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 .
This Office action is responsive to the communication received on 03/03/2023. The claims 1-14 are pending, of which the claim(s) 1, 13, & 14 is/are in independent form.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1- 14 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
I) Regarding claim 1, the claim recites elements: 1) "the relevant node" in the limitation of “the relevant node is a node based on one or more measurement data” 2) “the relevant branch portion” in limitation of “nodes belonging to the relevant branch portion, and one or more attribute values” and 3) “the relevant attribute item” in limitation “corresponding to the relevant attribute item, and represents a degree that the relevant attribute item will fit as a base of a branch condition”. See lines 20 and 31-32 in page 32 of claim listing.
However, there is insufficient antecedent basis for these claim elements in the claim thereby rendering the scope of the claim indefinite.
For the examination purpose, “the relevant node” is interpreted as “a [[the]] relevant node”, “the relevant branch portion” is interpreted as “[[the]] a relevant branch portion”, and “the relevant attribute item” is interpreted as “[[the]] a relevant attribute item”.
II) Regarding claim 13,
(a) the claim 13 recites the phrase “a computer” in three different limitations of the claim to generate ‘first tree structure’, ‘goodness-of-fit data’, and ‘second tree structure’. See in line 15 of page 35, in line 27 of page 35, and in line 4 of page 36. However, the claim fails to clarify whether computer used to calculate goodness-of-fit and second tree structures are same computers as that of computer used to generate a first tree structure or not. That is, claim 13 fails to recite “the computer” to generate “goodness-of-fit data” and “second tree structure” thereby rendering the scope of the claim indefinite.
For the examination purpose, the limitations of “a computer generates a second tree structure” and “a computer generates goodness-of-fit data” are being interpreted as “the [[a] computer generates a second tree structure” “[[a] the computer generates goodness-of-fit data” respectively.
(b) the claim recites the elements: "the relevant node" in the limitation of “the relevant node is a node based on one or more”; “the relevant branch portion” in limitation of “child nodes belonging to the relevant branch portion, and one or more attribute values”; and “the relevant attribute item” in the limitation of “corresponding to the relevant attribute item, and represents a degree that the relevant”. See, line 22 in the page 35 and lines 1-2 in page 36. There is insufficient antecedent basis for these elements in the claim.
For the examination purpose, they are interpreted as “a relevant node”, “a relevant branch portion”, and “a [[the]] relevant attribute item”.
III) Regarding claim 14, this claim also rejected for the similar reasons (a) and (b) set forth above in the claim 13 for the claim elements of “a computer”, “the node”, “the relevant branch portion”, and “the relevant attribute item”.
IV) Regarding claims 2- 3, the term “a same similarity range” is a relative term which renders the claim indefinite. The term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The BRI of the term same similarity range has not been elaborated/clarified in the specification other than merely mentioning it in paras. 032, 086. Accordingly, this term is subject to different interpretations and fails to provide clear cut scope to PHOSITA.
V) Regarding claims 2- 11, they are also rejected to because of their dependency with rejected claim 1.
Allowable Subject Matter
Claims 1- 14 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is an examiner’s statement of reasons for indicating allowable over prior arts of the record.
Chen et al. (JP 2020035413 A), Achin (US 20150339572 A1), and Tanaka (US 12061960 B2) are closest references to the claimed subject matter.
As to claims 1, 13, & 14:
1) Chen (JP 2020035413 A, see attached FOR document) teaches a data analysis system [Fig. 1, “a prediction server 100” + “prediction terminal 200”], comprising: an interface apparatus which accepts inputs [“the prediction server 100 constructs a power demand prediction model based on the attribute data of the customer 10 and the actual power usage data of the customer 10.”] of measurement data [“usage data”] and attribute data; a storage apparatus which stores the measurement data and the attribute data input via the interface apparatus; and a processor [“processor 111, a memory 112”] which is coupled to the interface apparatus and the storage apparatus, wherein: the attribute data [“inputting attribute data of a new user”] includes one or more attribute values at one or more points in time [“month characteristic data is composed of twelve data indicating the power consumption for each month,”] regarding each of one or more attribute items; the processor generates a first tree structure [“model construction unit 125 randomly generates a plurality of decision trees T1 to Tn based on the attribute data”] which represents a relation of a plurality of measurement data sets according to at least a part of the measurement data stored in the storage apparatus; each of the plurality of measurement data sets is a data set including values measured at each of one or more points in time; …. one or more nodes [“branch function of the Node N1”] …and the processor outputs estimation data [“outputting a prediction result of at which pattern”] based on a result of referring to the second tree structure [another free out of plurality of decision trees]
However, Chen fails to teach remaining limitations of the claim 1 and similar limitations in claims 13 & 14. That is, Chen does not teach, inter alia, generating of the second tree structures is based on the generated goodness-of fit data of a branch portion of the first tree structure as claimed.
