DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement filed on 6/12/2025 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because NPL reference #2 is not in English and no concise explanation of relevance or English translated copy was provided. It has been placed in the application file, but the information referred to therein concerning this reference has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Drawings
The drawings are objected to because of the following informalities.
Fig. 6 is illegible. Clear drawings are required for accurate scanning and reproduction on office documents.
Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. 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.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 and 13-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Exemplary claim 1 was amended to includes subject matter which was not described in the specification such as a plurality of prediction scores respectively corresponding to a plurality of GUI elements that are each associated with a different service interaction entry path. While Applicant pointed to two sections in paragraph 44 of the specification (but actually cited paragraphs 43 and 49), those sections do not support the specificity of the claim language. For this reason, the above listed claims are rejected for containing this language or being dependent on a claim that contains this language.
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.
Claim(s) 1, 2, 5, 7, 13-15, 17-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song et al. (hereinafter Song), Random generalized linear model: a highly accurate and interpretable ensemble predictor, in view of Saegusa, LARGE SAMPLE THEORY FOR MERGED DATA FROM MULTIPLE SOURCES, further in view of Zdravevski et al. (hereinafter Zdravevski), Robust histogram-based feature engineering of time series data, Ranft et al. (hereinafter Ranft), U.S. Patent Application Publication 2016/0232546.
Regarding Claim 1, Song discloses a computer-implemented method, comprising:
comprising one or more user features and a user response [“prediction methods based on multiple features” pg. 1, col. 1, ¶1; “Prediction methods (also known as classifiers, supervised machine learning methods, regression models, prognosticators, diagnostics)” pg. 1, col. 1, ¶1; Note: Supervised learning includes labeled data which reads on the user response],
training, …, one or more machine learning models [“generates and integrates multiple versions of a single predictor (often referred to as base learner)” pg. 1, col. 2, ¶1] corresponding to the one or more user features [“prediction methods based on multiple features” pg. 1, col. 1], wherein each of the one or more machine learning models learns a relationship between a corresponding user feature and the plurality of user responses [“An individual predictor (e.g. a tree predictor) is fitted on each bootstrapped data set” pg. 1, col. 2, ¶1]; and
using the one or more machine learning models [“prediction methods based on multiple features” pg. 1, col. 1, ¶1; “Prediction methods (also known as classifiers, supervised machine learning methods, regression models, prognosticators, diagnostics)” pg. 1, col. 1, ¶1].
However, Song fails to explicitly disclose obtaining, by sampling without replacement for a plurality of times, a plurality of training datasets from a plurality of historical data records, each of the plurality of historical data records … wherein the plurality of training datasets comprise a first training dataset and a second training dataset with one or more overlapped historical data records;
… respectively corresponding to the plurality of training datasets, … reuses one or more data points corresponding to the one or more overlapped historical data records.
Saegusa discloses obtaining, by sampling without replacement for a plurality of times [“sampling without replacement.” Abstract], a plurality of training datasets from a plurality of historical data records [“merged data from multiple sources” Abstract], each of the plurality of historical data records [“merged data from multiple sources” Abstract] … wherein the plurality of training datasets comprise a first training dataset and a second training dataset with one or more overlapped historical data records [“multiple datasets from overlapping data sources” Abstract];
… respectively corresponding to the plurality of training datasets [“merged data from multiple sources” Abstract], … reuses one or more data points corresponding to the one or more overlapped historical data records [“multiple datasets from overlapping data sources” Abstract].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song and Saegusa before him before the effective filing date of the claimed invention, to modify the method of Song to incorporate the data acquisition approach of Saegusa.
Given the advantage of scientific and financial benefits of data integration, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Song fails to explicitly disclose generating a plurality of histograms … wherein a histogram of the second training dataset … in a histogram of the first training dataset;
based on the plurality of histograms.
Zdravevski discloses generating a plurality of histograms … wherein a histogram of the second training dataset … in a histogram of the first training dataset;
based on the plurality of histograms [“a histogram-based method for feature engineering for time series data” §1 ¶5; “The next step is to calculate the histogram for the time series. If we use B bins, then the histogram for a particular training instance will have B values. By doing this, from a time series with N values we obtain a histogram of B values, where N > B. The B values represent the transformed features which are robust, but are also a significantly reduced representation of the original time series.” §III.C ¶7].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, and Zdravevski before him before the effective filing date of the claimed invention, to modify the combination to incorporate data processing using histograms of Zdravevski.
