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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/04/2025 has been entered.
Claim Rejections - 35 USC § 102
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.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-22 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Baur et al. (US 2022/0391414).
Regarding Claim 1, Baur discloses an information processing apparatus comprising processing circuitry configured to:
acquire data including a plurality of variable names (Fig. 3, 308: “Contextualized embedding of token” and 310: “Fully Embedded Text”, Baur) and a plurality of values associated with the plurality of variable names (Fig. 3, 316: “Numeric embedding” and 318: “Fully Embedded Numeric”, Baur), and a correspondence relationship between the plurality of variable names and the plurality of values ([0032], Baur);
generate a plurality of variable name vectors corresponding to the respective variable names ([0046] and [0047], contextualized sequence, Baur) and a plurality of value vectors corresponding to the respective values associated with each variable name ([0037], [0040], and [0050], Baur);
generate a combined vector by combining a corresponding one of the plurality of variable name vectors and a corresponding one of the plurality of value vectors based on the correspondence relationship ([0040], Fig. 3, “concat”, and Fig. 4, 422, Baur); and
output the combined vector, wherein each of the plurality of variable names includes text data, each of the plurality of variable name vectors is an embedding vector, and the combined vector includes the embedding vector (Fig. 4, arrow from 422 to 424 and arrow from 424 to 426 show outputting the combined vector as claimed; Baur).
Regarding Claim 2, Baur discloses an information processing apparatus according to claim 1, wherein the value is a categorical value, and the value vector is a vector corresponding to the categorical value ([0042], Baur).
Regarding Claim 3, Baur discloses an information processing apparatus according to claim 2, wherein the processing circuitry is configured to:
perform token division on the variable name and the categorical value for a variable including the categorical value and the variable name associated with the categorical value among the acquired data, and generate a token embedding vector corresponding to each obtained token ([0027], [0031]-[0033], and [0043], Fig. 4, 406: Embed index, 414: Embed origin; Baur);
specify, for each token, the variable from which the token embedding vector is derived and the variable name or the categorical value from which the token embedding vector is derived ([0027], [0031]-[0033], and [0043], Fig. 4, 406: Embed index, 414: Embed origin; Baur); and
generate the variable name vector from the token embedding vector derived from the variable name and generate the value vector from the token embedding vector derived from the categorical value for each variable based on the specified result ([0027], [0031]-[0033], and [0043], Fig. 4, 406: Embed index, 414: Embed origin; Baur), and
the embedding vector obtained from the text data is the token embedding vector derived from the variable name (Fig. 4, 406: Embed index, 414: Embed origin; Baur).
Regarding Claim 4, Baur discloses an information processing apparatus according to claim 3, wherein the processing circuitry is configured to generate the value vector by processing, using a neural network, a set of token vectors derived from the categorical value ([0033], Baur).
Regarding Claim 5, Baur discloses an information processing apparatus according to claim 3, wherein the processing circuitry is configured to generate the value vector by averaging the token vectors derived from the categorical value ([0027], Baur).
Regarding Claim 6, Baur discloses an information processing apparatus according to claim 1, wherein the processing circuitry is configured to generate the value vector that is a sentence vector by performing text analysis on the value in a case where the value is a categorical value or sentence data ([0031] and [0033], Baur).
Regarding Claim 7, Baur discloses an information processing apparatus according to claim 1, wherein the value is a numerical value, and the value vector is a vector corresponding to the numerical value ([0037], “To represent the numeric values, the integers may be encoded as vectors of cosines and/or sines of geometrically progressing frequencies as shown in Equation 1,” Baur).
Regarding Claim 8, Baur discloses an information processing apparatus according to claim 7, wherein the processing circuitry is configured to input the numerical value to a neural network and generate the value vector that is an output from the neural network ([0037], “To represent the numeric values, the integers may be encoded as vectors of cosines and/or sines of geometrically progressing frequencies as shown in Equation 1,” and [0039], Baur).
Regarding Claim 9, Baur discloses an information processing apparatus according to claim 7, wherein the processing circuitry is configured to generate the value vector by linearly transforming the numerical value ([0035], Baur).
Regarding Claim 10, Baur discloses an information processing apparatus according to claim 1, wherein the processing circuitry is configured to acquire the data and the correspondence relationship from tabular data having a plurality of variables in a sample along a row direction, each of the variables having the variable name and the value along a column direction (Fig. 1 and 3, Baur).
Regarding Claim 11, Baur discloses an information processing apparatus according to claim 1, wherein the processing circuitry is configured to generate the variable name vector by processing, using a neural network, a set of token embedding vectors obtained from tokens constituting the variable name ([0032], Fig. 4, 406: Embed index, 414: Embed origin; Baur), and
the embedding vector obtained from the text data is the token embedding vectors (Fig. 4, 406: Embed index, 414: Embed origin; Baur).
Regarding Claim 12, Baur discloses a information processing apparatus according to claim 1, wherein the processing circuitry is configured to generate the variable name vector by averaging token embedding vectors obtained from tokens constituting the variable names ([0024] and [0027], Fig. 4, 406: Embed index, 414: Embed origin; Baur), and
the embedding vector obtained from the text data is the token embedding vectors (Fig. 4, 406: Embed index, 414: Embed origin; Baur).
Regarding Claim 13, Baur discloses an information processing apparatus according to claim 1, wherein the processing circuitry is configured to combine the variable name vector and the value vector using a sum of the variable name vector and the value vector related to an identical variable name ([0027] and [0041], Baur).
