idea, making it more specific, but no less abstract. Any additionally recited limitations which are not directed to the abstract idea itself do not include limitations which amount to a practical application of, or significantly more than, the abstract idea.
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, 6, 11-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Branch (US Pub. No. 2013/0246606) in view of Siracusano (US Pub. No. 2024/0411944).
Regarding claim 1, Branch shows an automated network inventory discovery method, comprising:
at a network inventory discovery engine coupled to a network data source, receiving unstructured network configuration data associated with a network element (NE) of a network (see [0017], [0045]-[0049]: configuration data collected from network devices, including network interface information, ARP cache data, etc.) responsive to a device of the NE being initiated or modified (e.g., such as being modified in the form of migration, or being initiated as a necessary part of being installed in the first place: see [0043]-[0044], [0047], [0083]-[0084], claims 12 and 24);
using a [component] of the network inventory discovery engine, parsing named entity attributes from … the unstructured network configuration data and mapping the named entity attributes to a common information model having a predetermined data structure (see [0046] and [0051], describing filtering the configuration data and organizing it for storage to a data warehouse), wherein the named entity attributes comprise at least device identifier information associated with a device of the NE (e.g., device identifiers including MAC addresses, IP addresses, associated with devices such as network interfaces of the network elements: see [0045]-[0049]); and
one or more of automating, managing, controlling, or analyzing the network comprising the NE and the device of the NE that is initiated or modified using the common information model (using the data in the data warehouse, at least managing and analyzing the network and transparent network devices: see [0017], [0042]-[0046], [0052]-[0053], [0080]-[0084]).
Branch does not explicitly show:
that the parsing is using a trained machine learning (ML) model and that the parsing is parsing text of the unstructured network configuration data without a parser script.
Siracusano shows:
using a trained machine learning (ML) model, parsing named entity attributes from text of unstructured data without a parser script and mapping the named entity attributes to a common information model having a predetermined data structure (see Fig. 1, Fig. 5, and [0094]-[0115]).
It would have been obvious to modify the system of Branch with the teachings of Siracusano in order to reduce misclassification (see Siracusano, [0061]).
Regarding claim 2, the combination shows the limitations of claim 1 as applied above and further shows wherein the named entity attributes are related to one or more of a network device or a network topology (using the data in the data warehouse, at least managing and analyzing the network to identify the presence and location of transparent network devices: see Branch, 17, 42-46, 52-53, 80-84).
Regarding claim 3, the combination shows the limitations of claim 1 as applied above and further shows wherein the network inventory discovery engine comprises a non-transitory computer readable medium comprising instructions stored in a memory and executed by a processor of the network inventory discovery engine to carry out the automated network inventory discovery method (see Branch, Fig. 2 and 31-38).
Regarding claim 4, the combination shows the limitations of claim 1 as applied above and further shows wherein the unstructured network configuration data is received from the network data source by one of reading a log file to obtain the unstructured network configuration data, reading the unstructured network configuration data from a database, downloading the unstructured network configuration data from a file transfer protocol (FTP) server, or via an application programming interface (API) and a resource adapter (RA) coupled between the network inventory discovery engine and the network data source (see Branch, 47-50, at least a log file such as archived ifconfig and lsof output, a database such as an SNMP database, or an API to a resource adapter in the form of middleware).
Regarding claim 6, the combination shows the limitations of claim 1 as applied above and further shows wherein the trained ML model comprises a trained large language model (LLM) (see Siracusano, [0094]-[0115]).
Regarding claim 11, Branch shows a non-transitory computer readable medium comprising instructions stored in a memory and executed by a processor (at least implicitly disclosed as a necessary component of a computer-implemented system of a network inventory discovery engine to carry out an automated network inventory discovery method, comprising:
at the network inventory discovery engine coupled to a network data source, receiving unstructured network configuration data associated with a network element (NE) of a network (see [0017], [0045]-[0049]: configuration data collected from network devices, including network interface information, ARP cache data, etc.) responsive to a device of the NE being initiated or modified (e.g., such as being modified in the form of migration, or being initiated as a necessary part of being installed in the first place: see [0043]-[0044], [0047], [0083]-[0084], claims 12 and 24);
using a [component] of the network inventory discovery engine, parsing named entity attributes from … the unstructured network configuration data and mapping the named entity attributes to a common information model having a predetermined data structure (see [0046] and [0051], describing filtering the configuration data and organizing it for storage to a data warehouse), wherein the named entity attributes comprise at least device identifier information associated with a device of the NE (e.g., device identifiers including MAC addresses, IP addresses, associated with devices such as network interfaces of the network elements: see [0045]-[0049]); and
one or more of automating, managing, controlling, or analyzing the network comprising the NE and the device of the NE that is initiated or modified using the common information model (using the data in the data warehouse, at least managing and analyzing the network and transparent network devices: see [0017], [0042]-[0046], [0052]-[0053], [0080]-[0084]).
