Detailed Action
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This is the initial office action based on the application filed on September 13th, 2013, which claim 1-20 have been presented for examination.
Status of Claims
2. Claims 1-20 are pending in the application, of which claims 1, 10 and 19 are in independent form and these claims (1-20) are subject to following rejection(s) and/or objection(s) set forth in the following Office Action below.
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.
3 Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Step 1, claims 1-10 (apparatus), claims 11-19 (method), and claim 20 (non-transitory computer-readable storage medium). Thus, they fall in statutory categories.
Step 2A Prong 1:
Claim 1 recites:
(a). identify an alert group comprising a plurality of alert data objects;
(b). extract, based on the plurality of alert data objects and using one or more feature extraction models, one or more alert features associated with the alert group;
(C) . extract action-related communication content for the alert group;
(d). generate, using one or more machine learning models and based on an input data set comprising the one or more alert features and the action-related communication content, an alert group summary for the alert group; and
(e). cause rendering of an alert group summary interface on a display of a user device, wherein the alert group summary interface comprises at least a portion of the alert group summary.
Step 2A, Prong 1:
(a)-(d) can be done human mind or by human with pen and paper, i.e., (a) is evaluation or judgement), (b)-(c) are collecting data, (d) can be done by human with pen and paper.
Step 2A, Prong 2:
Additional elements are processor, memory, program code, extraction model, interface, display, user device, and €. However, processor, memory, program code, extraction model, interface, display, and user device are recited at high level of generality. Further, € is insignificant extra-solution activity. Thus, the claim as a whole does not integrate the exception into a practical application.
Step 2B:
The additional elements, considering them both individually and in combination, do not amount to significantly more than the judicial exception itself.
Claims 2 and 12 recites:
alert feature entity data associated with alert group, which is act of determination can be performed through observation, evaluation, judgement with the aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing extended list of alerts. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Claims 3 and 13 recite:
input data comprises behavioral insights comprises fault localization, blast radius or fault propagation path data is as similar as claim 2 above as classifying of data that can be performed through observation, evaluation, judgement with the aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing extended list of alerts. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Claims 3 and 13 recite:
input data comprises behavioral insights comprises fault localization, blast radius or fault propagation path data is as similar as claim 2 above as classifying of data that can be performed through observation, evaluation, judgement with the aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing extended list of alerts. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Claims 4 and 14 recite:
behavioral insights is generated based on the entity data and topology data –this is act of manipulation of information which can be done by aid of a human using a pen, marker and a paper, and presumably viewing and/or reviewing.
Claims 5 and 15 recite:
extract the entity data from the plurality of alert data objects using the BILSTM-CRF-NER model is act of collecting data in conjunction with simply adding extra-solution activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. MPEP 2106.05.
Claims 6 and 16 recite:
alert features comprises action notes corresponding to one or more actions by a user associated with the alert group is insignificant extra-solution activity. Thus, the claim as a whole does not integrate the exception into a practical application.
Claims 7 and 17 recite:
generate the alert group summary via the pre-trained LLM using retrieval augmented generation and based on the input data set these limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea.
Claims 8 and 18 recite:
input data set further comprises context data obtained via a domain knowledge graph this is insignificant extra-solution activity.
Claim 9 and 19 recite:
alert group summary comprises a title segment, a comprehensive summary segment, an actions summary segment, and a timeline segment this is insignificant extra-solution activity. Thus, the claim as a whole does not integrate the exception into a practical application.
Regarding independent claim 11:
Claim 11 recites:
(a). extract, based on the plurality of alert data objects and using one or more feature extraction models, one or more alert features associated with the alert group;
(d). generate, using one or more machine learning models and based on an input data set comprising the one or more alert features, an alert group summary for the alert group; and
(c). cause rendering of an alert group summary interface on a display of a user device, wherein the alert group summary interface comprises at least a portion of the alert group summary.
-which invoke the same analysis as claim 1 above; thus, simply adding extra-solution activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. MPEP 2106.05. Therefore, claim 11 is ineligible.