Regarding Claim 14, Baur discloses an information processing apparatus according to claim 1, wherein the processing circuitry is configured to combine the variable name vector and the value vector by arranging respective elements of the variable name vector and the value vector related to an identical variable name ([0027] and [0047], Baur).
Regarding Claim 15, Baur discloses an information processing apparatus according to claim 1, wherein the processing circuitry is configured to output a vector obtained by combining the variable name vector and the value vector by inputting, to a neural network, the variable name vector and the value vector related to an identical variable name ([0027] and [0047], Baur).
Regarding Claim 16, Baur discloses an information processing apparatus according to claim 1, wherein the processing circuitry is further configured to input a set of the combined vectors to a neural network to perform classification processing or regression processing based on the set ([0027], Baur).
Regarding Claim 17, Baur discloses an information processing method comprising:
acquiring, by processing circuitry, data including a plurality of variable names (Fig. 3, 308: “Contextualized embedding of token” and 310: “Fully Embedded Text”, Baur) and a value plurality of values associated with the plurality of variable names (Fig. 3, 316: “Numeric embedding” and 318: “Fully Embedded Numeric”, Baur), and a correspondence relationship between the plurality of variable names and the value plurality of values ([0032], Baur);
generating, by the processing circuitry, a plurality of variable name vectors corresponding to the respective variable names ([0046] and [0047], contextualized sequence, Baur) and a plurality of value vectors corresponding to the respective values ([0037], [0040], and [0050], Baur);
generating a combined vector by combining a corresponding one of the plurality of variable name vectors and a corresponding one of the plurality of value vectors based on the correspondence relationship ([0040], Fig. 3, “concat”, and Fig. 4, 422, Baur); and
outputting the combined vector, wherein each of the plurality of variable names includes text data, each of the plurality of variable name vectors is an embedding vector, and the combined vector includes the embedding vector (Fig. 4, arrow from 422 to 424 and arrow from 424 to 426 show outputting the combined vector as claimed; Baur).
Regarding Claim 18, Baur discloses a non-transitory computer readable storage medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising:
acquiring data including a plurality of variable names (Fig. 3, 308: “Contextualized embedding of token” and 310: “Fully Embedded Text”, Baur) and a plurality of values associated with the plurality of variable names (Fig. 3, 316: “Numeric embedding” and 318: “Fully Embedded Numeric”, Baur), and a correspondence relationship between the plurality of variable names and the plurality of values ([0032], Baur);
generating a plurality of variable name vectors corresponding to the respective variable names ([0046] and [0047], contextualized sequence, Baur) and a plurality of value vectors corresponding to the respective values ([0037], [0040], and [0050], Baur);
generating a combined vector by combining a corresponding one of the plurality of variable name vectors and a corresponding one of the plurality of value vectors based on the correspondence relationship ([0040], Fig. 3, “concat”, and Fig. 4, 422, Baur); and
outputting the combined vector, wherein each of the plurality of variable names includes text data, each of the plurality of variable name vectors is an embedding vector, and the combined vector includes the embedding vector (Fig. 4, arrow from 422 to 424 and arrow from 424 to 426 show outputting the combined vector as claimed; Baur).
Regarding Claim 19, Baur discloses an information processing apparatus according to claim 1, wherein the processing circuitry is configured to generate the combined vector using a sum of the corresponding one of the plurality of variable name vectors and the corresponding one of the plurality of value vectors ([0040], Fig. 3, “concat”, and Fig. 4, 422, Baur).
Regarding Claim 20, Baur discloses an information processing apparatus according to claim 1, wherein a dimension of the combined vector is based on a dimension of the corresponding one of the plurality of variable name vectors and a dimension of the corresponding one of the plurality of value vectors ([0040], Fig. 3, “concat”, and Fig. 4, 422, Baur).
Regarding Claim 21, Baur discloses an information processing apparatus according to claim 1, wherein a dimension of the combined vector, a dimension of the corresponding one of the plurality of variable name vectors, and a dimension of the corresponding one of the plurality of value vectors are equal to one another ([0040], Fig. 3, “concat”, and Fig. 4, 422, Baur).
Regarding Claim 22, Baur discloses an information processing apparatus according to claim 1, wherein a dimension number of the combined vector is equal to a sum of a dimension number of the corresponding one of the plurality of variable name vectors and a dimension number of the corresponding one of the plurality of value vectors ([0040], Fig. 3, “concat”, and Fig. 4, 422, Baur).
Response to Arguments
Applicant argues that the applied art fails to disclose; “generate a combined vector by combining a corresponding one of the plurality of variable name vectors and a corresponding one of the plurality of value vectors based on the correspondence relationship; and output the combined vector, wherein each of the plurality of variable names includes text data, each of the plurality of variable name vectors is an embedding vector, and the combined vector includes the embedding vector.”
The Examiner respectfully disagrees. The applied art does disclose: generate a combined vector by combining a corresponding one of the plurality of variable name vectors and a corresponding one of the plurality of value vectors based on the correspondence relationship ([0040], Fig. 3, “concat”, and Fig. 4, 422, Baur); and output the combined vector, wherein each of the plurality of variable names includes text data, each of the plurality of variable name vectors is an embedding vector, and the combined vector includes the embedding vector (Fig. 4, arrow from 422 to 424 and arrow from 424 to 426 show outputting the combined vector as claimed; Baur).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GIOVANNA B COLAN whose telephone number is (571)272-2752. The examiner can normally be reached on Mon - Fri 8:30-5:00.
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/GIOVANNA B COLAN/Primary Examiner, Art Unit 2165
January 5, 2026