Branch does not explicitly show:
that the parsing is using a trained machine learning (ML) model and that the parsing is parsing text of the unstructured network configuration data without a parser script.
Siracusano shows:
using a trained machine learning (ML) model, parsing named entity attributes from text of unstructured data without a parser script and mapping the named entity attributes to a common information model having a predetermined data structure (see Fig. 1, Fig. 5, and [0094]-[0115]).
It would have been obvious to modify the system of Branch with the teachings of Siracusano in order to reduce misclassification (see Siracusano, [0061]).
Claims 12, 13, and 15 correspond to claims 2, 4, and 6 and are rejected for the reasons given above, mutatis mutandis.
Claims 5, 7, 8, 14, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Branch (US Pub. No. 2013/0246606) in view of Siracusano (US Pub. No. 2024/0411944), and further in view of Portisch (US Pub. No. 2025/0094707).
Regarding claim 5, the combination shows the limitations of claim 1 as applied above, but does not explicitly show wherein the trained ML model comprises a trained named entity recognition (NER) model.
Portisch shows wherein a trained ML model comprises a trained named entity recognition (NER) model (see Fig. 16 and 308-318, describing NER services that "use machine learning" to extract entities in a text, which are in turn used to supplement prompt data sent to an LLM).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the system of Branch with the teachings of Smith in order to improve the accuracy of the output of the LLM.
Regarding claim 7, the combination shows the limitations of claim 1 as applied above, but does not explicitly show wherein the trained ML model comprises a trained named entity recognition (NER) model that is used to pre-process the text of the unstructured network configuration data prior to feeding resulting data into a trained large language model (LLM).
Portisch shows wherein a trained ML model comprises a trained named entity recognition (NER) model that is used to pre-process text of unstructured data prior to feeding resulting data into a trained large language model (LLM) (see Fig. 16, 39, and 308-320, describing NER services that "use machine learning" to extract entities in a text, which are in turn used to supplement prompt data sent to an LLM).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the system of Branch with the teachings of Smith in order to improve the accuracy of the output of the LLM.
Regarding claim 8, the combination shows the limitations of claim 7 as applied above and further shows wherein the trained NER model is used to categorize words in the text of the unstructured network configuration data and includes resulting categories in the text of the unstructured network configuration data prior to feeding the resulting data into the LLM See Portisch, 39, as combined above, describing "verbalizing" the data retrieved by the NER in order to feed it to the LLM; see also 320, where the NER is to classify and categorize entities into predefined categories.
Claims 14, 16, and 17 correspond to claims 5, 7, and 8 and are rejected for the reasons given above, mutatis mutandis.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Branch (US Pub. No. 2013/0246606) in view of Siracusano (US Pub. No. 2024/0411944) and Portisch (US Pub. No. 2025/0094707), and further in view of Pasumarthi (US Pub. No. 2025/0094619).
Regarding claim 9, the combination shows the limitations of claim 7 as applied above, but does not explicitly show wherein the trained NER model is used to remove parts from the text of the unstructured network configuration data prior to feeding the resulting data into the LLM.
Pasumarthi shows wherein a trained NER model is used to remove parts of text from data prior to feeding the resulting data into another model (see [0041]-[0044]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the system of Branch with the teachings of Pasumarthi in order to improve security by ensuring the LLM only has access to appropriate information.
Claim 18 corresponds to claim 9 and is rejected for the reasons given above, mutatis mutandis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher D. Biagini whose telephone number is (571)272-9743. The examiner can normally be reached weekdays from 9 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oscar Louie can be reached at (571) 270-1684. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Christopher D. Biagini
Primary Examiner
Art Unit 2445
/Christopher Biagini/ Primary Examiner, Art Unit 2445