DETAILED ACTION
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
Claims’ Status: The previous rejections of the claims under 35 USC §101 have been maintained. The previous rejections of the claims under 35 USC §103(a) have been withdrawn, in light of the newly amended claim language. New rejections under 35 USC §112(b) have been set forth, in light of the newly amended claim language
Regarding the previous rejections of the claims under 35 USC §103(a), Applicant’s arguments on pages 9-11 appear to be directed to the newly amended claim language. These arguments are moot as the previous rejections of the claims under 35 USC 103(a) have been withdrawn, in light of the newly amended claim language. New art has been cited to address the amended claim language.
Applicant's arguments, filed 8/21/2025, concerning the previous rejection of the claims under 35 USC §101 have been fully considered but they are not persuasive.
Regarding the previous rejection of the claims under 35 USC §101, Applicants argue on pages 7-9 (esp. page 9) that that because the specification indicates that the alleged judicial exception / abstractness represents an improvement in machine learning and therefore the claims are tied to a practical application.
The Office respectfully disagrees. The claims merely present a manipulation or labeling of data. The details of the use / follow-on processing of that data is not reflected in the claims.
And, it is further noted that generic computers performing generic computer functions to apply an abstract idea do not amount to significantly more than the abstract idea of organizing information through mathematical correlations. It is noted that the Internet/computer limitations are simply a field of use that attempt to limit the abstract idea to a particular technological environment and do not add significantly more than the abstract idea itself. Viewing the limitations as a combination does not add anything further than looking at the limitations individually. Therefore, the previous rejection of the claims under 35 USC §101 is maintained.
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.
Claims 1-11 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 pre-AIA the applicant regards as the invention.
Regarding independent claims 1, 8 and 10:
First, it is unclear what is meant by the newly claimed expression “not the maximum portion among the data within the second plurality of clusters as data classified into different clusters in the first plurality of clusters”. The language is awkward, and the specification merely mentions the terminology “maximum proportion” once. No explanation is provided in the specification as to how a “maximum proportion” (and then it’s inverse, which presumedly results in a minimum value of some sort) is determined.
Additionally, it is unclear what is meant by the newly claimed expression “a visualization of the first data set”. This terminology is overbroad. It’s not clear what the scope of this terminology is. Is the recited visualization a set of points, images, text, etc.? There appears to be a missing essential element.
Therefore, the scope of each claim is ambiguous.
Claims 2-7, 9 and 11 depend upon claims 1, 8 and 10, respectively, and do not correct the issues set forth above. Therefore, these claims are likewise rejected.
Claim Rejections – 35 U.S.C. § 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 1-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
These claims are rejected under 35 USC §101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites at a very high-level processing and displaying a dataset, classifying two datasets and displaying the results of the second dataset, identify/name cluster data, and output data based upon cluster information meeting certain criteria. Thus, the claims encompass the performance of the limitations in the mind, or alternatively the solving of a math problem (i.e., a series of mathematical steps) that are not tied to a practical application.
Regarding independent claim 1:
Statutory Category: Yes, recites a generic computing platform for implementing a series of steps executed (therefore a process embodied in a system).
Step 2A, Prong 1 (Judicial Exception Recited?): Yes. The claim recites a series of steps, embodied in generic hardware processing elements, including processing / displaying / classifying / categorizing / clustering data sets, including establishing labels for such clusters, and identifying/naming cluster data, and outputting data based upon cluster information meeting certain criteria. These concepts, under a broadest reasonable interpretation, encompass the performance of the limitations in the mind, or alternatively the solving of a math problem (i.e., using/assigning a vague/unstated mathematical concept, to group data). These limitations can be performed in the mind or simply with the aid of pencil and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic components, then it falls within the “Mental Processes” grouping of abstract ideas. A claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
Alternatively, use of a mathematical concept integrated into a practical application may represent patent eligible subject matter, but the mere solving of a math problem is considered an abstract idea.
It is further noted that generic hardware is also claimed.
Other than reciting additional generic elements, such as processors and storage, nothing in the claim precludes its characterization as either “Mental Processes” or a mathematical concept. For example, the claim encompasses the performance of mathematical operations/steps based upon mathematical relationships to group/label/classify data. These limitations are therefore reasonably characterized as encompassing mathematical concepts (i.e., an abstract idea).
