Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/9/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: in claims 1, 3-6, and 8-10 recite “parameter definition unit”, “missing data prediction unit”, “extraction unit”, “imputation priority flagging unit”, “priority order determination unit” and Claims 2-3 and 7-8 recite ” display unit”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. ( see Fig. 1)
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-5 are directed to a device ( i.e., machine) and Claims 6-10 are directed to a method (i.e., a process), Therefore, Claims 1-10 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claim 1 substantially recites a parameter definition step to define an imputation amount adjustment parameter used for adjustment of a proportion requiring imputation in pieces of data of specific column data items in a data table that includes a plurality of column data items defined in a column direction and a plurality of entries defined in a row direction and each including respective pieces of data of the plurality of column data items, and that contains missing data of each of the specific column data items in some of the entries; a missing data prediction step to predict the missing data of each of the specific column data items on a basis of data of each of the column data items other than the specific column data items and on a basis of data of each of the specific column data items in the plurality of entries constituting the data table; an extraction step to extract at least one of the entries falling within a predetermined proportion defined by the imputation amount adjustment parameter in the respective pieces of data of the specific column data items; an imputation priority flagging step to give an imputation priority flag to the at least one entry falling within the predetermined proportion; and a priority order determination step to count the number of the entries each given the imputation priority flag in the entries included in the data table, and to determine an integrated imputation priority order in a descending order of the number of the imputation priority flags. Claim 6 recites similar limitations.
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers mental process ( e.g., data manipulation and sorting) and mathematical concepts . Therefore, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1 and 6 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent),
The independent claims recite the additional elements: (i) missing data imputation device, a parameter definition unit, missing data prediction unit, an extraction unit, imputation priority flagging unit, priority order determination unit, which are recited at a high-level of generality (See specification [0012] FIG. 1 is a system configuration diagram illustrating a configuration example of a missing data imputation device 100 according to a first embodiment. The missing data imputation device 100 is a computer, for example, and includes a data table 10, a parameter definition unit 20, a missing data prediction unit 30, an extraction unit 40, an imputation priority flagging unit 50, and a priority order determination unit 60. The missing data imputation device 100 preferably includes a display unit 70 as well) such that, when viewed as whole/ordered combination (as shown in Fig. 1) , it amounts to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (as shown in Fig. 1) , do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent).
The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it”, which do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Therefore, the additional elements of: (i) missing data imputation device, a parameter definition unit, missing data prediction unit, an extraction unit, imputation priority flagging unit, priority order determination unit, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims are ineligible.
Dependent Claims Step 2A:
The limitations of the dependent claims but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented ( i.e., they merely narrow the abstract idea without adding any new additional elements beyond it). Additionally, for the same reasons as above, the limitations fail to integrate the abstract idea into a practical application because they use the same general technological environment and instructions to implement the abstract idea (e.g., using computers and internet) as the independent claims.
Claims 2-3 and 7-8 recite “display unit”, which fail to integrate the abstract idea into a practical application because merely invoking the generic components as a tool to perform the abstract idea or “apply it”.
Claims 5 and 10 recite “ model” and “ machine learning” which fail to integrate the abstract idea into a practical application because merely invoking the generic components as a tool to perform the abstract idea or “apply it”.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment and instructions to implement a narrowed abstract idea. They do not add any additional elements not already analyzed and the abstract idea has the same ineligible relationship when viewed in combination as the independent claims do.
Claims 2-3 and 7-8 recite “display unit”, that are recited at a high-level of generality (See [0012] The missing data imputation device 100 preferably includes a display unit 70 as well. Note that the display unit 70 may be formed either integrally with or separately from the missing data imputation device 100)- these do not amount to significantly more for the same reasons they fail to integrate the abstract idea into a practical application.
Claims 5 and 10 recite “ model” and “ machine learning” recited at a high-level of generality (See [0016] The missing data prediction unit 30 predicts the missing data of each of the specific column data items through machine learning by using this model ) these do not amount to significantly more for the same reasons they fail to integrate the abstract idea into a practical application. Accordingly, the dependent claims are not eligible subject matter under § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over OKI (US20200302324A1) in view of Resnick (US20200234151A1)
As per claim 1, Oki teaches:
A missing data imputation device ( see at least: Fig.1, #1data complementing apparatus[0036])
a data table that includes a plurality of column data items defined in a column direction and a plurality of entries defined in a row direction and each including respective pieces of data of the plurality of column data items, and that contains missing data of each of specific column data items in some of the entries; ( see at least: Fig.2, table includes rows (attribute j), column ( record i) and missing values [0016] calculating degree of correlation of a data item corresponding to a missing data value with another data item, in a case where the missing data value exists in a plurality of data records including data values corresponding to a plurality of data items, [0003-4] Each record has attribute values for a plurality of attributes. In FIG. 7, attribute values to which cancellation lines are applied are missing values.)