Regarding independent claim 20:
Claim 20 recites:
(a). identify an alert group comprising a plurality of alert data objects;
(b). generate, using one or more machine learning models and based on an input data set comprising the one or more alert features and the action-related communication content, an alert group summary for the alert group; and
(c). cause rendering of an alert group summary interface on a display of a user device, wherein the alert group summary interface comprises at least a portion of the alert group summary.
-which invoke the same analysis as claim 1 above; thus, simply adding extra-solution activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. MPEP 2106.05. Therefore, claim 20 is ineligible.
Claims 1-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitation(s) fail(s) to establish that the claim(s) 1-20 are not directed to an abstract idea, or establish itself a tangible or physical system, or an apparatus, or a structure or an entity. The filed invention is merely an abstract concept or an 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 of this title, 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.
4. Claims 1-4, 6-7, 9-14, 16-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bu et al. (US Patent Application Publication No. 2025/0225323 A1 -herein after Bu) in view of Ojha et al. (US Patent Application Publication No. 2023/0093091 A1 herein after Ojha).
Per claim 1:
Bu discloses:
An apparatus for generating alert group summaries (At least see ¶[0011] -summarizing a set of alert logs associated with a computer system), the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus ( At least see FIG. 6 with associated texts in ¶[0067] through ¶[0072]) to at least:
generate, using one or more machine learning models and based on an input data set comprising the one or more alert features and the action-related communication content, an alert group summary for the alert group (At least see ¶[0013] generates a summarization prompt that includes the set of alert logs, instructions to summarize the logs, and one or more output constraints. The system then uses the generative machine learning model to generate M summarization outputs; also see ¶[0048] -input text data as guided by the summarization instruction(s) and/or the output constraint(s) described in the input summarization prompt); and
cause rendering of an alert group summary interface on a display of a user device, wherein the alert group summary interface comprises at least a portion of the alert group summary (At least see ¶[0019] -select the top R scored outputs such as the top 3 outputs or outputs above a threshold T score such as 8 out of 10 to include in the final aggregated summarization presented to the user).
Bu sufficiently disclose the apparatus/system as set forth above, but Bu does not explicitly disclose : identify an alert group comprising a plurality of alert data objects; extract, based on the plurality of alert data objects and using one or more feature extraction models, one or more alert features associated with the alert group; extract action-related communication content for the alert group.
However, Ojha discloses:
identify an alert group comprising a plurality of alert data objects (At least see ¶[0009] -in response to determining to generate the one or more responder alert data objects);
extract, based on the plurality of alert data objects and using one or more feature extraction models (At least see ¶[0022] -incident data object based at least in part on extracting the incident metadata from the incident data object), one or more alert features associated with the alert group (At least see ¶[0003] -alert policy data objects corresponds to the incident data object based at least in part on incident metadata associated with the incident data (alert feature) object [emphasis added]);
extract action-related communication content for the alert group (At least see ¶[0119] --create action executor module 121 communicates with the policy matcher module 125, which in turn communicates with the policy registry service module 115 to retrieve one or more global alert policy data objects from the global alert policy data repository).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ojha into Bu’s invention because Ojha’s teaching would provide dynamically configuring alerts based on incidents that are detected in a computing network environment via an incident alert and management platform that may generate one or more responder alert data objects based at least in part on a global alert policy data object and/or an inline alert policy data object, wherein generating an inline alert policy data object that is separate from a global alert policy data object provides various technical benefits and advantages (please see ¶[0043] and ¶[0212]).
Per claim 2:
Ojha also discloses:
one or more alert features comprises entity data corresponding to one or more entities associated with the alert group (At least see ¶[0070] -incident metadata may specify a type or category of the incident that the incident data object represents, such as, but not limited to, hardware issue, software issue, service issue, network issue).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ojha into Bu’s invention because Ojha’s teaching would provide dynamically configuring alerts based on incidents that are detected in a computing network environment via an incident alert and management platform that may generate one or more responder alert data objects based at least in part on a global alert policy data object and/or an inline alert policy data object, wherein generating an inline alert policy data object that is separate from a global alert policy data object provides various technical benefits and advantages (please see ¶[0043] and ¶[0212]).