Accordingly, the claim recites an abstract idea. I.e., these limitations encompass mental processes, or in the alternative a mathematical concept (an abstract idea).
Step 2A, Prong 2 (Integrated into a Practical Application?): No. The claim recites a series of steps, embodied in generic hardware processing elements, including processing / displaying / classifying / categorizing / clustering data sets, including establishing labels for such clusters, and identifying/naming cluster data, and outputting data based upon cluster information meeting certain criteria. Under a broadest reasonable interpretation, other than reciting additional generic computing elements, such as processors and storage, nothing in the claim precludes characterization as either Mental Processes or a Mathematical Concept.
Furthermore, the limitation of displaying data (i.e., “output data …”) is not sufficient to show an improvement to technology according to, for example: Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48.
or transmitting data: the limitation of transmitting data (i.e., “output data …”) is identified as insignificant extra-solution activity, and this element is well-understood, routine, and conventional as evidenced by the court cases set forth in MPEP 2106.05(d)(II). E.g., “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, In., 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. And, the limitation reciting the use of a web portal is linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) – and, therefore remains insignificant extra-solution activity and does not provide significantly more.
The computing elements are recited at a high-level of generality such that the claim amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B (Inventive Concept Provided?): No. As discussed with respect to Step 2A, the elements (i.e., steps of steps of processing / displaying / classifying / categorizing / clustering data sets, including establishing labels for such clusters, and identifying/naming cluster data, and outputting data based upon cluster information meeting certain criteria) in the claim amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using generic computer components (e.g., a processor and memory) cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Furthermore, the limitation of displaying or transmitting data (i.e., “output data …”) is identified as insignificant extra-solution activity, and this element is well-understood, routine, and conventional as evidenced by the court cases set forth in MPEP 2106.05(d)(II). E.g., “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, In., 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. And, the limitation reciting the use of a web portal is linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) – and, therefore remains insignificant extra-solution activity and does not provide significantly more. And, Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48, is not sufficient to show an improvement to technology.
Therefore, the claim is not patent eligible, and therefore is reasonably rejected under 35 USC §101.
Independent claims 8 and 10 are each substantially similar to claim 1. Therefore, these claims are likewise rejected.
Claims 2-7, 9 and 11 depend upon claims 1, 8 and 10, respectively, and do not correct the issues set forth above. These claims merely further recite generic hardware/storage, further manipulate data, or further classify/categorize the data items. Therefore, these claims are likewise rejected.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5 and 7-11 are rejected under 35 U.S.C. §103 as being unpatentable over Scriven et al (US Patent Application Publication No. 2021/0097335, hereafter referred to as “Scriven”) in view of Ackerman et al (US Patent Application Publication No. 2022/0181027, hereafter referred to as “Ackerman”) and Basel et al (US Patent Application Publication No. 2020/0134510, hereafter referred to as “Basel”).
Regarding independent claim 1: Scriven teaches A labeling assistance system comprising: a memory storing instructions; and one or more processors configured to execute the instructions to: (See Scriven Figure 5, especially #501 Processor(s), and #502 Memory and #505 Persistent Storage.) process a first data set; (See Scriven paragraphs 0034 and 0038 discussing exemplary processing such as determining a training timeline for an original data set based upon the labels of the original data set); generate a visualization of the first data set, and output, on a display, the visualization; (See Scriven paragraph 0034 and figure 1A #108 teaching an output / display capability, and paragraphs 0038-0039 the generation of a plot, it having been implied that if one skilled in the art can generate a plot that such person can also display that plot.); generate a first plurality of clusters by classifying a first data set, … ; (See Scriven Abstract discussing the generation of clusters from the classes of a training dataset.) generate a second plurality of clusters by classifying a second data set, which is a data set containing at least some of the data to be labeled; (See Scriven para 0025 discussing the creation of a second, generalized set of clusters and the generation of logical containers for original labels.) and output data included in the second plurality of clusters, which were classified into different clusters in the first plurality of clusters. (See Scriven para 0041 and Fig. 4 and Fig. 5 #509 teaching the outputting of candidate classifiers created by the new, dynamic model.)
However, Scriven does not explicitly teach the remaining limitations as claimed. Ackerman, though, teaches … which is a data set to be labeled, through unsupervised learning; (See Ackerman Abstract discussing the use of datasets labelled in accordance with clusters generated by an unsupervised learning model.)
