DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is responsive to the following communication: Original claims filed 05/18/23. This action is made non-final.
3. Claims 1-15 are pending in the case. Claims 1, 14 and 15 are independent claims.
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.
Claim 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a device type claim. Claim 14 is a method claim. Claim 15 is a medium claim. Therefore, claims 1-15 are directed to either a process, machine, manufacture or composition of matter.
With respect to claim 1, 14 and 15:
2A Prong 1:
a reception unit that receives transformed features obtained by transforming, in accordance with a predetermined rule, features contained in pieces of learning data individually retained in the plurality of learning devices (mental process – a user can receive data);
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a distribution analysis unit that analyzes distributions of a plurality of the features of the plurality of learning devices on a basis of the transformed features received by the reception unit for each of the learning devices (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
an output unit that outputs a distribution analysis result analyzed by the distribution analysis unit (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a distribution analysis unit that analyzes distributions of a plurality of the features of the plurality of learning devices on a basis of the transformed features received by the reception unit for each of the learning devices (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
an output unit that outputs a distribution analysis result analyzed by the distribution analysis unit (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 2:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a distribution analysis unit that analyzes distributions of a plurality of the features of the plurality of learning devices on a basis of the transformed features received by the reception unit for each of the learning devices (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the output unit outputs information associated with similarity between the features of the learning devices, as the distribution analysis result. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 3:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the output unit outputs information associated with similarity between the features of the learning devices, as the distribution analysis result. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein information associated with similarity between the features of the learning devices is map information that indicates similarity between features of each combination of the two learning devices in the plurality of learning devices. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 4:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein information associated with similarity between the features of the learning devices is a dendrogram that indicates similarity between the features of the plurality of learning devices. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein information associated with similarity between the features of the learning devices is a dendrogram that indicates similarity between the features of the plurality of learning devices. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 5:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the distribution analysis unit selects, for each of the plurality of learning devices, on a basis of the distribution analysis result, any one of a first learning method that generates a prediction model by using the learning data retained in the corresponding learning device, a second learning method that generates one prediction model integrated by federated learning with a different learning device, and a third learning method that generates one or more prediction models integrated by federated learning with a different learning device having a feature similar to the feature of the corresponding learning device, and determines the selected learning method as a learning method applied to the corresponding learning device, and the output unit transmits, to each of the plurality of learning devices, the learning method determined by the distribution analysis unit for the corresponding one of the plurality of learning devices. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the distribution analysis unit selects, for each of the plurality of learning devices, on a basis of the distribution analysis result, any one of a first learning method that generates a prediction model by using the learning data retained in the corresponding learning device, a second learning method that generates one prediction model integrated by federated learning with a different learning device, and a third learning method that generates one or more prediction models integrated by federated learning with a different learning device having a feature similar to the feature of the corresponding learning device, and determines the selected learning method as a learning method applied to the corresponding learning device, and the output unit transmits, to each of the plurality of learning devices, the learning method determined by the distribution analysis unit for the corresponding one of the plurality of learning devices. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 6:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a wherein the distribution analysis unit determines any one of the first learning method, the second learning method, and the third learning method as the learning method applied to the corresponding one of the plurality of learning devices, on a basis of the distribution analysis result. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a wherein the distribution analysis unit determines any one of the first learning method, the second learning method, and the third learning method as the learning method applied to the corresponding one of the plurality of learning devices, on a basis of the distribution analysis result. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 7:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the distribution analysis unit determines any one of the first learning method, the second learning method, and the third learning method as the learning method applied to the corresponding one of the plurality of learning devices, on a basis of distances between the transformed features of the plurality of learning devices. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the distribution analysis unit determines any one of the first learning method, the second learning method, and the third learning method as the learning method applied to the corresponding one of the plurality of learning devices, on a basis of distances between the transformed features of the plurality of learning devices. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 8:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the distribution analysis unit determines any one of the first learning method, the second learning method, and the third learning method as the learning method applied to the corresponding one of the plurality of learning devices, on a basis of the transformed features and a threshold. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the distribution analysis unit determines any one of the first learning method, the second learning method, and the third learning method as the learning method applied to the corresponding one of the plurality of learning devices, on a basis of the transformed features and a threshold. