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 .
In response to the application filed on March 26, 2026, claims 1-6, 8-20 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 03/26/2026. In this action claim(s) 1-2, 4-6, 8-12, 14-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Proux (US Pub. No. 20190050500) and GONNET (US Pub. No. 20170220665) in further view of Conrad et al. (US Pub. No. 20170235820). The Proux reference has been added to address the amendment of generating a plurality of new clusters of encoded electronic data files in the searchable database based on the commonalities by associating the commonalities with each of the one or more updated plurality of new clusters of encoded electronic data files.
Applicant’s arguments:
In regards to claim 1 on Page(s) 14, applicant argues “The Office Action, at Step 2A Prong One, the Office Action alleges that the claims recite mental processes. Applicant respectfully disagrees. The claims, at least as amended, do not recite a mental process because the claimed steps cannot practically be performed in the human mind. As recited in claim 1, the method operates on "non- human-readable data" contained in "encoded electronic data files" that are received "over an electronic network." By definition, non-human-readable data cannot be processed mentally by a human. The claims further recite "determining.. a similarity of each pair of encoded electronic data files by generating a common word vector for each of the plurality of encoded electronic data files." Generating common word vectors for similarity analysis across a plurality of encoded electronic data files is not practically performable in the human mind. Additionally, claim 1 recites "creating a data structure for each electronic data file cluster, the data structure including a vector for each encoded electronic data file in the cluster" and "extracting, from the data structure for each electronic data file cluster and using one or more machine learning topic modeling techniques, one or more commonalities." Creating data structures containing vectors and extracting information from those data structures using machine learning techniques require computational processing that cannot be performed mentally.”
Examiner’s Reply:
Determining, extracting, creating, and generating are steps included as activities classified as a mental process that are performed using a computer as a tool. Steps like receiving is an additional element.
Applicant’s arguments:
In regards to claim 1 on Page(s) 15, applicant argues “Even assuming, arguendo, that the claims recite an abstract idea, the claims are integrated into a practical application under Step 2A Prong Two. Amended claim 1 recites a specific technical process, for example, receiving encoded electronic data files over an electronic network, preprocessing non-human-readable data, generating common word vectors, determining clusters based on similarity thresholds, creating a data structure for each cluster including a vector for each file, extracting commonalities from that data structure, updating a searchable database, and generating a plurality of new clusters by associating the commonalities. This combination of elements produces a concrete technical result, an improved searchable database which automatically generates clusters organized by extracted commonalities.”
Examiner’s Reply:
The examiner notes that the computer as recited in the claims are being used for identifying commonalities in documents (the computer is being used as a generic tool). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application. Identifying commonalities and themes of documents using cluster creation and analysis does not improve the functioning of a computing system.
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 1-6, 8-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1-6, 8-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim method (claims 1-6, 8-11), system(s) (claims 12-16), and machine-readable medium 18-20 is/are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes & Mathematical Concepts enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claims 1 recites the following limitations directed towards a Mental Processes & Mathematical Concepts:
preprocessing the non-human-readable data for each encoded electronic data file among the plurality of encoded electronic data files for processing in a format processable by a similarity operation (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by preprocessing non-human-readable data);
Determining, in response to the preprocessing and by the similarity operation
by generating a common word vector for each of the plurality of encoded electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a vector);
determining one or more electronic data file clusters among the plurality of encoded electronic data files based on the determined similarity of each pair of encoded electronic data files among the plurality of encoded electronic data files exceeding a threshold value (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a cluster); and
extracting, from the data structure for each electronic data file cluster and using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by extracting commonalities);
updating a searchable database to include the one or more commonalities (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating a database);
extracting, using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by extracting commonalities)
generating a plurality of new clusters of encoded electronic data files in the searchable database based on the commonalities by associating the commonalities with each of the plurality of new clusters of encoded electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a cluster).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1 and 11:
receiving a plurality of electronic data files including non-human-readable data (recites insignificant extra solution activity of receiving a data file).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes & Mathematical Concepts enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 12 and 17 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claims 12 and 17 recites the following limitations directed towards a Mental Processes & Mathematical Concepts:
preprocessing the non-human-readable data for each encoded electronic data file among the plurality of encoded electronic data files for processing in a format processable by a similarity operation (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by preprocessing non-human-readable data);
Determining, in response to the preprocessing and by the similarity operation, a similarity of each pair of encoded electronic data files among the plurality of encoded electronic data files file (The limitation recites a mathematical concept of calculating similarities)
by generating a common word vector for each of the plurality of electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a vector);
