DETAILED ACTION
Claims 1-20 are pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The disclosure is objected to because of the following informalities: The CROSS REFERENCE TO RELATED APPLICATIONS section must be updated. Appropriate correction is required.
Drawings
The drawings are objected to because figures 3 and 4 contain illegible text, shaded black and grey areas, and/or lines that are not uniformly thick and well defined. See MPEP §608.02, 37 CFR 1.84 (I), and 37 CFR 1.84 (m).
Corrected drawing sheets in compliance with 37 CFR 1.121 (d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.1 21 (d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2 and 9 of U.S. Patent No. 12406274 (‘274). Although the claims at issue are not identical, they are not patentably distinct from each other.
Independent claims 1, 19 and 20 of the current application correspond to independent claim 1 of ’274. Independent claims 1, 19 and 20 of the current application are merely broader versions of independent claim 1 in ‘274, while independent claims 1 and 19 of the current application are the method and computer-readable storage medium claims corresponding to the apparatus claim 1 in ‘274. As a result, the claims under examination are anticipated by the reference claim.
Additionally, dependent claims 2-18 of the current application recite elements found in independent claim 1 and/or at least dependent claims 2 and 9 of ‘274.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, method claims 1-18 are directed to a series of steps, computer-readable storage medium claim 19 is directed to instructions executed by a processor, and apparatus claim 20 is directed to a processor; and a memory storing instructions. Thus the claims are directed to a process, manufacture, and machine, respectively.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite improving clickstream data processing, including generating, transforming, determining, shading, and displaying steps.
The limitations of generating, transforming, determining, shading, and displaying, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite generating a probability matrix based on clickstream data; transforming the probability matrix into two dimensional data; generating based on the two dimensional data, a cluster graph comprising a plurality of clusters; determining a respective center of each cluster of the plurality of clusters; determining a respective subset of the two dimensional data closest to the center of each cluster; determining based on the subsets, a respective edge of each cluster; shading based on the determined edges of each cluster, the two dimensional data within each respective subset in the cluster graph; and displaying the cluster graph on a display.
That is, other than reciting a processor, the claim limitations merely cover commercial interactions, including marketing or sales activities or behaviors and managing interactions between people, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include a processor. The processor in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a processor amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claims 2-4 recite additional feeding, identifying, applying, shading and illustrating steps directed to mathematical relationships or calculations. Claims 5-7 recite additional receiving and storing steps. Claims 8-11 and 16 recite additional labeling, and downsampling steps, and further describes the clickstream data. Claims 12-15 recite an additional lacking steps. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, a processor.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in paragraph 0033 of the specification, “Embodiments of the present invention described herein, with reference to flowchart illustrations and/or block diagrams of methods or apparatuses (the term "apparatus" includes systems and computer program products), will be understood such that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a particular machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create mechanisms for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
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 (i.e., changing from AIA to pre-AIA ) 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, 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 1-6, 11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al (US 20140101580 A1), in view of Pai et al (US 10115121 B2).
As per claim 1, Shen et al disclose a method, comprising:
generating, by a processor, a probability matrix based on clickstream data (i.e., probability models may be used as prototypes to describe the clickstreams. The "similarity" between a clickstream and a probability model may be measured by the probability at which the clickstream fits in the model. As such, SOM with probability models can be applied to map the clickstream data, ¶ 0075);
transforming, by the processor, the probability matrix into two dimensional data (i.e., map clickstream data to a two-dimensional plane to form the plurality of clickstreams, ¶ 0025);
generating, by the processor based on the two dimensional data, a cluster graph comprising a plurality of clusters (i.e., the 2D visualization 700b shown in FIG. 7B may be provided for visual cluster exploration, ¶ 0093);
determining, by the processor, a respective center of each cluster of the plurality of clusters (i.e., FIG. 6 illustrates a spiral path 610 along which each clickstream 620 is looking for a position to stay without overlap, in accordance with some embodiments. The starting point is in the center and the clickstream rectangle marches to the outer end, ¶ 0092);
shading, by the processor based on the determined edges of each cluster, the two dimensional data within each respective subset in the cluster graph (i.e., the 2D visualization 700b shown in FIG. 7B may be provided for visual cluster exploration. As previously described, each rectangular block denotes a clickstream pattern, ¶ 0093); and
displaying, by the processor, the cluster graph on a display (i.e., FIG. 7A presents the visualization 700a of clickstreams by placing them where they are mapped on the 2D plane. FIG. 7B presents the visualization 700b of clickstreams after the data has been sorted according to significance measure and then placed by searching along the spiral path, in accordance with some embodiments, ¶ 0093).
