3Notice 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 .
DETAILED ACTION
1. This Office Action is in response to the application filed on 05/22/2024.
Claims 1-20 are pending.
Priority
2. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
3. The information disclosure statement (IDS) filed on 05/22/2024 complies with the provisions of M.P.E.P. 609. The examiner has considered it.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
At Step 1:
Independent claims 1, 12 and 20 are directed to a "method", a “device” and a “program product” and thus directed to a statutory category
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
• " acquire a reference data set …." as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 1 and paragraphs 82 and 195 where one can mentally visualize obtaining a reference data set.
• " determining a target template …" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 1 and paragraph 83 where one can mentally determine a target template based on various reference attributes.
• " generating a plurality of sample information …" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 1 and paragraph 84 where one can mentally visualize generating various sample information based on the target template.
• " generating a sample data set …" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 1 and paragraph 85 where one can mentally visualize generating a sample data set based on sample information.
At Step 2A, Prong Two:
• The claim recites no additional elements. At most one might consider that a "at least one processor … memory bus …" as claimed might be considered to represent a computer-implemented system and method consistent with Fig. 1 even though the claim does not recite any computer. At most this would be a high-level recitation of a generic computer components and represents mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
• Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
• The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
• Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Dependent Claims 2-11 and 13-19
The limitations as recited in dependent claims 2 and 13 recite, “analyzing the at least one information sample … generating a target template…”, which further describes the concepts performed in the human mind including an observation, evaluation, judgment, in step 2A prong one.
Claims 3 and 14 recite, “determining a search interval … determining a keyword list …”, which further describes the concepts performed in the human mind including an observation, evaluation, judgment, in step 2A prong one.
Claims 4 and 15 recite, “acquiring a target word of the target semantic… generating a compound word … constructing a misspelled word … generating the preset word library …”,which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claims 5 and 16 recite, “sorting the plurality of information templates… perform merging … to generate a target template …”, which further describes the concepts performed in the human mind including an observation, evaluation, judgment, in step 2A prong one.
Claims 6 and 17 recite, “determining a gap length between two adjacent information template… perform merging … to generate a target template …”, which further describes the concepts performed in the human mind including an observation, evaluation, judgment, in step 2A prong one.
Claims 7 and 18 recite, “generating replacement information… perform replacement operation …”, which further describes the concepts performed in the human mind including an observation, evaluation, judgment, in step 2A prong one.
Claims 8 and 19 recite, “constructing a regular expression … acquiring the replacement word … determining a replacement character…”,which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 9 recites, “determining an insertion scheme … generating the sample data set…”,which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 10 recites, “determining a sample quality… inserting the sample quality… determining the updated second reference data …”, which further describes the concepts performed in the human mind including an observation, evaluation, judgment, in step 2A prong one.
Claim 11 recites, “to train an information detection model…”,which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Examiner’s Note
6. A reference data set (According to Google): “A reference data set (RDS) is a collection of standardized data used to classify, categorize, or contextualize other information within an organization's databases and applications. These sets act as a "controlled vocabulary"—such as state codes, currency codes, or units of measurement—that are static and rarely change.”
He et al, US 20210342348, [He: Abstract and paragraphs 4-5 (“acquiring the query input by a user; constructing a syntactic dependency tree of the query; matching the syntactic dependency tree of the query with syntactic dependency trees of preset templates, and determining a target template according to the matching result; and marking a slot operator of a slot in the query using the target template, and the marked slot operator represents a logical relationship applied to the slot in the query.”)] [He: Paragraph 34 (“a plurality of templates are preset, and each template includes a template name, template confidence and the syntactic dependency tree of the template. The template name corresponds to a processing method after the query is parsed with the template, and for example, the template with the template name “[P:negate]” indicates that the processing method after the query is parsed is a negating operation; since corresponding to the same template name, a plurality of templates with the same name may be ranked according to the template confidence; the syntactic dependency tree of the template contains n non-Root nodes, and defines nodes included in the template, the position of each node, the position of a parent node of each node, the dependency relationship between each node and the parent node, the part of speech of each node, word contents of each node, an operator corresponding to each node, or the like”)] [He: Paragraph 26 (“it is also possible to construct a classification model, the syntactic dependency tree of the query and the syntactic dependency trees of the preset templates are input into the classification model, and the target template is determined according to an output result of the classification model”)].