determining one or more electronic data file clusters among the plurality of encoded electronic data files based on the determined similarity of each pair of encoded electronic data files among the plurality of encoded electronic data files exceeding a threshold value (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a cluster); and
extracting, from the data structure for each electronic data file cluster, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by extracting commonalities);
updating a searchable database to include the one or more commonalities (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating a database);
generating a plurality of new clusters of encoded electronic data files in the searchable database based on the commonalities by associating the commonalities with each of the a plurality of new clusters of encoded electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a cluster).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 12 and 17:
at least one data storage device storing instructions for electronic data cluster analysis in an electronic storage medium (i.e., as a generic processor/component performing a generic computer function); and
at least one processor (i.e., as a generic processor/component performing a generic computer function) configured to execute the instructions to perform operations including:
receiving, over an electronic network, a plurality of encoded electronic data files including non-human-readable data (recites insignificant extra solution activity of receiving a data file).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 12, and 17 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2:
Step 2A, prong one of the 2019 PEG:
reporting the extracted one or more commonalities (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by reporting commonalities).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3:
Step 2A, prong one of the 2019 PEG:
preprocessing the non-human-readable data for each encoded electronic data file among the plurality of electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by preprocessing non-human-readable data),
wherein preprocessing the non-human-readable data for each encoded electronic data file comprises:
performing one or more of:
lemmatizing document text for each document (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by lemmatizing text),
stemming the document text for each document (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by stemming text),
tokenizing the document text for each document (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by tokenizing text),
removing stop words from the document text for each document (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by removing stop words); and
constructing a list of common strings from the non-human-readable data for each encoded electronic data file (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by constructing a list).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4 and 14:
Step 2A, prong one of the 2019 PEG:
determining instances of each word of the common word vector appearing in the non- human-readable data for each electronic data file (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining word instances); and
determining the similarity of each pair of encoded electronic data files based on the counted determined instances (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a similarity).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5:
Step 2A, prong one of the 2019 PEG:
wherein the similarity of each pair of encoded electronic data files is determined as a positive number (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a similarity).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6, 15, and 19:
Step 2A, prong one of the 2019 PEG:
searching thedetermined similarities of each pair of encoded electronic data file for a maximum similarity between pairs of encoded electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by searching for a max similarity);
averaging the similarities of the pair of encoded electronic data files having the maximum similarity into a new electronic data file cluster (The limitation recites a mathematical concept of averaging similarities);
remove individual electronic data files of the pair of encoded electronic data files having the maximum similarity from the plurality of encoded electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind removing files);
adding the new electronic data file cluster to the plurality of encoded electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind adding files); and
determining a similarity of the new cluster and each encoded electronic data file among the plurality of encoded electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a similarity).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8, 16, and 20:
Step 2A, prong one of the 2019 PEG:
determining instances of each string appearing in the non-human-readable data for each encoded electronic data file and each cluster (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining instances); and
determining the similarity of each encoded electronic data file and each cluster based on the determined instances of each string (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a similarity).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 9:
Step 2A, prong one of the 2019 PEG:
wherein a number of extracted commonalities in each electronic data file cluster is a user-specified maximum number of commonalities (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by extracting commonalities).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 10:
Step 2A, prong one of the 2019 PEG:
wherein a number of determined clusters is a user-specified maximum number of clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind specifying a maximum).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 11:
Step 2A, prong one of the 2019 PEG:
identifying one or more outlier encoded electronic data files among the plurality of encoded electronic data files among the plurality of electronic data files and one or more commonalities in the one or more outlier encoded electronic data files (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind identifying an outlier).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 13:
Step 2A, prong one of the 2019 PEG:
constructing a list of common strings from the non-human-readable data for each encoded electronic data file (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by constructing a list).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 18:
Step 2A, prong one of the 2019 PEG:
constructing a list of common words from the non-human-readable data for each encoded electronic data file (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by constructing a list).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
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) 1-2, 4-6, 8-12, 14-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Proux (US Pub. No. 20190050500) and GONNET (US Pub. No. 20170220665) in further view of Conrad et al. (US Pub. No. 20170235820).
