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 .
Applicant’s amendment has necessitated the withdrawal of the previous 101 rejection.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-7, 9-14, 16 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gonzalez Macias et al (Gonzalez hereinafter, US PUB:2022/0342868).
Re claim 1. Gonzalez discloses a method of identifying data connections, the method comprising: automatically generating a connection analysis prompt in text format from one or more data items of a first dataset for identifying one or more data items of a second dataset, wherein the second dataset is different from the first data set (i.e., generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset, see paras 0015-0017); and applying the generated connection analysis prompt to a large language model (LLM) to produce an output from the LLM of whether said one or more data items of the first dataset are connected to said one or more data items of the second dataset; and when said one or more data items of the first dataset have one or more connections to said one or more data items of a second dataset, producing an alert (see paras 0023, 0033) (see paras 0070).
Re claims 2. Gonzalez discloses of claim 1, wherein said one or more data items of the first dataset comprise a network of customer data items which are linked to a customer dataset (see paras 0078).
Re claim 3. Gonzalez discloses a method of claim 1, wherein applying the generated connection analysis prompt comprises identifying data items within said one or more data items of a first dataset which are terminal data items and determining whether said terminal data items are similar to said one or more data items of the second dataset using LLM (i.e. anomaly detection using machine learning model, see paras 0075).
Re claim 4. Gonzalez discloses a method of claim 1, further comprising updating said first dataset based on said connections between said one or more data items of the first dataset and said one or more data items of the second dataset (see paras 0070).
Re claim 6. Gonzalez discloses a method of claim 1, wherein said one or more data items of the first dataset are extracted from an interaction transcript (see paras 0143).
Re claim 7. Gonzalez disclose a method of claim 1, wherein said automatic generation of the connection analysis prompt for identifying connections is generated from previously generated connection analysis prompts for identifying connections of said customer (see paras 0070).
Re claim 9. Gonzalez discloses a method of claim 1, further comprising updating said first dataset when said one or more data items of the first dataset have been linked to said one or more data items of the second dataset (see paras 0070).
Re claim 10. Gonzalez discloses a method of claim 1, wherein said connections are connections between said one or more data items of the first dataset and data items of a fraud dataset and said connection analysis prompt is applied to a machine learning model to analyze whether said one or more data items of the first dataset have connections to said one or more data items of said fraud dataset (see fig.3 element 306, see fig.10)
Re claim 11. Claim 11 recites similar limitations to claim 1 and thus rejected using the same art and rationale as in claim 1, above.
Re claim 12. Claim 12, though a system claim, recites similar limitations to claim 2 and thus rejected using the same art and rationale as in claim 2, above.
Re claim 13. Claim 13, though a system claim, recites similar limitations to claim 3 and thus rejected using the same art and rationale as in claim 3, above.
Re claim 14. Claim 14, though a system claim, recites similar limitations to claim 4 and thus rejected using the same art and rationale as in claim 4, above.
Re claim 16. Claim 16, though a system claim, recites similar limitations to claim 7 and thus rejected using the same art and rationale as in claim 7, above.
Re claim 18. Claim 18, though a system claim, recites similar limitations to claim 9 and thus rejected using the same art and rationale as in claim 9, above.
Re claim 19. Claim 19, though a system claim, recites similar limitations to claim 10 and thus rejected using the same art and rationale as in claim 10, above.
Re claim 20. Claim 20 recites similar limitations to claim 1 and thus rejected using the same art and rationale as in claim 1, above.
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.
Claim(s) 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez.
Re claim 8, 17. Gonzalez does not explicitly disclose a method according to claim 1, wherein said connection analysis prompt comprises said one or more data items of a first dataset and one or more operators for querying a database comprising said one or more data items of the second dataset. However, official notice is taken that using specific data terms and operators to query the database is old and well-known in the computing art. Thus, it would have been obvious to one of ordinary skill in the art to incorporate what is old and well-known in the art in the system of Gonzalez for anomaly detection in dataset.
Response to Arguments
Applicant's arguments filed on 04/30/26 have been fully considered but they are not persuasive.
In response to applicant’s argument that Gonzalez does not disclose "automatically generating a connection analysis prompt in text format" from one or more data items of a dataset, the examiner disagrees. Gonzalez discloses “timeseries datasets may include different types of data. For example, one timeseries dataset may include event log data from computing systems and another timeseries dataset may include temperature data measured around those computing systems. Thus, the timeseries datasets may include a first dataset having a first type of data and a second dataset having a second type of data. The datasets with different types of data may be input into different anomaly detection models. Thus, the anomaly detection system may select, based on the first type of data, a first anomaly detection model for the first dataset. For example, the anomaly detection system may select an anomaly detection model suited for processing event log data” (see para 0017. Also see para 0024), which reads on "automatically generating a connection analysis prompt in text format."
The applicant argues that Gonzalez does not anticipate the claimed application of prompts to an LLM, the examiner disagrees. The examiner contends that LLMs are built using deep learning, which is a subset of machine learning that uses neural networks with many layers. Accordingly, Gonzalez discloses application of prompts to the machine learning model may include an artificial neural network (see para 0071).
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OJO O OYEBISI whose telephone number is (571)272-8298. The examiner can normally be reached on Monday-Friday, 9am-7pm. 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, Christine Behncke can be reached at 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR 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.
/OJO O OYEBISI/Primary Examiner, Art Unit 3695
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