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 .
This action is made non-final.
Claims 1-20 are pending. Claims 1, 9 and 17 are independent claims.
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 7 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 7 and 15 each recite the limitation "the line of business". There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. Claim 1 recites: A method for computing data contraction and estimating similarity of data points from heterogeneous data descriptors by utilizing one or more processors along with allocated memory, the method comprising… Claim 1 is directed to a method (Step 1: YES).
Step 2A prong 1: Does the claim recite a judicial exception? Claim 1 recites: computing common features data among first data point and the second data point by comparing the first data point and the second data point and their respective data distributions (computing “common features” data by comparing two points and their distributions is a mathematical calculation or mental process); linking a pre-computed knowledge graph with the first data point and the second data point (linking a knowledge graph with data points is a mental process, i.e., identifying similar or related items) computing, in response to linking, knowledge-comparable features data among the first data point and the second data point based on other features, received as input, that are not common features (computing “knowledge-comparable features” data based on features of data is a mental process or mathematical calculation); computing knowledge-comparable data based on the knowledge-comparable features data and the common features data (computing “knowledge-comparable” data based on other data is a mathematical calculation or mental process); computing similarity of the first data point and the second data point based on the knowledge-comparable data (computing similarity between data points is a mathematical calculation). These steps can be performed mentally or are mathematical calculations (Step 2A prong 1: YES).
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, considered individually and in combination, integrate the judicial exception into a practical application? Claim 1 recites: receiving a first input raw dataset and a second input raw dataset that are usable for computing common features data; generating a first data point from the first input raw dataset and generating a second data point from the second input raw dataset… and generating a data contraction map along with assigned similarity score based on the computed similarity. Receiving datasets and generating points from the datasets are insignificant extra-solution activity of data gathering that does not add a meaningful limitation to the data contraction and similarity estimation method (MPEP 2106.05(g)). Generating a data contraction map along with an assigned similarity score based on the previously computed similarity is insignificant extra-solution activity of data outputting that does not add a meaningful limitation to the data contraction and similarity estimation method (MPEP 2106.05(g)), or is no more than mere instructions to implement the abstract idea which is equivalent to adding the words “apply it” to the recited judicial exception because the claim omits any details as to how the map is generated and only recites the idea of a solution or outcome (MPEP 2106.05(f)) (Step 2A prong 2: NO).
Step 2B: These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since they since they only amount to data gathering or outputting without significantly more (MPEP 2106.05(g)) or provide nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)). These limitations, taken either alone or in combination, fail to provide an inventive concept (Step 2B: NO). Thus, the claim is not patent eligible.
Regarding claims 2-8, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occurs (Claim 2, applying a data distribution sampling algorithm to datasets is a mathematical formula; Claim 3, describing sampled datasets that are smaller than the datasets that they were sampled from is still a mathematical formula; Claim 4, receiving the various input data types is insignificant extra-solution activity of data gathering without significantly more, and retrieving exact same features is a mental process; Claim 5, implementing a transforming algorithm is a mathematical formula; Claim 6, receiving a precomputed knowledge graph that has a tree-like data structure is still extra-solution activity of data gathering without significantly more, and specifying that the structure captures domain knowledge that corresponds to a line of business is specifying a field of use without significantly more; Claim 7, specifying that the line of business includes applications for loan approval is specifying a field of use without significantly more; Claim 8, applying a random sampling algorithm to the datasets is insignificant extra-solution activity of data gathering or selecting information, based on types of information and availability of information (see Electric Power Group, LLC v. Alstom S.A.), mapping data to seed points using a radius determined by an accuracy factor is a mathematical calculation or mental process, selecting seeds in a distance mapping is a mental process, and querying a distance between two points is a mathematical calculation or mental process).
Regarding claim 9, it is a system that implements a method similar to claim 1 and is rejected on the same grounds – see above.
Regarding claims 10-16, they recite similar limitations to claims 2-8 and are rejected on the same grounds – see above.
Regarding claim 17, it is an apparatus that implements a method similar to claim 1 and is rejected on the same grounds – see above.