With respect to claim 1, Proux teaches a computer-implemented method for electronic data cluster analysis, the method comprising:
receiving, over an electronic network, a plurality of encoded electronic data files including non-human-readable data (Paragraph 44 discloses a method for generating a condensed dictionary 36 for use in encoding documents);
preprocessing the non-human-readable data for each encoded electronic data file among the plurality of encoded electronic data files for processing in a format processable by a similarity operation (Paragraphs 58 & 59 discloses the test documents 40, 42 are encoded using the condensed dictionary 36, which was generated at S112, by the encoding component 58, to generate document encodings 46. At S126, optionally, similarity is computed between pairs of test documents 40, 42, based on their encodings 46, by the similarity computation component 60);
determining, in response to the preprocessing and by the similarity operation, a similarity of each pair of encoded electronic data files among the plurality of encoded electronic data files by generating a common word vector for each of the plurality of encoded electronic data files (Paragraph 104 discloses computing similarity between vectors and Paragraph 84 discloses multi-dimensional vectors derived from identified block types);
determining one or more electronic data file clusters among the plurality of encoded electronic data files based on the determined similarity of each pair of encoded electronic data files among the plurality of encoded electronic data files exceeding a threshold value (Paragraph 109 discloses identifies document pairs whose similarity score exceeds the threshold similarity score T2. If the first document D.sub.i is not yet in a cluster, a new cluster C.sub.k is created to which D.sub.i is added);
creating a data structure for each electronic data file cluster, the data structure including a vector for each encoded electronic data file in the one or more electronic data file clusters (Paragraph 87 discloses Once every document has been processed according to Algorithm 1, create a new empty dictionary E.sub.2 which is a compacted version of E.sub.1 with the following data structure);
extracting, from the data structure for each electronic data file cluster and using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters (Paragraph 30 discloses the block extractor 50 and/or content extractor 54);
generating a plurality of new clusters of encoded electronic data files in the searchable database based on the commonalities by associating the commonalities with each of the one or more updated plurality of new clusters of encoded electronic data files (Paragraph 109 discloses identifies document pairs whose similarity score exceeds the threshold similarity score T2. If the first document D.sub.i is not yet in a cluster, a new cluster C.sub.k is created to which D.sub.i is added).
Proux does not explicitly disclose extracting, using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters.
However, Gonnet teaches extracting, from the data structure for each electronic data file cluster and using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters (Paragraph 122 discloses common text elements such as dates, phone numbers, SSNs, etc. The method may be utilized in various contexts and applications, such as, (1) for example, the classification of documents, and (2) the classification of columns in a relational database and Paragraph 82 discloses machine-learning and/or machine-based feedback in relation to classification).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Proux with Gonnet. This would have facilitated clustering and theme identification. See Gonnet Paragraph(s) 3-30.
Proux as modified by Gonnet does not disclose updating a searchable database to include the one or more commonalities.
However, Conrad et al. teaches updating a searchable database to include the one or more commonalities (Paragraph 73 discloses extraction process 124 and populates database tables with document data and metadata tags—tags and other metadata may be the result of this or an other pre-clustering process, for example Calais tagging. In this manner, documents may be more rapidly processed based on metadata and/or tags rather than the content as a whole).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Proux and Gonnet with Conrad et al. This would have facilitated clustering and theme identification. See Conrad et al. Paragraph(s) 3-29.
The Proux reference as modified by Gonnet and Conrad et al. teaches all the limitations of claim 1. With respect to claim 2, Gonnet teaches the computer-implemented method of claim 1, further comprising:
reporting the extracted one or more commonalities (Paragraph 122 discloses common text elements such as dates, phone numbers, SSNs, etc. The method may be utilized in various contexts and applications, such as, (1) for example, the classification of documents, and (2) the classification of columns in a relational database). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Proux reference and the Gonnet reference is applicable to dependent claim 2.
