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 .
This action in response to the amendment/request for reconsideration dated 08/26/2025. Claims 21-40 are pending.
The rejection of claims 31-40 under 35 U.S.C. § 112(b) is withdrawn in view of the amendment to claim 31.
Claims 21-22, 24-32, and 34-40 have been rejected under the doctrine of nonstatutory (obviousness-type) double patenting, however, the applicants have not responded to the rejection.
Claims 21-22, 24-32 and 34-40 remain rejected on the ground of nonstatutory double
patenting as being unpatentable over claims 1-8 and 10 of U.S. Patent No. ll,887,018. The rejection set forth in the 3/20/2025 action is hereby incorporated.
The rejection of claims 21-40 under 35 U.S.C. 101 because the claimed invention is
directed to non-patentable subject matter (i.e., the claims are directed to an abstract idea
without significantly more) is hereby withdrawn.
Claims 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over Frieder et
al. U.S. Pub. No. 2015/0235138 (hereinafter "Frieder") in view of Raza U.S. Pub. No.
2015/0294257 (hereinafter "Raza"). The rejection set forth in the 3/20/2025 action is hereby incorporated.
Response to Arguments
Applicant's arguments filed in the 08/26/2025 response have been fully considered but they are not persuasive for the reasons given below.
On page 11 of the 08/26/2025 response, applicants argue:
Even if modified as suggested by the Patent Office, however, the modified system would not "identify a geographic region of interest" by identifying a deviation from a baseline based on the analysis of the additional documents and "identifying the relevant geographic region of the one or more additional documents" as recited in the claims.
Applicant appears to be arguing that relevant section of Reza does not teach identifying a geographic region by identifying a deviation from a baseline.
The office action stated:
While Frieder at paragraphs [0066]-[0067] discloses tracking a frequency of words and thresholding and clustering of concepts and hypotheses related to document analysis, Frieder does not disclose:
identifying, by the server, a baseline for each of the one or more temporal metrics;
However, Raza at paragraph [0023] teaches a sentiment engine that contains an ontology that identifies positive and negative terms in unstructured data and creates a baseline model for making a determination from unstructured data in the comments section of a document.
The office action also stated:
While Frieder at paragraph [0067] discloses thresholding of concepts or hypotheses
related to document analysis, Frieder does not disclose:
identifying, by the server based on the analysis of one or more of the additional
documents, a deviation from at least one baseline identified for at least one of the one or more
temporal metrics; and identifying a geographic region of interest, by the server, by identifying
the relevant geographic region of the one or more additional documents.
The Reza reference teaches “deviation” as follows.
[0023] Sentiment analysis engine 154 contains an ontology that identifies the positive and negative terms in unstructured data. Terms in the ontology can be refined on an ongoing basis. In one embodiment, sentiment analysis engine 154 can process unstructured employee data that is associated with a churn metric and generate a baseline for the churn metric. In other examples, structured employee data associated with an employee metric is processed by machine learning engine 152 and unstructured employee data associated with the same employee metric is processed by sentiment analysis engine 154. Organization model generator 150 can combine the processed data from machine learning engine 152 and sentiment analysis engine 154 to create the baseline for the employee metric. For instance, a performance review document can include structured fields and a comments section. The structured fields can be processed by machine learning engine 152 while the comments section is processed by sentiment analysis engine 154. The results can be combined to create the baseline for employee performance.
Reza, although does not expressly recite the word, “deviation,” it does provide teaching from which “deviation” can be inferred. As found in [0023] above, in Reza unstructured data is processed (using ML engine) to distinguish positive and negative terms, the ontology is refined on an ongoing basis, and the results of the ongoing processing of unstructured data is combined.
Applicants’ disclosure describes “deviation” as follows:
[0062]The event recognition module 280 analyzes the documents in the data 210 to determine a baseline for each metric 218 (or aggregates of the metrics 218). Deviations from those baselines may be indicative of an event of interest in a particular domain (e.g., food integrity). Accordingly, the event recognition module 280 identifies deviations from those baselines and outputs information indicative of those deviations (e.g., alerts) to the user (e.g., via a client device 160). In other words, the metrics are aggregated and once the deviations are identified from the baselines, and then, alerts are issued.
The positive and negative terms in Reza are differentiated in the same fashion as described in applicants’ disclosure par. [0062].
Therefore, contrary to applicants opinions, the Reza reference does teach the deviation.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HOSAIN T ALAM whose telephone number is (571)272-3978. The examiner can normally be reached Mon-Thu, 8:00 - 4:30.
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.
The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HOSAIN T ALAM/Supervisory Patent Examiner, Art Unit 2132