Given the advantage of using robust features with reduced representation, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, while Song’s focus is on creating and training the system rather than implementation, Song fails to explicitly disclose to different graphical user interface (GUI) elements initiating a service interaction;
processing a feature vector of a given user using the one or more machine learning models to generate a plurality of prediction scores respectively corresponding to a plurality of GUI elements that are each associated with a different service interaction entry path; and
displaying, on a user device associated with the given user, a selected GUI element of the plurality of GUI elements based on the plurality of prediction scores, wherein the selected GUI element is configured to initiate the service interaction entry path predicted to result in higher user engagement relative to alternative GUI elements.
Ranft discloses to different graphical user interface (GUI) elements initiating a service interaction [Fig. 19, 21];
processing a feature vector of a given user [“GUI 400 includes a section labeled my profile 402, which includes an about me section 404, a personal information section 406, a how I like to do business section 408, a what I have now section 410, a my banking section 412, a my credit cards section 414, a my mortgages section 416, a my home insurance section 418, and a my auto insurance section 420” ¶78] using the one or more machine learning models to generate a plurality of prediction scores respectively corresponding to a plurality of GUI elements that are each associated with a different service interaction entry path [“GUI 560 displays product names and details 577 and proprietary score information” ¶93; Fig. 17, 19]; and
displaying, on a user device associated with the given user, a selected GUI element of the plurality of GUI elements based on the plurality of prediction scores, wherein the selected GUI element is configured to initiate the service interaction entry path predicted to result in higher user engagement relative to alternative GUI elements [“Using the values for the various variables (as specified by the values entered into the fields of variable portions 518,520,524,526 and528), the recommendation system calculates a propriety score for each of products 516a-516n. GUI 500 includes proprietary score section 522 for display of a calculated proprietary score, for each of products 516a-516n.” ¶89; “Via control 638, consumers could choose to receive a pricing quote from a financial provider for the product. Via control 640, a consumer selects to apply for the product.” ¶96; Figs. 17-21].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, and Ranft before him before the effective filing date of the claimed invention, to modify the combination to incorporate the GUI aspects and implementation of Ranft.
Given the advantage of predicting a service interaction to increase desired outcomes, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 2, Song, Saegusa, Zdravevski, and Ranft disclose the method of claim 1. Song further discloses further comprising:
ensembling the one or more machine learning models into a generalized linear model for predicting user responses based on the one or more user features [“RGLMis an ensemble predictor based on bootstrap aggregation (bagging) of generalized linear models” §Methods ¶1].
Regarding Claim 5, Song, Saegusa, Zdravevski, and Ranft disclose the method of claim 1.
However, Song fails to explicitly disclose wherein the generating a plurality of histograms respectively corresponding to the plurality of training datasets comprises:
identifying one or more first historical data records that are in the first training dataset but not in the second training dataset, and one or more second historical data records that are in the second training dataset but not in the first training dataset; and
by removing one or more data points corresponding to the one or more first historical data records and adding one or more data points corresponding to the one or more second historical data records.
Saegusa discloses wherein the generating a plurality of histograms respectively corresponding to the plurality of training datasets comprises:
identifying one or more first historical data records that are in the first training dataset but not in the second training dataset, and one or more second historical data records that are in the second training dataset but not in the first training dataset [Fig. 1; Note: this figure shows a Ven diagram depicting one or more first historical data records that are in the first training dataset but not in the second training dataset, and one or more second historical data records that are in the second training dataset but not in the first training dataset]; and
by removing one or more data points corresponding to the one or more first historical data records and adding one or more data points corresponding to the one or more second historical data records [Fig. 1; Note: different sampling of the overlapped parts of the Ven diagram will result in removal and addition of data points in the first and second historical data records].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, and Ranft before him before the effective filing date of the claimed invention, to modify the combination to incorporate the data acquisition approach of Saegusa.
Given the advantage of scientific and financial benefits of data integration, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Song fails to explicitly disclose generating a first histogram based on the first training dataset;
generating a second histogram based on the first histogram.