Shen et al does not disclose determining, by the processor, a respective subset of the two dimensional data closest to the center of each cluster; and determining, by the processor based on the subsets, a respective edge of each cluster.
Pai et al disclose the determining of the mathematical distances may also involve the use of a learning algorithm, such as, for example, a large margin nearest neighbor (LMNN) algorithm. In some implementations, the resulting mathematical distances may then be used to classify or group the visitor sessions using a k-nearest neighbor (KNN) algorithm (column 2, lines 40-45).
Accordingly, in some implementations, a Euclidean distance within a transformed space that allows dimension-specific weighting (e.g., different weights for different web page categories for a particular feature) may be determined. In implementations described herein, the Mahalanobis metric is used to provide a distance measurement in a large margin nearest neighbor (LMNN) algorithm, which may be employed to improve the accuracy of a group classification system, such as a k-nearest neighbor (KNN) algorithm (column 9, lines 4-24).
In one example, those visitor sessions that resulted in a purchase from the website are labeled as residing in the target group, while those visitor sessions that did not result in a purchase are labeled as belonging to the non-target group. As mentioned above, one particular classification algorithm that may be utilized is the k-nearest neighbor (KNN) algorithm (column 11, lines 8-13)
Shen et al and Pai et al are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include determining, by the processor, a respective subset of the two dimensional data closest to the center of each cluster; and determining, by the processor based on the subsets, a respective edge of each cluster in Shen et al, as seen Pai et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Shen et al disclose the clickstream data comprises sequential user navigation paths through a web-based interface or a mobile-based interface (i.e., As previously discussed, clickstreams are sequences of user actions, which may be of various lengths. Each click action may be encoded as a geometric shape, such as a rectangle, and be colored differently from the other click actions, ¶ 0086).
As per claim 3, Shen et al disclose the clickstream data comprises a plurality of pages and the probability matrix comprises a plurality of entries (i.e., probability models may be used as prototypes to describe the clickstreams. The "similarity" between a clickstream and a probability model may be measured by the probability at which the clickstream fits in the model. As such, SOM with probability models can be applied to map the clickstream data, ¶ 0075).
As per claim 4, Shen et al disclose each entry of the probability matrix comprising a respective probability of proceeding from a first one of the plurality of pages to a second one of the plurality of pages (i.e., As such, SOM with probability models can be applied to map the clickstream data, ¶ 0075).
As per claim 5, Shen et al disclose prior to generating the probability matrix: receiving, by the processor, the clickstream data over a predetermined period of time (i.e., First, at 410, clickstream data may be received. This clickstream data may be obtained from one or more databases, ¶ 0067).
As per claim 6, Shen et al disclose storing, by the processor, the clickstream data in a memory (i.e., First, at 410, clickstream data may be received. This clickstream data may be obtained from one or more databases, ¶ 0067).
As per claim 11, Shen et al disclose the cluster graph is generated based on a clustering algorithm (i.e., Although the clickstreams may be successfully projected onto a 2D space after the data mapping step, creating a visualization that can clearly present the clickstream clusters can still be an issue. This problem may be addressed by introducing a self-illustrative visual representation of clickstreams and an effective layout algorithm, ¶ 0085).
As per claim 13, Shen et al disclose determining the respective center of each cluster is based on Euclidean distances between the two dimensional data in each cluster (i.e., the similarity between an input data item and a prototype may be measured by a pre-defined similarity metric, such as Euclidean distance, ¶ 0075).
As per claims 14 and 15, Shen et al does not disclose a K-Nearest Neighbor (KNN) algorithm determines the subset of the two dimensional data closest to the center of each cluster and labeling, by the processor, each cluster of the plurality of clusters based on common features subsequent to the KNN algorithm determining the subset of the two dimensional data closest to the center of each cluster.
Pai et al disclose the determining of the mathematical distances may also involve the use of a learning algorithm, such as, for example, a large margin nearest neighbor (LMNN) algorithm. In some implementations, the resulting mathematical distances may then be used to classify or group the visitor sessions using a k-nearest neighbor (KNN) algorithm (column 2, lines 40-45).
Accordingly, in some implementations, a Euclidean distance within a transformed space that allows dimension-specific weighting (e.g., different weights for different web page categories for a particular feature) may be determined. In implementations described herein, the Mahalanobis metric is used to provide a distance measurement in a large margin nearest neighbor (LMNN) algorithm, which may be employed to improve the accuracy of a group classification system, such as a k-nearest neighbor (KNN) algorithm (column 9, lines 4-24).