Regarding claims 18-20, they recite similar limitations to claims 2-4 and are rejected on the same grounds – see above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 9 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abolhasssani et al. (US 20220358336 A1), herein Abolhasssani, in view of Liu et al. (US 20210120206 A1), herein Liu, and Han et al. (US 20190332946 A1), herein Han.
Regarding claim 1, Abolhasssani teaches: A method for computing data contraction and estimating similarity of data points from heterogeneous data descriptors by utilizing one or more processors along with allocated memory (¶16, An AI-based data matching and alignment system that generates similarity mappings for a target data source from a plurality of data sources in a data corpus is disclosed), the method comprising: receiving a first input raw dataset and a second input raw dataset that are usable for computing common features data (¶16, the plurality of data sources from the data corpus are initially filtered to identify candidate data sources that are similar to the target data source); generating a first data point from the first input raw dataset and generating a second data point from the second input raw dataset (¶16, The candidate data sources are further analyzed to identify columns from the candidate data sources that are similar to the columns of the target data source); computing common features data among first data point and the second data point by comparing the first data point and the second data point and their respective data distributions (¶51, The relationships between the two tables can be derived by identifying the… distribution of the characters that make the attributes in these files similar); linking a… knowledge graph with the first data point and the second data point (¶21, The AI-based data matching and alignment system can estimate matching data from different data sources based on the relationships determined through AI techniques described herein. Furthermore, the determined matches and relationships can be used to build the knowledge graph for the data from the plurality of data sources, which in turn can drive more efficient and accurate analytics by downstream applications); computing, in response to linking, knowledge-comparable features data among the first data point and the second data point based on other features, received as input, that are not common features; computing knowledge-comparable data based on the knowledge-comparable features data and the common features data (¶51, The relationships between the two tables can be derived by identifying the patterns… of the characters that make the attributes in these files similar, the semantical and statistical features that are in common among them – knowledge-comparable features data can be interpreted as features that have similarities (i.e., not determined by a comparison of distributions), and the features data can be interpreted as some statistical measure or pattern that is derived from the features) computing similarity of the first data point and the second data point based on the knowledge-comparable data (¶39, In an example, the similarity calculation can use the feature information about the columns in the target data source 190 and the candidate data source(s))…
Abolhasssani fails to teach: a pre-computed graph.
However, in the same field of endeavor, Liu teaches: a pre-computed graph (¶60, the assistant system 140 may pre-compute a plurality of personalized language models for a plurality of possible subjects a user may talk about. When a user requests assistance, the assistant system 140 may then swap these pre-computed language models quickly).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a precomputed model as disclosed by Liu in the method disclosed by Abolhasssani to allow for rapid and efficient configuration (¶60, As a result, the assistant system 140 may have a technical advantage of saving computational resources while efficiently determining what the user may be talking about).
Abolhasssani in view of Liu fails to teach: and generating a data contraction map along with assigned similarity score based on the computed similarity.
However, in the same field of endeavor, Han teaches: and generating a data contraction map (¶67, The input vector is provided to a machine-learned model (e.g., a neural network) which maps it to a vector representing a hypothetical ideal movie) along with assigned similarity score based on the computed similarity (¶82, The similarity scores indicate how well the positive and negative examples match the intent of the user. In one embodiment, the similarity score for an example… is the distance in vector space).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a data contraction map along with similarity score as disclosed by Han in the method disclosed by Abolhasssani in view of Liu to connect related items so they can be accessed quickly and efficiently (¶93, Furthermore, in embodiments where the recommendations are generated using both a machine-learned model and information in a knowledge graph, the likelihood that the virtual assistant 240 will provide a valuable recommendation without requesting clarification or further inquiry is increased further).
Regarding claim 9, it is a system that implements a method similar to claim 1 and is rejected on the same grounds – see above.
Regarding claim 17, it is an apparatus that implements a method similar to claim 1 and is rejected on the same grounds – see above.
Claim(s) 2, 3, 10, 11, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abolhasssani in view of Liu and Han as applied to claims 1, 9 and 17 above, and further in view of Shimazu (US 20210056444 A1).