It 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 to apply the teachings of Ackerman for the benefit of Scriven, because to do so provided a designer with options to implement a system for more thoroughly/accurately analyzing / quickly analyzing data, as taught by Ackerman in paras 0012 and 0041, for example. These references were all applicable to the same field of endeavor, i.e., machine learning techniques.
Additionally, Scriven in view of Ackerman does not explicitly teach the remaining limitations as claimed. Basel, though, teaches adding cluster identification information to each data in the first plurality of clusters; (See Basel paragraph 0091, for example, discussing the assigning of labeling information to clusters in a group of clusters.) and outputting data in which the cluster identification information is not the maximum proportion among the data within the second plurality of clusters as data classified into different clusters in the first plurality of clusters. (See Basel paragraphs 0094-0096 discussing the determination of whether a cluster label associated with first and second datasets meet [i.e., are above or below] membership quantity thresholds. It is noted that this enables generation and training [when such data is output to] a machine learning classifier.)
It 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 to apply the teachings of Basel for the benefit of Scriven in view of Ackerman, because to do so provided a designer with options to implement a system in which a second group of clusters are associated with labels that are associated with a first group of clusters, thereby associating later and earlier time periods / retaining earlier learned historical data, as taught by Basel in paragraph 0070, for example. These references were all applicable to the same field of endeavor, i.e., machine learning techniques.
Regarding claim 2: Scriven teaches wherein the processor is configured to execute the instructions to generate the second data set according to the generated first plurality of clusters from the data set to be labeled. (See Scriven para 0027 teaching a reduction in the corresponding (second) corpus size.)
Regarding claim 3: Scriven teaches wherein the processor is configured to execute the instructions to generate the second data set by performing labeling for each cluster on data classified into one of the first plurality of clusters from the data set to be labeled. (See Scriven para 0025 teaching the determination of an optimal cluster size, and creation of generalized clusters from the original clusters.)
Regarding claim 4: Scriven teaches wherein the processor is configured to execute the instructions to generate, from the data set to be labeled, a data set classified into a cluster selected from the first plurality of clusters, as the second data set. (See Scriven para 0027 teaching a reduction in the corresponding (second) corpus size. See also, Scriven para 0025 teaching the determination of an optimal cluster size, and creation of generalized clusters from the original clusters.)
Regarding claim 5: Scriven teaches wherein the processor is configured to execute the instructions to generate, from the data set to be labeled, the second data set by excluding one or more pieces of data that are not classified into any of the first plurality of clusters. (See Scriven para 0025 teaching the creation of generalized clusters from the original [more specific] clusters.)
Regarding claim 7: Scriven teaches wherein the processor is configured to execute the instructions to display statistical information of the clusters for each classification process. (See Scriven paras 0016 and 0022 teaching the storage of statistical information, in the context of Fig. 5 #509 and para 0049 teaching the display of data.)
Claims 8 and 9 are substantially similar to claims 1 and 2, respectively, and therefore likewise rejected.
Claims 10 and 11 are substantially similar to claims 1 and 2, respectively, and therefore likewise rejected.
Claim 6 is rejected under 35 U.S.C. §103 as being unpatentable over Scriven et al (US Patent Application Publication No. 2021/0097335, hereafter referred to as “Scriven”) in view of Ackerman et al (US Patent Application Publication No. 2022/0181027, hereafter referred to as “Ackerman”), Basel et al (US Patent Application Publication No. 2020/0134510, hereafter referred to as “Basel”) and Jaegul Choo et al. (“iVisClassifier: An Interactive Visual Analysics System for Classification Based on Supervised Dimension Reduction”, VAST 2010, Salt Lake City, UT, October 25-26, 2010, pp. 27-34, hereafter referred to as “Choo”).
Regarding claim 6: Scriven in view of Ackerman and Basel does not explicitly teach the remaining limitations as claimed. Choo, though, teaches wherein the processor is configured to execute the instructions to reduce the dimensions of the data set to be labeled, graphically draw the reduced-dimension data included in the first plurality of clusters and the reduced-dimension data included in the second plurality of clusters in a manner that allows identification by cluster, and display the reduced-dimension data included in the second plurality of clusters, which were classified into different clusters in the first plurality of clusters, in a different manner from other data. (See Choo last paragraph on page 27 carrying over to the 1st paragraph on page 28 discussing the use of dimension reduction methods involving cluster labels. See also, page 30 Figure 4 teaching a GUI tool for displaying / manipulating such data.)
