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 communication is responsive to the application filed on 02/13/2025.
Claims 1-5 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 10/21/2025 complies with the provisions of M.P.E.P 609. The information referred to therein has been considered as to the merits.
Abstract
The Abstract filed on 02/13/2025 has been considered as to the merits.
Drawings
The Drawings filed on 02/13/2025 have been considered as to the merits.
Claim Objections
Claim 1 and 5 are objected because of the followings:
- Claim 1 recite the sign “-“ in front the limitations, which needs to be removed from the claim.
- Claims 1 and 5: recite the term “those”. It is unclear what Applicant refers by “those”, only what refers by “those” should set forth in the claimed. Clarification is advised.
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.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 5 is direct to “a system for automatically identifying import and urgent news…", wherein the claim does not positive recited a processor or a memory. It is at best, for use with the system claims, where all of the elements would reasonably be interpreted by one of ordinary skill in light of the disclosure as software, such the system is software, per se. Therefore, renders the system at most software per se, failing to fall within a statutory category. Thus, in order to overcome this 35 USC § 101 rejection the claim needs to be amended to include physical computer hardware (i.e. a memory, a processor) to execute the software components. See MPEP § 2106.01
Accordingly, the claims lack the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 USC 101. They are clearly not a series of steps or acts to be a process nor are they a combination of chemical compounds to be a composition of matter. As such, they fail to fall within a statutory category. They are, at best, functional descriptive material per se.
Claims 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claim 1 and 5 are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
“Using the difference to identify important and urgent news in the large set of data; clustering the textual-format data into a plurality of clusters; for each cluster, calculating the distances to all other clusters in the plurality of clusters and from those calculated distances determining a radius and a median of those calculated distances”.
The limitations “identify .. clustering…calculating”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the limitations “identify .. clustering…calculating”, in the context of the claim encompasses one can manually or mentally with the aid of pen and paper clustering data to identify important and urgent news.
If a claim limitation, 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, the claim recites an abstract idea.
Step 2A, Prong Two:
This judicial exception is not integrated into a practical application. The claims recite the additional elements “
“obtaining the large set of data in a textual-format, the large set of textual-data data contain a plurality of individual texts; and then obtaining a difference between the radius and the median”, amount to data gathering steps which is considered to be insignificant extra-solution activity. (See MPEP 2106.05(g).
Step 2B:
“obtaining the large set of data in a textual-format, the large set of textual-data data contain a plurality of individual texts; and then obtaining a difference between the radius and the median”. These are identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Looking at the claims as a whole does not change this conclusion and the claim appears to be ineligible.
Claim 2, recites “the radius calculated using 90% rather than 100%”. The judicial exception is not integrated into a practical application. In particular, the additional limitation of "the radius calculated" has been discussed above with respect to the abstract idea (i.e., "Mental Processes") and do not amount to significantly more than the above-identified judicial exception.
Claim 3, recites “applying a dimension reduction technique to the textual-format data before clustering”. The judicial exception is not integrated into a practical application. In particular, the additional limitation of "applying" has been discussed above with respect to the abstract idea (i.e., "Mental Processes") and do not amount to significantly more than the above-identified judicial exception.
Claim 4, recites “the clustering is performed using one or more techniques selected from the group comprising: HDBSCAN, Agglomerative, and KMeans”. The judicial exception is not integrated into a practical application. In particular, the additional limitation of " the clustering is performed using…" has been discussed above with respect to the abstract idea (i.e., "Mental Processes") and do not amount to significantly more than the above-identified judicial exception.
Claim 5, is a system claim to utilizing the method of claim 1. Therefore, claim 5 is rejected under the same rational as claim 1 above.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1: recites the limitations:
- “the large set of textual-data data”. This limitation is not previously introduced. There is insufficient antecedent basis for this limitation in the claim. Applicant is required for clarification/correction is required.
- “using the difference to identify important and urgent news in the large set of data”. The claim provides no guidance as to what/how this step is identified/performed as such. Applicant is required for clarification/correction is required.
- All dependent claims are rejected under the same rational as their based claim as above.
Claim 2: recites the limitation “the radius calculated using 90% rather than 100%”. This claim is an omnibus type claim. Applicant is required for clarification/correction is required.
Claim 5: is a system claim to utilizing the method of claim 1. Therefore, claim 5 is rejected under the same rational as 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.
Claims 1 and 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over
Wang et al. (US 2021/0109954), hereinafter “Wang”, in view of Meehan et al. (US 2021/0406272), hereinafter “Meehan”.
As per claim 1, Wang discloses a method for automatically identifying important and urgent news (IUN) in a large set of data (abstract and par. [0013], a methodology that uses a combination of entity-based news tracking, clustering, relevancy, and ranking models to detect news events, filter out noise, cluster the same event into each of the respective clusters, assign relevance of news event to main entity, and rank each news event cluster based on the importance of the event to the overall company):
- obtaining the large set of data in a textual-format, the large set of textual-data data contain a plurality of individual texts (abstract and par. [0005], extracting using a natural language processing (NLP) technique a set of events from a set of text strings of speaker turns and identifies a set of clusters of events based on the set of events and labels each cluster of events in the set of clusters of events to generate a set of labeled clusters of events);
- clustering the textual-format data into a plurality of clusters (par. [0005] a set of events from a set of text strings and identifies a set of clusters of events based on the set of events and labels each cluster of events in the set of clusters of events to generate a set of labeled clusters of events ; and par. [0047], the events output by the event extraction system can serve as input for both the cluster generation and the cluster assignment system);
- using a difference to identify important and urgent news in the large set of data (par.[0071], clustering and ranking 221 rank both clusters and news in each cluster. Important clusters rank higher (i.e., cluster-level ranking), with each cluster showing the most representative news for each cluster (i.e., news-level ranking); and par. [0124] In one illustrative example, cluster ranking 328 ranks clusters by three factors sequentially: 1) update date; 2) clustering ranking score; and 3) cluster size. The updated date is a good indicator of the event recentness. Ranking by the update date ensures analysts do not miss new news events. A clustering ranking score is one indicator of event importance. In one illustrative example, the clustering ranking score is computed by taking the maximum news ranking score of all the news within the cluster and summing the weighted cluster size. Cluster size is another indicator of event importance. Important clusters tend to have large sizes because more news sources may cover the news events).