The Proux reference as modified by Gonnet and Conrad et al. teaches all the limitations of claim 1. With respect to claim 4, Gonnet teaches the computer-implemented method of claim 1, wherein calculating a similarity of each pair of electronic data files comprises:
counting instances of each string of the common strings appearing in the non- human-readable data for each electronic data file (Paragraph 121 discloses a library of ‘standard’ counts for common text elements such as dates, phone numbers, social security numbers (SSNs), etc); and
calculating the similarity of each pair of electronic data files based on the counted instances (Paragraph 111 discloses A statistical measure may then applied be to determine the similarity of sets of counts associated with each pair of texts). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Proux reference and the Gonnet reference is applicable to dependent claim 4.
The Proux reference as modified by Gonnet and Conrad et al. teaches all the limitations of claim 1. With respect to claim 5, Gonnet teaches the computer-implemented method of claim 4, wherein the similarity of each pair of electronic data files is calculated as a positive number (Paragraph 111 discloses A statistical measure may then applied be to determine the similarity of sets of counts associated with each pair of texts). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Proux reference and the Gonnet reference is applicable to dependent claim 5.
The Proux reference as modified by Gonnet and Conrad et al. teaches all the limitations of claim 1. With respect to claim 6, Gonnet teaches the computer-implemented method of claim 1, wherein determining one or more electronic data file clusters comprises iteratively performing until a desired number of electronic data file clusters is determined:
searching the calculated similarities of each pair of electronic data file for a maximum similarity between pairs of electronic data files (Paragraph 48 discloses the maximum likelihood G test may be used for computation of the distance. This distance may be utilized in performing classifications to form groupings 104 (e.g., clusters) based on identified similarities between aspects of information and Paragraph 50 discloses a level of similarity may be determined through the application of a threshold or a pre-defined threshold, above which documents may be deemed similar, and such similarities may be utilized for the purpose of searching, integrating, transformation, etc);
averaging the similarities of the pair of electronic data files having the maximum similarity into a new electronic data file cluster (Paragraph 48 discloses the maximum likelihood G test may be used for computation of the distance. This distance may be utilized in performing classifications to form groupings 104 (e.g., clusters) based on identified similarities between aspects of information and Paragraph 50 discloses a level of similarity may be determined through the application of a threshold or a pre-defined threshold, above which documents may be deemed similar, and such similarities may be utilized for the purpose of searching, integrating, transformation, etc);
remove individual electronic data files of the pair of electronic data files having the maximum similarity from the plurality of electronic data files (Paragraph 48 discloses the maximum likelihood G test may be used for computation of the distance. This distance may be utilized in performing classifications to form groupings 104 (e.g., clusters) based on identified similarities between aspects of information and Paragraph 50 discloses a level of similarity may be determined through the application of a threshold or a pre-defined threshold, above which documents may be deemed similar, and such similarities may be utilized for the purpose of searching, integrating, transformation, etc);
adding the new electronic data file cluster to the plurality of electronic data files (Paragraph 48 discloses the maximum likelihood G test may be used for computation of the distance. This distance may be utilized in performing classifications to form groupings 104 (e.g., clusters) based on identified similarities between aspects of information and Paragraph 50 discloses a level of similarity may be determined through the application of a threshold or a pre-defined threshold, above which documents may be deemed similar, and such similarities may be utilized for the purpose of searching, integrating, transformation, etc); and
calculating a similarity of the new cluster and each electronic data file among the plurality of electronic data files (Paragraph 48 discloses the maximum likelihood G test may be used for computation of the distance. This distance may be utilized in performing classifications to form groupings 104 (e.g., clusters) based on identified similarities between aspects of information and Paragraph 50 discloses a level of similarity may be determined through the application of a threshold or a pre-defined threshold, above which documents may be deemed similar, and such similarities may be utilized for the purpose of searching, integrating, transformation, etc). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Proux reference and the Gonnet reference is applicable to dependent claim 6.