Zdravevski discloses generating a first histogram based on the first training dataset [“a histogram-based method for feature engineering for time series data” §1 ¶5; “The next step is to calculate the histogram for the time series. If we use B bins, then the histogram for a particular training instance will have B values. By doing this, from a time series with N values we obtain a histogram of B values, where N > B. The B values represent the transformed features which are robust, but are also a significantly reduced representation of the original time series.” §III.C ¶7];
generating a second histogram based on the first histogram [“a histogram-based method for feature engineering for time series data” §1 ¶5; “The next step is to calculate the histogram for the time series. If we use B bins, then the histogram for a particular training instance will have B values. By doing this, from a time series with N values we obtain a histogram of B values, where N > B. The B values represent the transformed features which are robust, but are also a significantly reduced representation of the original time series.” §III.C ¶7].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, and Ranft before him before the effective filing date of the claimed invention, to modify the combination to incorporate data processing using histograms of Zdravevski.
Given the advantage of using robust features with reduced representation, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 7, Song, Saegusa, Zdravevski, and Ranft disclose the method of claim 1. Song further discloses wherein the one or more machine learning models comprise one or more regression models or one or more classification models [“GLMs comprise a large class of regression models” pg. 2, col. 2, last paragraph].
Claim 13 is rejected on the same grounds as Claim 1.
Claim 14 is rejected on the same grounds as Claim 2.
Claim 15 is rejected on the same grounds as Claim 5.
Claim 17 is rejected on the same grounds as Claim 1.
Claim 18 is rejected on the same grounds as Claim 5.
Claim 20 is rejected on the same grounds as Claim 2.
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song, Saegusa, Zdravevski, and Ranft, further in view of Morimura et al. (hereinafter Morimura), U.S. Patent Application Publication 2016/0180251.
Regarding Claim 3, Song, Saegusa, Zdravevski, and Ranft disclose the method of claim 1.
However, Song fails to explicitly disclose wherein obtaining the plurality of training datasets from the plurality of historical data records by sampling without replacement for a plurality of times comprises:
sampling the first training dataset without replacement from the plurality of randomly arranged historical data records;
sampling the second training dataset without replacement from the plurality of randomly rearranged historical data records.
Saegusa discloses wherein obtaining the plurality of training datasets from the plurality of historical data records by sampling without replacement for a plurality of times comprises:
sampling the first training dataset without replacement from the plurality of randomly arranged historical data records [“sampling without replacement.” Abstract];
sampling the second training dataset without replacement from the plurality of randomly rearranged historical data records [“sampling without replacement.” Abstract].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, and Ranft before him before the effective filing date of the claimed invention, to modify the combination to incorporate the data acquisition approach of Saegusa.
Given the advantage of scientific and financial benefits of data integration, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Song fails to explicitly disclose randomly arranging the plurality of historical data records;
randomly rearranging the plurality of historical data records.
Morimura discloses randomly arranging the plurality of historical data records [“The learning processing unit 150 may use, as the samples for learning, data set obtained by shuffling the hundred input and output sample vectors in total at random.” ¶102];
randomly rearranging the plurality of historical data records [“The learning processing unit 150 may use, as the samples for learning, data set obtained by shuffling the hundred input and output sample vectors in total at random.” ¶102].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, Ranft, and Morimura before him before the effective filing date of the claimed invention, to modify the combination to incorporate the data shuffling of Morimura.
Given the advantage of increasing the size of the usable training data, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 4, Song, Saegusa, Zdravevski, Ranft, and Morimura disclose the method of claim 3. Song further discloses and each comprises more than half of the plurality of historical data records [“2/3 of the observations(arrays) were randomly chosen as the training set, while the remaining samples were chosen as test set.” pg. 1, col. 1, lines 3-5].
However, Song fails to explicitly disclose wherein the first training dataset and the second training dataset are equal in size.
Morimura discloses wherein the first training dataset and the second training dataset are equal in size [“learning processing unit 150 learns the selection model 10 using a hundred input and output sample vectors in total shown in Expression (5) as samples for learning” ¶102].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, Ranft, and Morimura before him before the effective filing date of the claimed invention, to modify the combination to incorporate the data size of Morimura.
Given the advantage of increasing the size of the usable training data, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song, Saegusa, Zdravevski, and Ranft, further in view of Zhao et al. (hereinafter Zhao), Comparison of decision tree methods for finding active objects.
Regarding Claim 6, Song, Saegusa, Zdravevski, and Ranft disclose the method of claim 1. Song further discloses aggregating the plurality of single-feature shallow trees into a single-feature machine learning model corresponding to the user feature [“Random forest (RF) RF is an ensemble predictor that consists of a collection of decision trees which vote for the class of observations” pg. 5 col. 2, ¶3].