However, Wang does not disclose “for each cluster, calculating the distances to all other clusters in the plurality of clusters and from those calculated distances determining a radius and a median of those calculated distances and then obtaining a difference between the radius and the median”.
On the other hand, Meehan discloses for each cluster, calculating the distances to all other clusters in the plurality of clusters and from those calculated distances determining a radius and a median of those calculated distances and then obtaining the difference between the radius and the median (par. [0013], identifying subpopulations of the second cell population and recognizing at least some of the subpopulations of the second cell population as corresponding to at least some of the subpopulation labels of cells in the first training cell population based on the second reduced parameter data includes one or more of: a) detecting clusters in the second reduced parameter data and determining a most similar median or mean of clusters between the subpopulations in the first training cell population and the detected clusters in the second reduced parameter data; b) detecting clusters in the second reduced parameter data and determining QFM dissimilarity scores on combinations of subpopulations in the first training cell population and the detected clusters in the second reduced parameter data; par. [0111]-[0112], UMAP builds a k-nearest neighbor graph among data points. UMAP assigns weights among pairs in the k-nearest neighbor graph according to how close points are in HiD; par. [0116]-[0120], UMAP can be described as seeking to minimize cross-entropy. Cross-entropy measures the difference between high and low dimensional embeddings. The best low dimensional embedding should minimize cross-entropy. When doing any type of parameter reduction, UMAP performs much of the work of identifying subsets and classification by structuring the input data into data islands that make it easy for almost any clustering method to identify subsets or subpopulations as being points in the low D space that “clump” together, wherein supervised UMAP computes closeness in terms of both the topological characteristics of the unreduced parameters; and par. [0136], This strategy requires k-dimensional bins of variable size that “adapt” to the structure of the data. Multivariate adaptive binning begins by calculating the median and variance of the combined data for each of the k-dimensions included in the comparison).
Therefore, one having ordinary skill in the art would have been obvious before the effective filing date of the claimed invention to have modified the system of Wang to include the samples comparison and matching the curse of dimensionality in order to provide more efficiently and accurately identify relevant subsets among compatible sample populations.
As per claim 3, The combination of Wang and Meehan discloses the invention as claimed. In addition, Meehan further discloses applying a dimension reduction technique to the textual-format data before clustering (Meehan discloses at par. [0119]-[0120], When doing any type of parameter reduction, UMAP performs much of the work of identifying subsets and classification by structuring the input data into data islands that make it easy for almost any clustering method to identify subsets or subpopulations as being points in the low D space that “clump” together, wherein supervised UMAP computes closeness in terms of both the topological characteristics of the unreduced parameters (e.g., measurements) of the input data as well as the common external classification labels for the input data. When supervisory labels are involved in UMAP's reduction (e.g., for supervised UMAP), subsets are identified through a recognition method, which identifies previously known subsets corresponding to subsets in supervising or training data set. According to Applicant’s Specification (par. [0035]), a dimension reduction technique is a UMAP (Uniform Manifold Approximation and Projection).).
As per claim 4, The combination of Wang and Meehan discloses the invention as claimed. In addition, Meehan further discloses the clustering is performed using one or more techniques selected from the group comprising: HDBSCAN, Agglomerative, and KMeans (par. [0163], The hierarchical tree-structure visualization in the QF-tree (phenogram) of the clusters or subsets enables agglomerative arrangement of identified clusters or subsets based on their dissimilarity in the space of measured parameters).
Therefore, one having ordinary skill in the art would have been obvious before the effective filing date of the claimed invention to have modified the system of Wang to include Agglomerative grouping feature as disclosed by Meehan to increase data performance and greater scalability of data.
As per claim 5, is a system claim to utilizing the method of claim 1. Therefore, claim 5 is rejected under the same rational as claim 1 above.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over
Wang in view of Meehan, and further in view of Chachek et al. (US 2025/0029170), hereinafter “Chachek”.
As per claim 2, The combination of Wang and Meehan discloses the invention as claimed except “the radius calculated using 90% rather than 100%”.
On the other hand, Chachek discloses the radius calculated using 90% rather than 100% (par. [0271], entirely or in a level of confidence that is greater than a pre-defined threshold (e.g., at least 90 percent confidence)).
Therefore, one having ordinary skill in the art would have been obvious before the effective filing date of the claimed invention to have modified the system of cited references to include the features as disclosed by Chachek in order to provide the grouping data more accurate result.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (see PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Loan T. Nguyen whose telephone number is (571) 270-3103. The examiner can normally be reached on Monday from 10:00 am - 6:00 pm, Thursday-Friday from 10:00 am - 2:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached on (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-270-4103. 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 http://pair-direct.uspto.gov. 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.
11/10/2025
/LOAN T NGUYEN/Examiner, Art Unit 2165