a parameter definition unit that defines an imputation amount adjustment parameter used for adjustment of a proportion requiring imputation in pieces of data of the specific column data items; ( see at least: [0051] As illustrated in FIG. 3, the data complementing apparatus 1 reads the missing threshold and the correlation threshold (step S1)[0037] The missing threshold is included in a set threshold 3. [0064] The data complementing apparatus 1 may determine whether or not the number of the complemented learning records 5 is sufficient, and when it is not sufficient, the correlation threshold and the missing threshold may be reduced).
a missing data prediction unit that predicts the missing data of each of the specific column data items on a basis of data of each of the column data items other than the specific column data items and on a basis of data of each of the specific column data items in the plurality of entries constituting the data table; ( see at least: [0016] performing complementation of the missing data value by a recursive method based on a data item value of the other data item, in a case where the degree of correlation is larger than a predete0rmined correlation threshold [0053] a case where the attribute in which the absolute value of the correlation value is larger than the correlation threshold is present, the data complementing apparatus 1 performs the regression complement (step S6) [0010-11])
an extraction unit that extracts at least one of the entries falling within a predetermined proportion defined by the imputation amount adjustment parameter in the respective pieces of data of the specific column data items; ( see at least: [0051-52] The data complementing apparatus 1 takes out one record from the total missing learning records 4, and determines presence/absence of the record (step S4). [0055] in a case where the record is absent in step S7, the data complementing apparatus 1 takes out one record from the total missing learning records 4 and determines presence/absence of the record (step S10))
Oki does not explicitly teach, but Resnick teaches an imputation priority flagging unit that gives an imputation priority flag to the at least one entry falling within the predetermined proportion; ( see at least: Fig. 1 steps 110-150, [0016] The techniques generally proceed by analyzing the cases in a computer-based reasoning model and determining which cases have missing fields. In some embodiments, conviction scores [imputation priority flagging] are then determined for the cases, and the cases and/or the features of the computer-based reasoning model are then ordered by conviction. [0021] Once it is known which have fields to impute in the computer-based reasoning model, then, in some embodiments, conviction scores for those cases can be determination 120 [0024-26])
Resnick further teaches a priority order determination unit that counts the number of the entries each given the imputation priority flag in the entries included in the data table, ( see at least: [0019] imputation order information (such as numbers of missing features and/or conviction scores) is determined 120 and the order of imputation is determined 130 based on the imputation order information [0024] determine the cases with the highest conviction among the missing features may have their data imputed first [0025] .he cases are sorted by conviction, without consideration of conviction related to features, and the cases with the highest conviction that have one or more missing fields, have those all or a subset of the missing one or more fields imputed. In some embodiments, the conviction of the feature is multiplied by the conviction of the case (that is missing that feature) and/or the number of missing features. The case with the highest product (or other function) of these two conviction numbers and/or the number of missing features is chosen as the next case for which to impute data)
Resnick further teaches determines an integrated imputation priority order in a descending order of the number of the imputation priority flags. ( see at least: [0024] determining 120 the imputation order information includes determining a sorted order of the cases, sorted by the number of missing features in the case. Further, among the cases with the same number of missing features, the cases may again be sorted. For example, all cases with N missing features may be sorted by the conviction associated with the cases themselves and/or missing features as discussed herein (and, for example, the cases with the highest [descending order] conviction among the missing features may have their data imputed first) [0025] The case with the highest product (or other function) of these two conviction numbers and/or the number of missing features is chosen as the next case for which to impute data.).
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the imputation priority flag and ranking feature for the same reasons its useful in Resnick -namely, the conviction score and/or imputation order information determined 120 is prediction conviction as a proxy for accuracy of a prediction (par.69). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
As per claim 2, Oki in view of Resnick teaches claim 1 as above. Oki further teaches:
a display unit that displays list data containing the data of each of the specific column data items ,( see at least: Fig.6 [0066] a digital visual interface (DVI) 56 [0056] e data complementing apparatus 1 outputs the learning data as the complemented learning record 5 (step S14) See Fig.2, table includes rows (attribute j), column ( record i) and missing values )
While Oki teaches the missing data, Oki does not explicitly teach the integrated imputation priority order for each of the entries containing the missing data. However, this is taught by Resnick ( [0016] conviction scores are then determined for the cases, and the cases and/or the features of the computer-based reasoning model are then ordered by conviction [0024] determining 120 the imputation order information includes determining a sorted order of the cases, sorted by the number of missing features in the case [0025] the cases are sorted by conviction, without consideration of conviction related to features, and the cases with the highest conviction that have one or more missing fields, have those all or a subset of the missing one or more fields imputed)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the imputation priority ranking feature for the same reasons its useful in Resnick -namely, the conviction score and/or imputation order information determined 120 is prediction conviction as a proxy for accuracy of a prediction (par.69). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
As per claim 3, Oki in view of Resnick teaches claim 2 as above. Oki further teaches:
While Oki teaches a plurality of the predetermined proportions defined as the imputation amount adjustment parameters, ( see at least : [0051] As illustrated in FIG. 3, the data complementing apparatus 1 reads the missing threshold and the correlation threshold (step S1), and calculates the correlation matrix between the attributes by using the total learning records 2 (step S2). [0064] The data complementing apparatus 1 may determine whether or not the number of the complemented learning records 5 is sufficient, and when it is not sufficient, the correlation threshold and the missing threshold may be reduce [ adjusted])
Resnick teaches the imputation priority flagging unit gives the imputation priority flag to each case. ( see at least:[0030] a batch may include a percentage of the cases in the computer-based reasoning system, such as 1%, 2%, 3%. [0016] conviction scores are then determined for the cases, and the cases and/or the features of the computer-based reasoning model are then ordered by conviction [0032] process 100 proceeds until there are no more missing fields in the computer-based reasoning model)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the imputation priority flag feature for the same reasons its useful in Resnick -namely, the conviction score and/or imputation order information determined 120 is prediction conviction as a proxy for accuracy of a prediction (par.69). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Oki taches display unit displays a data imputation measurement quantity based on the list data, ( see at least [0054] In a case where the record is absent in step S4, the data complementing apparatus 1 takes out one record from the total missing learning records 4 and determines presence/absence of the record (step S7) [0066] a digital visual interface (DVI) 56 [0056])
Oki further teaches prediction accuracy associated with the data of each of the specific column data items and obtained by the missing data prediction unit ( see at least [0064] By adjusting the correlation threshold and the missing threshold, the data complementing apparatus 1 may adjust the trade-off between the accuracy of complement and the number of learning records [0040] Tk is a missing threshold. As the Tk is larger, the accuracy of complement increases, and the number of records is reduced. By adjusting Tk, it is possible to adjust trade-off between the accuracy of complement and the number of records. [0016])
As per claim 4, Oki in view of Resnick teaches claim 3 as above. Oki further teaches:
the missing data prediction unit , a plurality of the predetermined proportions defined as the imputation amount adjustment parameters, ( see at least: [0051] As illustrated in FIG. 3, the data complementing apparatus 1 reads the missing threshold and the correlation threshold (step S1), and calculates the correlation matrix between the attributes by using the total learning records 2 (step S2). [0064] The data complementing apparatus 1 may determine whether or not the number of the complemented learning records 5 is sufficient, and when it is not sufficient, the correlation threshold and the missing threshold may be reduced [ adjusted]. By adjusting the correlation threshold and the missing threshold, the data complementing apparatus 1 may adjust the trade-off between the accuracy of complement and the number of learning records)
Oki does not explicitly teach, but Resnick teaches selects one predetermined proportion appropriate for obtaining sufficient total prediction accuracy of learning data containing the missing data for meeting target accuracy. ( see at least: [0030] a batch may include a percentage of the cases in the computer-based reasoning system, such as 1%, 2%, 3%. In some embodiments batch size is a combination of a percentage and a fixed number. As noted elsewhere herein, larger batches may be useful to improve performance and reduce computational spend. Smaller batches may provide more accurate results [selecting a particular percentage batch corresponds to selecting predetermined proportion for processing for accurate result], [0032])
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the selecting predetermined proportion feature for the same reasons its useful in Resnick -namely, to increase the accuracy of the imputation of data (par.30). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
As per claim 5, Oki in view of Resnick teaches claim 1 as above. Oki further teaches:
missing data prediction unit learns a model on a basis of an explanatory variable as the data of each of the column data items other than the specific column data items ( see at least: [0042] The regression complement unit 12 reads a missing learning record 4, which is a collection of the learning records containing a missing, and performs the regression complement. [0016] performing complementation of the missing data value by a recursive method based on a data item value of the other data item [other columns], in a case where the degree of correlation is larger than a predetermined correlation threshold).
on a basis of an objective variable as the data of each of the specific column data items in the plurality of entries constituting the data table, ( see at least: [0042] The regression complement unit 12 reads a missing learning record 4, which is a collection of the learning records containing a missing, and performs the regression complement [ target / objective variable] [0003] Each record has attribute values for a plurality of attributes See Fig. 7 data table structure with multiple records, rows, and attributes columns)
and predicts the missing data of each of the specific column data items by using the model. ( see at least: [0016] performing complementation of the missing data value by a recursive method based on a data item value of the other data item, in a case where the degree of correlation is larger than a predetermined correlation threshold).
Oki does not explicitly teach through machine learning; however, this is taught by Resnick ( see at least; [0028] after determining 130 which cases to impute data and/or the order in which to impute data, the imputed data is determined 140 based on the case with the missing data and the imputation model. The imputation model may be any appropriate statistical or other machine learning model. Such a machine learning model would then be able to predict what data is missing for each missing field for each case. [0017] machine learning )
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the machine learning model feature for imputation for the same reasons its useful in Resnick -namely, to predict what data is missing for each missing field for each case (par.28). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claims 6-10 recite similar limitations as claims 1-5, therefore they are rejected over the same rationales.
Conclusion
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/MANAL A. ALSAMIRI/Examiner, Art Unit 3628