Regarding claim 2, Abolhasssani in view of Liu and Han fails to teach: The method according to claim 1, further comprising: applying a data distribution sampling algorithm onto each of said first input raw dataset and said second input raw dataset to generate a first sampled dataset and a second sampled dataset, respectively.
However, in the same field of endeavor, Shimazu teaches: applying a data distribution sampling algorithm onto each of said first input raw dataset and said second input raw dataset to generate a first sampled dataset and a second sampled dataset, respectively (¶263, After random sampling (i.e. bootstrap), a reduced training set of data can be generated from each of the randomly sampled raw training sets of data (using e.g. the various methods described above for computing a reduced training set based on a raw training set of data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a data sampling algorithm to create sampled datasets as disclosed by Shimazu in the method disclosed by Abolhasssani in view of Liu and Han to reduce computation (¶38, thereby reducing computation complexity when processing the reduced training set of data by the classification algorithm, compared to processing the training set of data).
Regarding claim 3, Abolhasssani in view of Liu and Han fails to teach: The method of claim 2, wherein a size of the first sampled dataset is smaller than the first received input raw dataset, and wherein a size of the second sampled dataset is smaller than the second received input raw dataset.
However, in the same field of endeavor, Shimazu teaches: wherein a size of the first sampled dataset is smaller than the first received input raw dataset, and wherein a size of the second sampled dataset is smaller than the second received input raw dataset (¶263, After random sampling (i.e. bootstrap), a reduced training set of data can be generated from each of the randomly sampled raw training sets of data (using e.g. the various methods described above for computing a reduced training set based on a raw training set of data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to create reduced sampled datasets as disclosed by Shimazu in the method disclosed by Abolhasssani in view of Liu to reduce computation (¶38, thereby reducing computation complexity when processing the reduced training set of data by the classification algorithm, compared to processing the training set of data).
Regarding claims 10 and 11, they recite limitations similar to claims 2 and 3 respectively and are rejected on the same grounds – see above.
Regarding claims 18 and 19, they recite limitations similar to claims 2 and 3 respectively and are rejected on the same grounds – see above.
Claim(s) 4, 5, 12, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abolhasssani in view of Liu, Han and Shimazu as applied to claims 3, 11 and 19 above, and further in view of Kiljanek (US 20200118691 A1).
Regarding claim 4, Abolhasssani further teaches: the method according to claim 3, wherein in computing the common features, the method further comprising: receiving as input data the following data: the first data point generated from the first input raw dataset, the second data point generated from the second input raw dataset, the first sampled data set, and the second sampled dataset; retrieving exact same features among the first data point and the second data point (¶33, Lastly, if the matching columns are of numeric type, the explanations can be generated by showing that the distance between the distributions of the two columns is minimum as compared to other non-matching, numeric columns – and – ¶34, Distribution distance between NPD_FACILITY_CODE and NPD_FACILITY_CODE_2 is 0.0 – i.e., exact same features between data points, in the case of Abolhasssani columns are data points extracted from datasets).
Abolhasssani in view of Liu, Han and Shimazu fails to teach: and retrieving exact same features among the first sampled data set and the second sampled dataset.
However, in the same field of endeavor, Kiljanek teaches: and retrieving exact same features among the first sampled data set and the second sampled dataset (¶60, For example, the “age” feature may have “ages” or “years” or “how old” or “how old are you?” as possible aliases. If aliases of features across different simulated patient population datasets match, these features may be normalized by renaming one or both feature names so that the features appear consistently named).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify matching features across datasets as disclosed by Kiljanek in the method disclosed by Abolhasssani in view of Liu, Han and Shimazu to enable efficient comparisons between different datasets (¶60, allowing simulated patient datasets that were originally from different simulated patient population datasets to be easily compared).