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 9:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the distribution analysis unit determines any one of the first learning method, the second learning method, and the third learning method as the learning method applied to the corresponding one of the plurality of learning devices, on a basis of the transformed features, the threshold, and a limiting condition. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the distribution analysis unit determines any one of the first learning method, the second learning method, and the third learning method as the learning method applied to the corresponding one of the plurality of learning devices, on a basis of the transformed features, the threshold, and a limiting condition. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 10:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the limiting condition is the number of the learning devices each having the similar feature. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the limiting condition is the number of the learning devices each having the similar feature. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 11:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the limiting condition is the number of sets of the learning devices each having the similar feature (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the limiting condition is the number of sets of the learning devices each having the similar feature (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 12:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the reception unit receives model parameters of identifiers generated by learning of the pieces of learning data individually retained in the plurality of learning devices, the identifiers identifying the respective learning devices, the distribution analysis unit generates an integrated identifier by integrating the identifiers of the learning devices on a basis of the model parameters of the identifiers of the learning devices, the model parameters being received by the reception unit, a transmission unit transmits, to each of the plurality of learning devices, a model parameter of the integrated identifier generated by the distribution analysis unit, the reception unit receives, from each of the plurality of the learning devices, an identification result obtained by the integrated identifier, the distribution analysis unit selects, for each of the plurality of learning devices, on a basis of the identification result received by the reception unit from each of the learning 69 devices, any one of a first learning method that generates a prediction model by using the learning data retained in the corresponding learning device, a federated learning method that generates one prediction model integrated by federated learning with a different learning device having a feature similar to the feature of the corresponding learning device, and a third learning method that generates one or more prediction models integrated by federated learning with a different learning device having a feature similar to the feature of the corresponding learning device, and determines the selected learning method as a learning method applied to the corresponding learning device, and the output unit transmits, to each of the plurality of learning devices, the learning method determined by the distribution analysis unit for the corresponding one of the plurality of learning devices. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the reception unit receives model parameters of identifiers generated by learning of the pieces of learning data individually retained in the plurality of learning devices, the identifiers identifying the respective learning devices, the distribution analysis unit generates an integrated identifier by integrating the identifiers of the learning devices on a basis of the model parameters of the identifiers of the learning devices, the model parameters being received by the reception unit, a transmission unit transmits, to each of the plurality of learning devices, a model parameter of the integrated identifier generated by the distribution analysis unit, the reception unit receives, from each of the plurality of the learning devices, an identification result obtained by the integrated identifier, the distribution analysis unit selects, for each of the plurality of learning devices, on a basis of the identification result received by the reception unit from each of the learning 69 devices, any one of a first learning method that generates a prediction model by using the learning data retained in the corresponding learning device, a federated learning method that generates one prediction model integrated by federated learning with a different learning device having a feature similar to the feature of the corresponding learning device, and a third learning method that generates one or more prediction models integrated by federated learning with a different learning device having a feature similar to the feature of the corresponding learning device, and determines the selected learning method as a learning method applied to the corresponding learning device, and the output unit transmits, to each of the plurality of learning devices, the learning method determined by the distribution analysis unit for the corresponding one of the plurality of learning devices. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 13:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the reception unit receives, from each of the learning devices, a model parameter of the prediction model generated by the corresponding learning device on a basis of the learning method determined by the distribution analysis unit, and a generation unit that generates the prediction model on a basis of the model parameter received by the reception unit from the corresponding learning device is provided. 14. An analysis method performed by an analysis device capable of communicating with a plurality of learning devices, the analysis method comprising: a reception process that receives transformed features obtained by transforming, in accordance with a predetermined rule, features contained in pieces of learning data individually retained in the plurality of learning devices; a distribution analysis process that analyzes distributions of a plurality of the features of the plurality of learning devices on a basis of the transformed features received by the reception process for each of the learning devices; and an output process that outputs a distribution analysis result analyzed by the distribution analysis process. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the reception unit receives, from each of the learning devices, a model parameter of the prediction model generated by the corresponding learning device on a basis of the learning method determined by the distribution analysis unit, and a generation unit that generates the prediction model on a basis of the model parameter received by the reception unit from the corresponding learning device is provided. 14. An analysis method performed by an analysis device capable of communicating with a plurality of learning devices, the analysis method comprising: a reception process that receives transformed features obtained by transforming, in accordance with a predetermined rule, features contained in pieces of learning data individually retained in the plurality of learning devices; a distribution analysis process that analyzes distributions of a plurality of the features of the plurality of learning devices on a basis of the transformed features received by the reception process for each of the learning devices; and an output process that outputs a distribution analysis result analyzed by the distribution analysis process. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
35 U.S.C. 112(f)
5. 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.