Cella et al, US 20210358032, [Cella: Paragraphs 158 and 202 (“the distribution and access devices available to one or more parties to a particular transaction; jurisdictional limitations on the storage, type, and communication of certain types of information; requirements or desired aspects of security and verification of information communication for the service; the response time of information gathering, inter-party communications, and determinations to be made by algorithms, machine learning components, and/or artificial intelligence components of the service;”)].
Claim Rejections - 35 USC § 102
7. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
8. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
9. Claims 1-2, 11-13 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Todd et al (11,550,834).
Claim 1:
Todd suggests a method for sample generation, comprising: acquiring a reference data set [Todd: Abstract and column 1, lines 32-49 (“the data set valuation engine may generate the valuation measure for a given data set as a function of valuation measures previously generated for respective other data sets determined to exhibit at least a threshold similarity to the given data set”, i.e., “previously generated for respective other data sets” = ‘reference data set’)], wherein the reference data set comprises first reference data and second reference data, the first reference data comprises at least one information sample value, the at least one information sample value belongs to a target information type [Todd: Column 4, lines 19-23, column 7, lines 4-12, and column 9, lines 40-56 (“The valuation propagator 110 is illustratively configured for propagation of value based on newer data, which may include by way of example newly ingested or published data sets. Newer data can additionally or alternatively comprise newly-discovered legacy data, as well as other types of data”, i.e., “types of data” = ‘first reference data’ and other data or other metadata = ‘second reference data’)], the target information type is a preset information type with a security requirement, and the second reference data does not comprise an information sample value of the target information type [Todd: Column 7, lines 33-45 and column 9, lines 40-56 (“This information illustratively includes metadata on data sets, content in data sets, explicit and implicit relationships among data sets, and rich descriptions of these relationships. The metadata can illustratively include information such as lineage, provenance, security level, ownership, and history (e.g., cleaned).”)]. Todd suggests determining a target template corresponding to the first reference data based on the at least one information sample value in the first reference data, wherein the target template is used to characterize an information structure of the at least one information sample value in the first reference data [Todd: Column 5, lines 24-44 (“The suitability template illustratively characterizes suitability for at least one of a particular purpose, a particular goal and a particular role in an analytic process or other type of process. The suitability template is associated with at least one target data set. The data set indexer 122 may be configured to generate similarity indexes for a plurality of target data sets each associated with one or more suitability templates. The suitability template in some embodiments is characterized at least in part by valuation measures of respective data sets.”)]. Todd suggests generating a plurality of sample information based on the target template corresponding to the first reference data [Todd: Column 11, lines 14-50 (“The suitability template in some embodiments is created from user-defined rules reflecting the purpose, goal or analytic role of required data sets. This may include, for example, identification of one or more appropriate training data sets for building analytic models. The suitability template can illustratively be viewed as a relativistic representation of one or more data sets. Such a suitability template can be utilized in conjunction with scoring and ranking of data sets, but can also be applied to historical targets”, i.e., data belonging to the template = ‘sample information’)] [Todd: Column 14, lines 35-40 (“target data set associated with the suitability template to one or more other target data sets associated with one or more other suitability templates”)]. Todd suggests generating a sample data set based on the plurality of sample information and the second reference data [Todd: Column 11, lines 33-50, and column 18, lines 35-45 (“The suitability template in some embodiments is created from user-defined rules reflecting the purpose, goal or analytic role of required data sets. This may include, for example, identification of one or more appropriate training data sets for building analytic models. The suitability template can illustratively be viewed as a relativistic representation of one or more data sets. Such a suitability template can be utilized in conjunction with scoring and ranking of data sets, but can also be applied to historical targets”, i.e., data used to train for building analytic models = ‘sample data set’)].