2) Achin (US 20150339572 A1) teaches a techniques for predictive data analytics using one or more tree structures (Abstract, [0204]).
3) Tanaka (US 12061960 B2) teaches a data analysis system with a processor and memory to use a processor to perform learning of a decision tree using a branch score calculator, wherein the processor generates goodness-of-fit data [“calculate a branch score, of a branch of the decision tree”] based on at least a part of the attribute data stored in the storage apparatus regarding one or more branch portions included in the first tree structure; the goodness-of-fit data [“method of calculating the branch score by the learning module 20”] includes goodness-of-fit regarding each of one or more attribute items regarding each of the one or more branch portions (Claim 1, fig. 12, Col 24 lines 20- 40).
However, the prior arts of the record, alone or in any proper combination, do not teach or suggest each of the limitations of the claims 1, 13, & 14. More specifically, the prior arts do not teach or suggest the inclusion of:
As to claim 1, “the goodness-of-fit data includes goodness-of-fit regarding each of one or more attribute items regarding each of the one or more branch portions; with regard to each of the one or more attribute items for each branch portion, goodness-of-fit is a value calculated based on a parent node and two or more child nodes belonging to the relevant branch portion, and one or more attribute values corresponding to the relevant attribute item, and represents a degree that the relevant attribute item will fit as a base of a branch condition; the processor generates a second tree structure as a tree structure in which a branch condition decided based on the goodness-of-fit data is associated with a branch portion included in the first tree structure; and the processor outputs estimation data based on a result of referring to the second tree structure from a root node to a leaf node with input data including one or more attribute values regarding at least one attribute item as an input”.
As to claim 13, “the goodness-of-fit data includes goodness-of-fit regarding each of one or more attribute items regarding each of the one or more branch portions; with regard to each of the one or more attribute items for each branch portion, goodness-of-fit is a value calculated based on a parent node and two or more child nodes belonging to the relevant branch portion, and one or more attribute values corresponding to the relevant attribute item, and represents a degree that the relevant attribute item will fit as a base of a branch condition; a computer generates a second tree structure as a tree structure in which a branch condition decided based on the goodness-of-fit data is associated with a branch portion included in the first tree structure; and a computer outputs estimation data based on a result of referring to the second tree structure from a root node to a leaf node with input data including one or more attribute values regarding at least one attribute item as an input.”
As to claim 14, “the goodness-of-fit data includes goodness-of-fit regarding each of one or more attribute items regarding each of the one or more branch portions; with regard to each of the one or more attribute items for each branch portion, goodness-of-fit is a value calculated based on a parent node and two or more child nodes belonging to the relevant branch portion, and one or more attribute values corresponding to the relevant attribute item, and represents a degree that the relevant attribute item will fit as a base of a branch condition; and a computer generates a second tree structure as a tree structure in which a branch condition decided based on the goodness-of-fit data is associated with a branch portion included in the first tree structure”.
As to claims 2- 11, they are also allowable over prior arts of the record of their dependency with claim 1 and other limitations they further recite.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Carvalho (US 20210224247 A1) teaches estimations based on statistical tree structures (Abstract).
2) Matthes (US 20060259744 A1) teaches using of a first tree structure and a second tree structure ([0623]).
3) Takagi et al. (US 20210004698 A1) teaches a computer to generate tree structure data responsive to input data to be predicted (Abstract).
4) Shi (US 20220188648 A1) teaches using a regression tree model with a binary tree structure may be obtained by using the CART tree algorithm (para. 045), which is one of the algorithm (see spec, para. [080]) used by applicants to generate their first and second tree structures.
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115