The Proux reference as modified by Gonnet and Conrad et al. teaches all the limitations of claim 1. With respect to claim 8, Gonnet teaches the computer-implemented method of claim1, wherein extracting one or more commonalities in each electronic data file cluster further comprises:
counting instances of each string appearing in the non-human-readable data for each electronic data file and each cluster (Paragraph 121 discloses a library of ‘standard’ counts for common text elements such as dates, phone numbers, social security numbers (SSNs), etc); and
calculating the similarity of each electronic data file and each cluster based on the counted instances of each string (Paragraph 111 discloses A statistical measure may then applied be to determine the similarity of sets of counts associated with each pair of texts). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Proux reference and the Gonnet reference is applicable to dependent claim 8.
The Proux reference as modified by Gonnet and Conrad et al. teaches all the limitations of claim 1. With respect to claim 9, Gonnet teaches the computer-implemented method of claim 1, wherein a number of extracted commonalities in each electronic data file cluster is a user-specified maximum number of commonalities (Paragraph 121 discloses a library of ‘standard’ counts for common text elements such as dates, phone numbers, social security numbers (SSNs), etc. is provided for comparison and/or reference template purposes). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Proux reference and the Gonnet reference is applicable to dependent claim 9.
The Proux reference as modified by Gonnet and Conrad et al. teaches all the limitations of claim 1. With respect to claim 10, Gonnet teaches the computer-implemented method of claim 1, wherein a number of determined clusters is a user-specified maximum number of clusters (Paragraph 72 discloses the value of n can be varied, and different/parallel clustering approaches may be used to identify differences that arise if n=2, n=3, n=4, and so forth. For example, a user may select which value of n led to the most accurate clustering, as different values may have differing outcomes depending on the source data). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Proux reference and the Gonnet reference is applicable to dependent claim 10.
The Proux reference as modified by Gonnet and Conrad et al. teaches all the limitations of claim 1. With respect to claim 11, Gonnet teaches the computer-implemented method of claim 1, further comprising:
identifying one or more outlier encoded electronic data files among the plurality of encoded electronic data files among the plurality of encoded electronic data files and one or more commonalities in the one or more outlier electronic data files (Paragraph 47 discloses statistical tests, for example chi-square, can measure how different the count vectors are or how different the columns are, for example and Paragraph 53 discloses iterative statistical analyses). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Proux reference and the Gonnet reference is applicable to dependent claim 11.
With respect to claim 12, Proux teaches a system for electronic data cluster analysis, the system comprising:
at least one data storage device storing instructions for electronic data cluster analysis in an electronic storage medium (See Fig. 1); and
at least one processor (See Fig. 1) configured to execute the instructions to perform operations including:
receiving, over an electronic network, a plurality of encoded electronic data files including non-human-readable data (Paragraph 44 discloses a method for generating a condensed dictionary 36 for use in encoding documents);
preprocessing the non-human-readable data for each encoded electronic data file among the plurality of encoded electronic data files for processing in a format processable by a similarity operation (Paragraphs 58 & 59 discloses the test documents 40, 42 are encoded using the condensed dictionary 36, which was generated at S112, by the encoding component 58, to generate document encodings 46. At S126, optionally, similarity is computed between pairs of test documents 40, 42, based on their encodings 46, by the similarity computation component 60);
determining, in response to the preprocessing and by the similarity operation, a similarity of each pair of encoded electronic data files among the plurality of encoded electronic data files by generating a common word vector for each of the plurality of encoded electronic data files (Paragraph 104 discloses computing similarity between vectors and Paragraph 84 discloses multi-dimensional vectors derived from identified block types);
determining one or more electronic data file clusters among the plurality of encoded electronic data files based on the determined similarity of each pair of encoded electronic data files among the plurality of encoded electronic data files exceeding a threshold value (Paragraph 109 discloses identifies document pairs whose similarity score exceeds the threshold similarity score T2. If the first document D.sub.i is not yet in a cluster, a new cluster C.sub.k is created to which D.sub.i is added);
creating a data structure for each electronic data file cluster, the data structure including a vector for each encoded electronic data file in the one or more electronic data file clusters (Paragraph 87 discloses Once every document has been processed according to Algorithm 1, create a new empty dictionary E.sub.2 which is a compacted version of E.sub.1 with the following data structure);
extracting, from the data structure for each electronic data file cluster and using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters (Paragraph 30 discloses the block extractor 50 and/or content extractor 54);
generating a plurality of new clusters of encoded electronic data files in the searchable database based on the commonalities by associating the commonalities with each of the one or more updated plurality of new clusters of encoded electronic data files (Paragraph 109 discloses identifies document pairs whose similarity score exceeds the threshold similarity score T2. If the first document D.sub.i is not yet in a cluster, a new cluster C.sub.k is created to which D.sub.i is added).