In one example, those visitor sessions that resulted in a purchase from the website are labeled as residing in the target group, while those visitor sessions that did not result in a purchase are labeled as belonging to the non-target group. As mentioned above, one particular classification algorithm that may be utilized is the k-nearest neighbor (KNN) algorithm (column 11, lines 8-13)
Shen et al and Pai et al are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a K-Nearest Neighbor (KNN) algorithm determines the subset of the two dimensional data closest to the center of each cluster and labeling, by the processor, each cluster of the plurality of clusters based on common features subsequent to the KNN algorithm determining the subset of the two dimensional data closest to the center of each cluster in Shen et al, as seen Pai et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 16, Shen et al disclose storing the cluster graph in a memory for subsequent retrieval and analysis (i.e., Analysts may also label or name the selected clusters and their corresponding detailed information, and store them in one or more databases, ¶ 0067).
As per claim 17, Shen et al disclose the shading graphically identifies each cluster of the plurality of clusters in the cluster graph (i.e., the 2D visualization 700b shown in FIG. 7B may be provided for visual cluster exploration. As previously described, each rectangular block denotes a clickstream pattern, ¶ 0093).
As per claim 18, Shen et al disclose the shading further comprises assigning a respective color to each cluster (i.e., A box 740 of legends of click actions may be presented to help the user identify to what click action each colored geometric shape corresponds, ¶ 0093).
Claim 19 is rejected based upon the same rationale as the rejection of claim 1, since it is the computer-readable medium claim corresponding to the method claim.
Claim 20 is rejected based upon the same rationale as the rejection of claim 1, since it is the apparatus claim corresponding to the method claim.
Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al (US 20140101580 A1), in view of Pai et al (US 10115121 B2), in further view of Martinec et al (US 20220391445 A1).
As per claims 7 and 8, Shen et al does not disclose the probability matrix is transformed into two-dimensional data based on a dimensionality reduction algorithm and the dimensionality reduction algorithm comprises a Uniform Manifold Approximation and Projection algorithm (UMAP).
Martinec et al disclose The evaluation agent 16 gathers browsing history and clickstreams from a browser 50 with which it is integrated or in communication with, which data is transmitted to the evaluation manager 20 via an evaluation application program interface (“API”) 32 (¶ 0032). A data analyzing method for dimensionality reduction such as t-SNE or Uniform Manifold Approximation and Projection (“UMAP”) is applied to reduce the number of columns of the table to two, without affecting the rows which still correspond to the search results. These two newly created columns can be represented as x and y coordinates. Clusters are formed of coordinates that are close to each other (¶ 0074).
Shen et al and Martinec et al are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the probability matrix is transformed into two-dimensional data based on a dimensionality reduction algorithm and the dimensionality reduction algorithm comprises a Uniform Manifold Approximation and Projection algorithm (UMAP) in Shen et al, as seen in Martinec et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claims 9 and 10, Shen et al does not disclose prior to generating the cluster graph: downsampling, by the processor, the two dimensional data and reducing, by the processor, the downsampled two dimensional data to reduce a density of the two dimensional data before the application of an algorithm to generate the cluster graph.
Martinec et al disclose the evaluation agent 16 gathers browsing history and clickstreams from a browser 50 with which it is integrated or in communication with, which data is transmitted to the evaluation manager 20 via an evaluation application program interface (“API”) 32 (¶ 0032). A data analyzing method for dimensionality reduction such as t-SNE or Uniform Manifold Approximation and Projection (“UMAP”) is applied to reduce the number of columns of the table to two, without affecting the rows which still correspond to the search results. These two newly created columns can be represented as x and y coordinates. Clusters are formed of coordinates that are close to each other (¶ 0074).
Shen et al and Martinec et al are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include prior to generating the cluster graph: downsampling, by the processor, the two dimensional data and reducing, by the processor, the downsampled two dimensional data to reduce a density of the two dimensional data before the application of an algorithm to generate the cluster graph in Shen et al, as seen in Martinec et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al (US 20140101580 A1), in view of Pai et al (US 10115121 B2), in further view of Saunkeah et al (US 20230162241 A1).
As per claim 12, Shen et al does not disclose the clustering algorithm comprises a Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm.
Saunkeah et al disclose through the cookie, the gift advertisement sub-system 104 may collect clickstream data (e.g., data corresponding to webpages the user has accessed) (¶ 0037). A set of data may be analyzed using one of a variety of machine learning algorithms to identify correlations between different elements of the set of data without supervision and feedback (e.g., an unsupervised training technique). A machine learning data analysis algorithm may also be trained using sample or live data to identify potential correlations. Such algorithms may include k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms (¶ 0195).
Shen et al and Saunkeah et al are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the clustering algorithm comprises a Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm in Shen et al, as seen in Saunkeah et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE D BOYCE whose telephone number is (571)272-6726. The examiner can normally be reached M-F 10a-6:30p.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 July 10, 2026
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