Claim 2:
Todd suggests wherein the determining a target template corresponding to the first reference data based on the at least one information sample value in the first reference data
comprises: analyzing the at least one information sample value in the first reference data to determine a keyword list corresponding to the at least one information sample value [Todd: Column 5, lines 24-34 (“The data set indexer 122 is configured to generate similarity indexes for a plurality of data sets, and the relativistic data set retriever 124 is configured to obtain a suitability template for a query and to execute the query against one or more of the similarity indexes based at least in part on the suitability template. A given one of the similarity indexes in this embodiment is assumed to comprise at least first and second auxiliary information generated from respective ones of at least first and second different similarity measures of a plurality of different similarity measures supported by the data set discovery engine”, i.e., “first and second auxiliary information” = ‘keyword list’)] [Todd: Column 11, lines 21-65 (“bag of words”)] [Todd: Column 12, lines 4-25, and column 13, lines 14-32 (“words from a document relative to a set of terms or words from a query”)]. Todd suggests according to index information of the at least one information sample value in the first reference data, and index information of each keyword in the keyword list corresponding to the at least one information sample value in the first reference data, determining an information template of the at least one information sample value [Todd: Column 5, lines 24-34 (“The data set indexer 122 is configured to generate similarity indexes for a plurality of data sets, and the relativistic data set retriever 124 is configured to obtain a suitability template for a query and to execute the query against one or more of the similarity indexes based at least in part on the suitability template. A given one of the similarity indexes in this embodiment is assumed to comprise at least first and second auxiliary information generated from respective ones of at least first and second different similarity measures of a plurality of different similarity measures supported by the data set discovery engine”)]. Todd suggests generating a target template corresponding to the first reference data based on the information template of the at least one information sample value [Todd: Column 5, lines 24-44 (“The suitability template illustratively characterizes suitability for at least one of a particular purpose, a particular goal and a particular role in an analytic process or other type of process. The suitability template is associated with at least one target data set. The data set indexer 122 may be configured to generate similarity indexes for a plurality of target data sets each associated with one or more suitability templates. The suitability template in some embodiments is characterized at least in part by valuation measures of respective data sets”, i.e., templates are generated from other templates)].
Claim 11:
Todd suggests wherein the sample data set generated according to the method is used to train an information detection model, the information detection model is used to detect information content comprised in data to be detected to obtain a detection result corresponding
to the data to be detected, and when the detection result indicates that the data to be detected comprises target information belonging to a target information type, prompt information is generated [Todd: Column 4, lines 19-23, column 7, lines 4-12, and column 9, lines 40-56 (“The valuation propagator 110 is illustratively configured for propagation of value based on newer data, which may include by way of example newly ingested or published data sets. Newer data can additionally or alternatively comprise newly-discovered legacy data, as well as other types of data”, i.e., “types of data” = ‘first reference data’ and other data or other metadata = ‘second reference data’)] [Todd: Column 4, lines 50-67, and column 14, lines 62-67 through column 15, lines 1-5 (“at least portions of the above-noted semantic hierarchy utilized by the data set discovery engine 104 to identify similar data sets are adjusted over time through the use of machine learning techniques. Such machine learning techniques illustratively utilize feedback derived at least in part from user interaction with particular data sets and their valuation measures. By way of example, machine learning or related approaches can be used to analyze dynamic changes in value of a given data set over all or part of the lifecycle to date of that data set. This can involve tagging data sets with their current position in the lifecycle or adjusting their values directly based on lifecycle position or history. Machine learning can therefore be used to improve valuation techniques and associated weights given some representation of usage outcomes. These and other machine learning arrangements can involve generating revised values and/or usage statistics as well as tagging of data set values.”)].
Claim 12:
Claim 12 is essentially the same as claim 1 except that it sets forth the claimed invention as a device rather than a method and rejected under the same reasons as applied above.
Claim 13:
Claim 13 is essentially the same as claim 2 except that it sets forth the claimed invention as a device rather than a method and rejected under the same reasons as applied above.
Claim 20:
Claim 20 is essentially the same as claim 1 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Allowable Subject Matter
10. Claims 3-10 and 14-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Hung Le
06/09/2026
/HUNG D LE/Primary Examiner, Art Unit 2161
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