It 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 to apply the teachings of Choo for the benefit of Scriven in view of Ackerman and Basel, because to do so provided a designer with options to implement a system that provided users with meaning of reduced data dimensions and the ability manage such data, as taught by Choo on page 30 in Figure 4 and the 1st paragraph in the section entitled Basis view. These references were all applicable to the same field of endeavor, i.e., data clustering.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Relevance is provided in at least the Abstract of each cited document.
US Patent Application Publications
Basel 2020/0134510
A method includes performing a first clustering operation to group members of a first data set into a first group of clusters and associating each cluster of the first group of clusters with a corresponding label of a first group of labels. The method includes performing a second clustering operation to group members of a combined data set into a second group of clusters. The combined data set includes a second data set and at least a portion of the first data set. The method includes associating one or more clusters of the second group of clusters with a corresponding label of the first group of labels and generating training data based on a second group of labels and the combined data set. The method includes training a machine learning classifier based on the training data to provide labels to a third data set. (Abstract). In a particular implementation, the cluster and label mapping techniques described herein map labels assigned based on a first clustering operation to clusters generated during a second clustering operation. The first clustering operation and the second clustering operation are performed on different (though possibly overlapping) data sets. For example, the first clustering operation can be performed using a first data set that represents a system or machine during a first time period, and the second clustering operation can be performed using a second data set that represents the system or machine during a second time period that is subsequent to the first time period. The second data set can include more data or fewer data than the first data set. In some implementations, the second data set includes the entire first data set. In other implementations, the second data set includes a subset or portion of the first data set. Because the data sets include different data, the first clustering operation and the second clustering operation can identify different clusters (or different cluster boundaries). Further, many clustering techniques use randomized operations, e.g., to select an initial number of cluster centers or to designate initial cluster centers, which can also result in differences between clusters identified by the first clustering operation and clusters identified by the second clustering operation. The cluster and label mapping techniques described herein identify commonalities between clusters from the first clustering operation and clusters from the second clustering operation to map the labels assigned based on the first clustering operation to corresponding clusters from the second clustering operation. The commonalities identified can include, for example, common data points, similar relative positions in a cluster space, or other similar features, as described further below. Thus, the cluster and label mapping techniques described herein simplify the process of updating the machine learning classifier by preserving information across clustering operations. (paras 0007-0008). The first clustering operations may be any type of clustering operations, including centroid clustering operations (such as K-Means clustering), hierarchical clustering operations, mean-shift clustering operations, connectivity clustering operations, density clustering operations (such as density-based spatial clustering applications with noise (DBSCAN)), distribution clustering operations, expectation-maximization (EM) clustering using Gaussian mixture models (GMM), or other types of clustering operations or algorithms. (para 0027).
Ide 2019/0244132
[Object] To previously predict learning performance in accordance with the labeling status of learning data. [Solution] Provided is an information processing device including: a data distribution presentation unit configured to perform dimensionality reduction on input learning data to generate a data distribution diagram related to the learning data; a learning performance prediction unit configured to predict learning performance on the basis of the data distribution diagram and a labeling status related to the learning data; and a display control unit configured to control a display related to the data distribution diagram and the learning performance. The data distribution diagram includes overlap information about clusters including the learning data and information about the number of pieces of the learning data belonging to each of the clusters. (Abstract).
Tanaka 2020/0065621
An information processing device according to an embodiment includes a determination unit and a first training unit. The determination unit determines whether an unlabeled data point whose class label is unknown is a non-targeted data point that is not targeted for pattern recognition. The first training unit trains a first classifier for use in the pattern recognition through semi-supervised learning using a first training dataset including unlabeled data determined not to be the non-targeted data and not including unlabeled data determined to be the non-targeted data. (Abstract).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner ROBERT STEVENS whose telephone number is (571) 272-4102. The examiner can normally be reached Mon - Fri 6:00 - 2:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on (571) 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT STEVENS/Primary Examiner, Art Unit 2164
November 1, 2025