6. With regard to claim 1, claim limitations “a reception unit”, “a distribution analysis unit”, and an “output unit” configured to” for presenting”, “means for providing" and “means for performing” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder (e.g. “unit”) coupled with functional language (e.g. “configured to”, etc.) without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
With regard to claim 12, claim limitations “a transmission unit” configured to” for presenting”, “means for providing" and “means for performing” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder (e.g. “unit”) coupled with functional language (e.g. “configured to”, etc.) without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
With regard to claim 12, claim limitations “a generation unit” configured to” for presenting”, “means for providing" and “means for performing” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder (e.g. “unit”) coupled with functional language (e.g. “configured to”, etc.) without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claims 2-13 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Objections
7. Claims 5-13 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.
Claim Rejections - 35 USC § 102
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. Claim 1, 2, 14 and 15 are rejected under 35 U.S.C. 102(a)(1) as being rejected by anticipated by Mitarai (US 20170364826).
Regarding claim 1, Mitarai discloses an analysis device capable of communicating with a plurality of learning devices the analysis device comprising:
a reception unit that receives transformed features obtained by transforming (see FIG. 2 wherein the transformation processing unit transforms data), in accordance with a predetermined rule (see paragraph 0005 and a transformation rule), features contained in pieces of learning data individually retained in the plurality of learning devices (see FIG. 7, classifier generating unit);
a distribution analysis unit that analyzes distributions of a plurality of the features of the plurality of learning devices on a basis of the transformed features received by the reception unit for each of the learning devices (see paragraph 0009 and the data being distributed, also see FIG. 9 the classification result outputting unit); and
an output unit that outputs a distribution analysis result analyzed by the distribution analysis unit (see FIG. 9 outputting unit).
Regarding claim 2, Mitarai discloses wherein the output unit outputs information associated with similarity between the features of the learning devices, as the distribution analysis result (see FIG. 7, similar task searching unit).
Regarding claim 14, the subject matter of the claim is substantially similar to claim 1 and as such the same rationale of rejection applies.
Regarding claim 15, the subject matter of the claim is substantially similar to claim 1 and as such the same rationale of rejection applies.
Claim Rejections - 35 USC § 103
10. 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 of this title, 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.
11. Claim 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Mitarai in further view of Pan (US 20220222924).
Regarding claim 3, Mitarai does not disclose wherein information associated with similarity between the features of the learning devices is map information that indicates similarity between features of each combination of the two learning devices in the plurality of learning devices.
However, Pan discloses an ISAM output file delivery can include precision, recall, and F1-scores. For example, if the precision, recall, and F1-scores are acceptable, a suggested group can be generated with a selected percentile mapping as per processed connected component sizes where the groups can be updated using ISAM platform. In this example, if the evaluation metrics are not acceptable, a different set of mapping can be used for size to percentile cut-off for the dendrogram and regenerate results until a particular precision-recall can be achieved (paragraph 0177).
The combination of Mitarai and Pan would result in the classification of data of Mitarai to utilize Pan’s teachings of utilizing similarity mappings between the data. One of ordinary skill would have been motivated to have combined the teachings because a user in Mitarai would have benefited from using such similarity teachings to build a more robust classification system. As such, the combination would have been predictable to one of ordinary skill in the art.
Regarding claim 4, Mitarai does not disclose wherein information associated with similarity between the features of the learning devices is a dendrogram that indicates similarity between the features of the plurality of learning devices.
However, Pan discloses wherein iterative splitting (e.g., iterative divisive branching) can be performed by implementing a hierarchy tree or a dendogram to organize items belonging to the hierarchy, as per levels of item similarity (paragraph 0127).
The combination of Mitarai and Pan would result in the classification of data of Mitarai to utilize Pan’s teachings of utilizing similarity mappings between the data. One of ordinary skill would have been motivated to have combined the teachings because a user in Mitarai would have benefited from using such similarity teachings to build a more robust classification system. As such, the combination would have been predictable to one of ordinary skill in the art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID E CHOI whose telephone number is (571)270-3780. The examiner can normally be reached on M-F: 7-2, 7-10 (PST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bechtold, Michelle T. can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID E CHOI/Primary Examiner, Art Unit 2148
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