Per claim 3:
Bu discloses:
input data set further comprises behavioral insights comprising one or more of fault localization data, blast radius data, or fault propagation path data (At least see ¶[0022] - Anomaly detection may include identifying deviations from normal network behavior, which could indicate possible intrusions and/or suspicious activities. Heuristic analysis may include applying predefined rules and behavioral models to detect threats).
Per claim 4:
Bu discloses:
behavioral insights is generated based on the entity data and topology data (At least see ¶[0016] - model encoder maps input log details to an abstract representation, and the decoder translates that representation into coherent summarization text).
Per claim 6:
Ojha also discloses:
one or more alert features comprises action notes corresponding to one or more actions by a user associated with the alert group (At least see ¶[0125] - incident close action applier workflow 143 is defined or specified based on user input, also see ¶[0024]).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jiang into Bu modified by Ojha’s invention because Jiang proposed model demonstrate the superiority of the proposed model that provides BiLSTM-CRF model to optimally reduce the time and equipment cost of model training, while taking advantage of the pre-trained model with superior named-entity recognition capabilities, to enable lightweight recognition of power equipment defect text in the condition of low resources (please see page 372 top of right column).
Per claim 7:
Bu discloses:
cause the apparatus to generate the alert group summary via the pre-trained LLM using retrieval augmented generation and based on the input data set (At least see ¶[0013] - generative machine learning model to generate M summarization outputs (e.g., provides the summarization prompt to a generative machine learning model M times to determine M summarization outputs).
Per claim 9:
Bu discloses:
alert group summary comprises a title segment, a comprehensive summary segment, an actions summary segment, and a timeline segment (At least see ¶[0031] title, ¶[0019] comprehensive summary, ¶[0003] action leads to summarizing data, ¶[0025] timeline or timestamp associated with alert log/information).
Per claim 10:
Bu discloses:
receiving an alert group identifier (At least see ¶[0057] - identifiers of the alert groups associated with the set of alert logs); and
identifying the alert group based on the alert group identifier (At least see ¶[0061] - a number of alerts associated with each of the alert groups described by the summarization output, identifiers of the alert groups described by the summarization output).
Per claim 11:
Bu discloses:
A computer-implemented method for generating alert group summaries (At least see ¶[0011] method associated with first alert grpup), the computer-implemented method comprising:
remaining limitations as depicted in this method claim are as similar as claim 1 above; as such, the remaining limitations are rejected using same rational as claim 1 above.
Per claim 12:
Ojha also discloses:
one or more alert features comprise entity data corresponding to one or more entities associated with the alert group.
limitation as depicted in this method claim is as similar as claim 2 above; as such, the limitation is rejected using same rational as claim 2 above.
Per claim 13:
Bu discloses:
input data set further comprises behavioral insights comprising one or more of fault localization data, blast radius data, or fault propagation path data.
limitation as depicted in this method claim is as similar as claim 3 above; as such, the limitation is rejected using same rational as claim 3 above.
Per claim 14:
Bu discloses:
behavioral insights is generated based on the entity data and topology data.
limitation as depicted in this method claim is as similar as claim 4 above; as such, the limitation is rejected using same rational as claim 4 above.
Per claim 16:
Ojha also discloses:
one or more alert features comprises action notes corresponding to one or more actions by a user associated with the alert group.
limitation as depicted in this method claim is as similar as claim 6 above; as such, the limitation is rejected using same rational as claim 6 above.
Per claim 17:
Bu discloses:
generating the alert group summary comprises retrieval augmented generation via the pre-trained LLM and based on the input data set.
limitation as depicted in this method claim is as similar as claim 7 above; as such, the limitation is rejected using same rational as claim 7 above.
Per claim 19:
Bu discloses:
alert group summary further comprises a title segment, a comprehensive summary segment, an actions summary segment, and a timeline segment.
limitation as depicted in this method claim is as similar as claim 9 above; as such, the limitation is rejected using same rational as claim 9 above.