Proux does not explicitly disclose extracting, using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters.
However, Gonnet teaches extracting, from the data structure for each electronic data file cluster and using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters (Paragraph 122 discloses common text elements such as dates, phone numbers, SSNs, etc. The method may be utilized in various contexts and applications, such as, (1) for example, the classification of documents, and (2) the classification of columns in a relational database and Paragraph 82 discloses machine-learning and/or machine-based feedback in relation to classification).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Proux with Gonnet. This would have facilitated clustering and theme identification. See Gonnet Paragraph(s) 3-30.
Proux as modified by Gonnet does not disclose updating a searchable database to include the one or more commonalities.
However, Conrad et al. teaches updating a searchable database to include the one or more commonalities (Paragraph 73 discloses extraction process 124 and populates database tables with document data and metadata tags—tags and other metadata may be the result of this or an other pre-clustering process, for example Calais tagging. In this manner, documents may be more rapidly processed based on metadata and/or tags rather than the content as a whole).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Proux and Gonnet with Conrad et al. This would have facilitated clustering and theme identification. See Conrad et al. Paragraph(s) 3-29.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 4, because claim 14 is substantially equivalent to claim 4.
With respect to claim 15, it is rejected on grounds corresponding to above rejected claim 6, because claim 15 is substantially equivalent to claim 6.
With respect to claim 16, it is rejected on grounds corresponding to above rejected claim 8, because claim 16 is substantially equivalent to claim 8.
As to claim 17, GONNET teaches a non-transitory machine-readable medium storing instructions that, when executed by a computing system, causes the computing system to perform operations for electronic data cluster analysis, the operations comprising:
receiving, over an electronic network, a plurality of encoded electronic data files including non-human-readable data (Paragraph 44 discloses a method for generating a condensed dictionary 36 for use in encoding documents);
preprocessing the non-human-readable data for each encoded electronic data file among the plurality of encoded electronic data files for processing in a format processable by a similarity operation (Paragraphs 58 & 59 discloses the test documents 40, 42 are encoded using the condensed dictionary 36, which was generated at S112, by the encoding component 58, to generate document encodings 46. At S126, optionally, similarity is computed between pairs of test documents 40, 42, based on their encodings 46, by the similarity computation component 60);
determining, in response to the preprocessing and by the similarity operation, a similarity of each pair of encoded electronic data files among the plurality of encoded electronic data files by generating a common word vector for each of the plurality of encoded electronic data files (Paragraph 104 discloses computing similarity between vectors and Paragraph 84 discloses multi-dimensional vectors derived from identified block types);
determining one or more electronic data file clusters among the plurality of encoded electronic data files based on the determined similarity of each pair of encoded electronic data files among the plurality of encoded electronic data files exceeding a threshold value (Paragraph 109 discloses identifies document pairs whose similarity score exceeds the threshold similarity score T2. If the first document D.sub.i is not yet in a cluster, a new cluster C.sub.k is created to which D.sub.i is added);
creating a data structure for each electronic data file cluster, the data structure including a vector for each encoded electronic data file in the one or more electronic data file clusters (Paragraph 87 discloses Once every document has been processed according to Algorithm 1, create a new empty dictionary E.sub.2 which is a compacted version of E.sub.1 with the following data structure);
extracting, from the data structure for each electronic data file cluster and using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters (Paragraph 30 discloses the block extractor 50 and/or content extractor 54);
generating a plurality of new clusters of encoded electronic data files in the searchable database based on the commonalities by associating the commonalities with each of the one or more updated plurality of new clusters of encoded electronic data files (Paragraph 109 discloses identifies document pairs whose similarity score exceeds the threshold similarity score T2. If the first document D.sub.i is not yet in a cluster, a new cluster C.sub.k is created to which D.sub.i is added).