Regarding claim 5, Abolhasssani further teaches: The method according to claim 4, wherein in computing knowledge-comparable data, the method further comprising: implementing a corresponding transforming algorithm to transform corresponding received input data with respect to the common features and knowledge-comparable features sets (¶33, Lastly, if the matching columns are of numeric type, the explanations can be generated by showing that the distance between the distributions of the two columns is minimum as compared to other non-matching, numeric columns. The Kolmogorov-Smimov test may be used for determining the distance between the columns. For example, NPD_FACILITY and NPD_FACILITY_CODE_2 may be matched from Tables 1 and 2 respectively – and – ¶34, Distribution distance between NPD_FACILITY_CODE and NPD_FACILITY_CODE_2 is 0.0 – performing statistical tests on data can be interpreted as implementing a transforming algorithm).
Regarding claims 12 and 13, they recite limitations similar to claims 4 and 5 respectively and are rejected on the same grounds – see above.
Regarding claim 20, it recites limitations similar to claim 4 and is rejected on the same grounds – see above.
Claim(s) 6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abolhasssani in view of Liu and Han as applied to claims 1 and 9 above, and further in view of Budzik (US 20210158085 A1).
Regarding claim 6, Abolhasssani further teaches: The method according to claim 1, wherein the precomputed knowledge graph is a tree-like data structure (fig. 3 demonstrates a tree-like data structure that has nodes and connections between nodes like a tree).
Abolhasssani in view of Liu and Han fails to teach: that captures domain knowledge corresponding to a line of business.
However, in the same field of endeavor, Budzik teaches: that captures domain knowledge corresponding to a line of business (¶15, the model purpose data is generated by domain experts (e.g., data scientists, business analysts, and the like) having specific domain knowledge related to the identified purpose).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include domain knowledge associated with a line of business as disclosed by Budzik in the method disclosed by Abolhasssani in view of Liu and Han to create models that can function without continued expert input (¶15, can be used to automatically generate models for “auto loan origination” purposes without further input from a data scientist).
Regarding claim 14, it recites similar limitations to claim 6 and is rejected on the same grounds – see above.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abolhasssani in view of Liu, Han as applied to claims 5 and 13 above, and further in view of Budzik.
Regarding claim 7, Abolhasssani in view of Liu, Han, Shimazu and Kiljanek fails to teach: The method according to claim 5, wherein the line of business includes applications for loan approval.
However, in the same field of endeavor, Budzik teaches: wherein the line of business includes applications for loan approval (¶16, In some variations, the model purpose relates to consumer loan origination, and results of the model are used to determine whether to grant a consumer loan. In some variations, the model purpose relates to business loan origination, and results of the model are used to determine whether to grant a loan to a business. In other variations, the model purpose relates to loan repayment prediction, and results of the model are used to determine whether a loan already granted will be repaid).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include domain knowledge associated with applications for loan approval as disclosed by Budzik in the method disclosed by Abolhasssani in view of Liu, Han, Shimazu and Kiljanek to create a loan processing model that can function without continued expert input (¶15, can be used to automatically generate models for “auto loan origination” purposes without further input from a data scientist).
Regarding claim 15, it recites similar limitations to claim 7 and is rejected on the same grounds – see above.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abolhasssani in view of Liu and Han as applied to claims 1 and 9 above, and further in view of Lewis et al. (US 20160019282 A1), herein Lewis, Nakamura et al. (US 20180217812 A1), herein Nakamura, and Pitalúa García et al. (US 20210021414 A1), herein Pitalúa García.
Regarding claim 8, Abolhasssani further teaches: The method according to claim 1, wherein in computing the similarity of the first data point and the second data point, the method further comprising… implementing an inter-dataset mapping algorithm that selects, for every pair of input raw datasets, a unique pair of seeds in the distance mapping (¶40, For example, a target column labeled Sensor 1 has another column labeled Sensor 3 identified as being similar with a match score of 91% -– the columns of the target data source may be interpreted as seeds, with the candidate data sources being the input raw datasets – and – ¶43, a tree-based similarity is calculated for the columns of the target data source 190 and the candidate data source using the corresponding feature matrices… it is determined if more candidate data sources remain to be processed. If yes, the method returns to block 258 to select the next candidate data source else the method moves on to block 270 to provide the output – i.e., this process repeats if a pair of data sources is provided); and querying, in response to selecting, a distance between the first data point and the second data point (Fig. 5, datasets Well_0_1/Log1.csv and Well_0_1/Log2.csv are selected, while their individual data points are compared with respect to similarity – the similarity can be a distance metric as described in ¶39, In an example, the column similarity calculator 134 can implement tree-based similarity techniques such as but not limited to, a random forest distance (RFD) metric for the column similarity calculation – also: ¶44, the distance between the nodes or the length of the edges in the knowledge graph 172 may signify the extent of column similarity so that similar columns are represented by closer nodes while columns of lower similarity are represented farther apart by edges with greater length).