Per claim 20:
Bu discloses:
At least one non-transitory computer-readable storage medium for generating alert group summaries (At least see ¶[0012]- a system and/or device having non-transitory computer-readable media storing computer-executable instructions), the at least one non-transitory computer-readable storage medium having computer coded instructions configured to (At least see ¶[0012]- that, when executed by one or more processors, performs the method), when executed by at least one processor:
remaining limitations as depicted in this product claim are as similar as claim 1 above; as such, the remaining limitations are rejected using same rational as claim 1 above.
5. Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bu et al. in view of Ojha et al. and further in view of Jiang et al. (NPL “A Lightweight Named Entity Recognition Method for Chines Power Equipment Defect Test” here in after Jiang).
Per claim 5:
Bu modified by Ojha sufficiently discloses the system as set forth above, but Bu modified by Ojha does not explicitly disclose: one or more feature extraction models comprises a BILSTM-CRF-NER model, wherein the at least one memory and the program code are configured to, with the at least one processor, cause the apparatus to extract the entity data from the plurality of alert data objects using the BILSTM-CRF-NER model.
However, Jiang discloses:
one or more feature extraction models comprises a BILSTM-CRF-NER model, wherein the at least one memory and the program code are configured to, with the at least one processor, cause the apparatus to extract the entity data from the plurality of alert data objects using the BILSTM-CRF-NER model (At least see page 368: right col. - a text information extraction model based on the bi-directional long short-term memory with conditional random field (BiLSTM-CRF) model to deal with the text data of functional defects of the secondary equipment in the power system).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jiang into Bu modified by Ojha’s invention because Jiang proposed model demonstrate the superiority of the proposed model that provides BiLSTM-CRF model to optimally reduce the time and equipment cost of model training, while taking advantage of the pre-trained model with superior named-entity recognition capabilities, to enable lightweight recognition of power equipment defect text in the condition of low resources (please see page 372 top of right column).
Per claim 15:
Jiang discloses:
one or more feature extraction models comprises a BILSTM-CRF-NER model, wherein extracting the one or more alert features comprises extracting the entity data from the plurality of alert data objects using the BILSTM-CRF-NER model.
limitation as depicted in this method claim is as similar as claim 5 above; as such, the limitation is rejected using same rational as claim 5 above.
6. Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bu et al. in view of Ojha et al. and further in view of Mayo et al. (US Patent Application Publication No. 2025/0247314 A1 herein after Mayo).
Per claim 8:
Bu modified by Ojha sufficiently discloses the system as set forth above, but Bu modified by Ojha does not explicitly disclose: input data set further comprises context data obtained via a domain knowledge graph.
However, Mayo discloses:
input data set further comprises context data obtained via a domain knowledge graph (At least see ¶[0050] - alert group manager 236 may update alert group contexts based on alert contexts received from compute nodes).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Mayo into Bu modified by Ojha’s invention because Mayo’s teaching would provide capabilities to Bu modified by Ojha manage the scale of an infrastructure implemented to group alerts based on a context, wherein node may update an alert group context, corresponding to an alert group the alert was grouped with, based on the determined alert; thus, dynamically manage assignments of one or more services to a compute node of a set of compute nodes to efficiently group alerts for the one or more services (see ¶[0003]).
Per claim 18:
Mayo discloses:
input data set further comprises context data obtained via a domain knowledge graph.
limitation as depicted in this method claim is as similar as claim 8 above; as such, the limitation is rejected using same rational as claim 8 above.
CONCLUSION
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZIAUL A. CHOWDHURY whose telephone number is (571)270-7750. The examiner can normally be reached on 9:30PM 6:30PM Monday -Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung S. Sough can be reached on 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Status information for published applications may be obtained from Patent Public Search tool (for all users) – A link to the Patent Public Search Tool is available at www. Uspto.gov/PatentPublicSearch. To find a U.S. patent or U.S. patent application publication, open the Patent Public Search tool by selecting “Start search”. Type the U.S. patent or U.S. patent application publication number in the “Search” panel without any punctuation and followed by an”.pn.”.
Should you have questions on access to the system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ZIAUL A CHOWDHURY/ Primary Examiner, Art Unit 2192
Prosecution Insights