However, Song fails to explicitly disclose based on the plurality of histograms.
Zdravevski discloses based on the plurality of histograms [“a histogram-based method for feature engineering for time series data” §1 ¶5; “The next step is to calculate the histogram for the time series. If we use B bins, then the histogram for a particular training instance will have B values. By doing this, from a time series with N values we obtain a histogram of B values, where N > B. The B values represent the transformed features which are robust, but are also a significantly reduced representation of the original time series.” §III.C ¶7].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, and Ranft before him before the effective filing date of the claimed invention, to modify the combination to incorporate data processing using histograms of Zdravevski.
Given the advantage of using robust features with reduced representation, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Song fails to explicitly disclose wherein the training one or more machine learning models corresponding to one or more user features based on the plurality of histograms comprises:
for each of the one or more user features, constructing a plurality of single-feature shallow trees.
Zhao discloses wherein the training one or more machine learning models corresponding to one or more user features based on the plurality of histograms comprises:
for each of the one or more user features, constructing a plurality of single-feature shallow trees [“A Decision stump is basically a one-level decision tree where the split at the root level is based on a specific attribute/value pair.” §2.4]; and
aggregating the plurality of single-feature shallow trees into a single-feature machine learning model corresponding to the user feature [“Random forest (Breiman, 2001) is an ensemble of unpruned classification or regression trees” §2.5].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, Ranft, and Zhao before him before the effective filing date of the claimed invention, to modify the combination to incorporate the decision stump and forests of Zhao.
Given the advantage of simplicity, speed, and interpretability, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim(s) 8-10, 16, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song, Saegusa, Zdravevski, and Ranft, further in view of Erciyes, Guide to Graph Algorithms.
Regarding Claim 8, Song, Saegusa, Zdravevski, and Ranft disclose the method of claim 1.
However, Song fails to explicitly disclose further comprising: ordering the plurality of training datasets for minimizing a computational cost for generating the plurality of histograms.
Erciyes discloses further comprising: ordering the plurality of training datasets for minimizing a computational cost for generating the plurality of histograms [“Clustering is the process of grouping of similar objects based on some metric.” §13.5.3 ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, Ranft, and Erciyes before him before the effective filing date of the claimed invention, to modify the combination to incorporate the ordering of similar objects of Erciyes.
Given the advantage of grouping similar objects for faster creation of the histogram, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 9, Song, Saegusa, Zdravevski, Ranft, and Erciyes disclose the method of claim 8.
However, Song fails to explicitly disclose related to a number of historical data records belonging to either of the two training datasets, but not in their intersection.
Saegusa discloses related to a number of historical data records belonging to either of the two training datasets, but not in their intersection [Fig. 1; Note: Ven diagram includes sections belonging to either dataset but not their intersection].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, Ranft before him before the effective filing date of the claimed invention, to modify the combination to incorporate the data acquisition approach of Saegusa.
Given the advantage of scientific and financial benefits of data integration, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Song fails to explicitly disclose wherein the ordering the plurality of training datasets comprises:
constructing a fully connected graph comprising a plurality of nodes corresponding to the plurality of training datasets and a plurality of edges, wherein each of the plurality of edges connects two training datasets and is associated with a weight;
determining a minimum spanning tree of the fully connected graph, wherein the minimum spanning tree comprises a subset of the plurality of edges connecting the plurality of nodes with a minimum total edge weight; and
ordering the plurality of training datasets based on the minimum spanning tree.