Abolhasssani in view of Liu and Han fails to teach: applying an independent and identically distributed sampling algorithm to each of said first input raw dataset and said second input raw dataset to construct seed points.
However, in the same field of endeavor, Lewis teaches: applying an independent and identically distributed sampling algorithm to each of said first input raw dataset and said second input raw dataset to construct seed points (¶85, the Training Set is expanded (often iteratively). This is accomplished by selecting training records using one or more of the following methods: simple random sample – random sampling leads to an independent and identically distributed result).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use an independent and identically distributed sampling algorithm (i.e., random sample) as disclosed by Lewis in the method disclosed by Abolhasssani in view of Liu, Shimazu and Han to create representative data points (¶116, Randomness is used simply as a way to get representative records).
Abolhasssani in view of Liu, Han and Lewis fails to teach: implementing an intra-dataset mapping algorithm that maps an expanded set to a seed point among the constructed seed points using a radius of an accuracy factor, wherein the accuracy factor is a parameter controlling an approximation error of a distance mapping value…
However, in the same field of endeavor, Nakamura teaches: implementing an intra-dataset mapping algorithm that maps an expanded set to a seed point among the constructed seed points using a radius of an accuracy factor, wherein the accuracy factor is a parameter controlling an approximation error of a distance mapping value (¶193, The time-series data search device 100 can generate the sample segment as a representative of a set of the training segments included in the sphere exactly having the approximation error ε as the radius – and – Figs. 14 and 15, which show mapping various centroids (including an initial, (i.e., seed) centroid, shown in Fig. 14 and labeled “C” in Fig. 15) where the radius is taken into account, further described in ¶381, Each of (1) to (6) in FIG. 15 represents the centroid and the distance is investigated for the centroids (1), (2), and (3) in this order, which have average values closer to the average value of C in this order. Thereafter, the centroids up to (4) having differences in the average values relative to C within ε/2 are treated as candidates of objects to be merged as the sample segment).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a radius determined by an accuracy factor that represents approximation error of a distance mapping as disclosed by Nakamura in the method disclosed by Abolhasssani in view of Liu, Han and Lewis to limit the error to a specific value (¶193, With this, the approximation error ε can be ensured in the similarity search).
Abolhasssani in view of Liu, Han, Lewis and Nakamura fails to teach: in an interval (0,1).
However, in the same field of endeavor, Pitalúa García teaches: in an interval (0,1) (¶96, small allowed error rate γ∈(0,1)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use an accuracy factor in the interval (0,1) as disclosed by Pitalúa García in the method disclosed by Abolhasssani in view of Liu, Han, Lewis and Nakamura to represent all possible values of a rate, which can theoretically be 0%-100% or (0,1) (¶96, error rate γ∈(0,1)).
Regarding claim 16, it recites similar limitations to claim 8 and is rejected on the same grounds – see above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Martineau et al. (US 20190392330 A1), which discusses clustering implicit and explicit features between different data sets, and Tacchi et al. (US 20170228435 A1), which discusses measuring distribution of terms to determine similarities between texts.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HARRISON CHAN YOUNG KIM whose telephone number is (571)272-0713. The examiner can normally be reached Monday -Thursday 10:00 am - 7:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CESAR PAULA can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HARRISON C KIM/ Examiner, Art Unit 2145
/CESAR B PAULA/ Supervisory Patent Examiner, Art Unit 2145