Proux does not explicitly disclose extracting, using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters.
However, Gonnet teaches extracting, from the data structure for each electronic data file cluster and using one or more machine learning topic modeling techniques, one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters (Paragraph 122 discloses common text elements such as dates, phone numbers, SSNs, etc. The method may be utilized in various contexts and applications, such as, (1) for example, the classification of documents, and (2) the classification of columns in a relational database and Paragraph 82 discloses machine-learning and/or machine-based feedback in relation to classification).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Proux with Gonnet. This would have facilitated clustering and theme identification. See Gonnet Paragraph(s) 3-30.
Proux as modified by Gonnet does not disclose updating a searchable database to include the one or more commonalities.
However, Conrad et al. teaches updating a searchable database to include the one or more commonalities (Paragraph 73 discloses extraction process 124 and populates database tables with document data and metadata tags—tags and other metadata may be the result of this or an other pre-clustering process, for example Calais tagging. In this manner, documents may be more rapidly processed based on metadata and/or tags rather than the content as a whole).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Proux and Gonnet with Conrad et al. This would have facilitated clustering and theme identification. See Conrad et al. Paragraph(s) 3-29.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 6, because claim 19 is substantially equivalent to claim 6.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 4, because claim 20 is substantially equivalent to claim 4.
Claim(s) 3, 13, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Proux (US Pub. No. 20190050500) and GONNET (US Pub. No. 20170220665) and Conrad et al. (US Pub. No. 20170235820) in further view of Guggilla et al. (US Pub. No. 20190065991).
The Proux reference as modified by Gonnet and Conrad et al. teaches all the limitations of claim 1. With respect to claim 3, Gonnet teaches the computer-implemented method of claim 1, further comprising:
preprocessing the non-human-readable data for each electronic data file among the plurality of electronic data files (Paragraph 4 disclose automated clustering engine that performs an approximate classification using extracted encoded information that is processed into count vectors and populated into a topology of a tree using determinations based on the count vectors), wherein preprocessing the non-human-readable data for each electronic data file comprises:
constructing a list of common strings from the non-human-readable data for each encoded electronic data file (Paragraph 47 discloses extracted information is transformed into a compressed string representative of possibilities of n-grams, among other methods). Gonnet et al. as modified by Conrad et al. does not disclose lemmatizing document text for each document.
However, Guggilla et al. teaches lemmatizing document text for each document, stemming the document text for each document, tokenizing the document text for each document, removing stop words from the document text for each document (Paragraph 35 discloses phrase classification may include tokenization of sentences to unigram tokens, stemming techniques such as lemmatizer, term document matrices, Naïve Bayes classifier and the like and Paragraph 36 discloses stop words).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Proux and Gonnet and Conrad et al. with Guggilla et al. This would have facilitated clustering and theme identification. See Conrad et al. Paragraph(s) 1.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 3, because claim 13 is substantially equivalent to claim 3.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 3, because claim 18 is substantially equivalent to claim 3.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-Pub. No. 20110225159 is directed Computer-based Method For Organizing Data For Search, Accessing Domain Corpus, Where Domain Corpus Is Parsed Into Multiple Documents And Each Document Is Parsed Into One Term That Corresponds To Document: [0057] FIG. 2 displays a natural language query 29 applied to the reformed term-to-document matrix 23. The natural language query 29 maps into the reduced dimensional space of the reformed term-to-document matrix 23 and retrieves relevant documents 31. kPOOL may correlate the relevant documents 31 to the hierarchy of clustered documents 11, to retrieve information regarding the topics the relevant documents 31 fall into. A fisheye view 33 displays the topics that correlate to the relevant documents 31. The fisheye view 33 displays short phrases from the relevant documents 31 and contains hyperlinks linking the short phrases directly to the domain corpus 13.
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.
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/N.E.A/Examiner, Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154
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