Erciyes discloses wherein the ordering the plurality of training datasets comprises:
constructing a fully connected graph comprising a plurality of nodes corresponding to the plurality of training datasets and a plurality of edges [“A graph can have weights associated with its edges or its vertices” §7.1 ¶1; “there are (n − 1)! possible routes in a fully connected graph with n vertices” §3.8.4 ¶2; “A minimum spanning tree (MST) of a weighted, undirected, and connected graph” §8.1 ¶2], wherein each of the plurality of edges connects two training datasets and is associated with a weight [“A graph can have weights associated with its edges or its vertices” §7.1 ¶1; “weighted graphs” §7.2; “the spanning tree with the minimum total cost of edges among all spanning trees of that graph” §7.1 ¶2];
determining a minimum spanning tree of the fully connected graph, wherein the minimum spanning tree comprises a subset of the plurality of edges connecting the plurality of nodes with a minimum total edge weight [“Minimum Spanning Trees” §7.2; “we are searching for the MST T ⊆ G such that w(T ) given below is minimized” §7.2.1; “the spanning tree with the minimum total cost of edges among all spanning trees of that graph” §7.1 ¶2]; and
ordering the plurality of training datasets based on the minimum spanning tree [“The cut property is useful in forming an MST of a graph. Any least weight edge across any cut of the graph can be included in the MST until we have n−1 edges which means the formed tree is an MST.” §7.2.1 ¶5; “MSTs are also used for clustering of large networks consisting of tens of thousands of nodes and hundreds of thousands of edges such as biological networks. Removing a number of heaviest weight edges results in clusters in such networks.” §7.1 ¶2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, Ranft, and Erciyes before him before the effective filing date of the claimed invention, to modify the combination to incorporate the MST of Erciyes.
Given the advantage of efficient and accurately clustering large graphs, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 10, Song, Saegusa, Zdravevski, Ranft, and Erciyes disclose the method of claim 9.
However, Song fails to explicitly disclose wherein the ordering the plurality of training datasets based on the minimum spanning tree comprises:
selecting a node from the minimum spanning tree as a starting point;
performing a breadth-first search (BFS) to determine a processing sequence of the plurality of nodes in the minimum spanning tree; and
ordering the plurality of training datasets based on the processing sequence of the plurality of nodes in the minimum spanning tree.
Erciyes discloses wherein the ordering the plurality of training datasets based on the minimum spanning tree comprises:
selecting a node from the minimum spanning tree as a starting point [“Starting from the source vertex s” §6.4 ¶1];
performing a breadth-first search (BFS) to determine a processing sequence of the plurality of nodes in the minimum spanning tree [“Breath-First Search” §6.4; “Starting from the source vertex s, we first visit all neighbors of s in an arbitrary order. These neighbor vertices, N(s), all have a distance of unity from the vertex s after the visit. We then visit neighbors of vertices in N(s) which are labeled with distance of 2 to vertex s. This process continues until all vertices are visited.” §6.4]; and
ordering the plurality of training datasets based on the processing sequence of the plurality of nodes in the minimum spanning tree [“A directed spanning tree of the graph G is obtained as a result of this algorithm” §6.4.1 ¶4].
It would have been obvious to one having ordinary skill in the art, having the teachings of Song, Saegusa, Zdravevski, Ranft, and Erciyes before him before the effective filing date of the claimed invention, to modify the combination to incorporate the BFS of Erciyes.
Given the advantage of using a well-known graph traversal method in order to traverse all vertices or all edges of a graph in some order, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 16 is rejected on the same grounds as Claim 9.
Claim 19 is rejected on the same grounds as Claim 9.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Response to Arguments
Regarding the withdrawal of the 101 rejections, these rejections were withdrawn due to the amendments. However, it is noted that the amendments have been rejected as having a lack of written description under 35 USC 112(a).
Regarding the prior art rejections, for Applicant’s first argument, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that Zdravevski does not teach or suggest reusing data points from one histogram in constructing another histogram corresponding to a different training dataset that overlaps with the first. Examiner disagrees for at least the following reasons.
The argued language is not in the claim. Specifically, the claim recites, generating a plurality of histograms respectively corresponding to the plurality of training datasets, wherein a histogram of the second training dataset reuses one or more data points corresponding to the one or more overlapped historical data records in a histogram of the first training dataset. It is not claimed that the “reusing data points from one histogram in constructing another histogram.” If a first histogram is made from the first training dataset, and a second histogram is made from the second training dataset, and as previously claimed, wherein the plurality of training datasets
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comprise a first training dataset and a second training dataset with one or more overlapped historical data records, then the two histograms reuse data that’s already in the other since there’s overlapping (i.e., duplicate) data from the training datasets.
Additionally, this limitation is not solely rejected by Zdravevski. It is further rejected by Saegusa. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
For Applicant’s second through fourth arguments, Applicant's arguments with respect to the claims have been considered but are moot because the arguments do not apply to the combination of references being used in the current rejection of the limitation(s).
Accordingly, the rejections are maintained.
Conclusion
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 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 ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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, Michelle T. Bechtold can be reached at (571) 431-0762. 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.
/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
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