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 .
Note on Prior art
There is no art rejection for claims 10-14. In claim 10, Applicant recites, “extract a feature of a time-series change in the company-to-company transaction relation using a predetermined algorithm from the graph to which a growth record of the intended company indicated by the intended company attribute information is assigned as a label, and then determines an explanatory variable of the growth potential of the intended company based on an extraction result, thereby generating the estimation model including the explanatory variable.” The prior art of record doesn’t teach or make obvious extracting a feature “using a predetermined algorithm from the graph to which a growth record… is assigned as a label, and then determin[ing] and explanatory variable of the growth potential… thereby generating the estimation model….” Id.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental concept or method of organizing human activity without significantly more. The claims recite estimating growth potential based on accounting information, extracting features from accounting information, determining explanatory variables from a graph of accounting information and determining importance of accounting information. This judicial exception is not integrated into a practical application because the elements directed to generating a model and displaying results are insignificant extra-solution activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the elements directed to a processor and memory are generic computer parts.
Claim Rejections - 35 USC § 103
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.
Claims 1, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach by Rezazadeh (2/4/2020 version) hereafter Reza and US20180181903A1 to Kojima et al.
Claims 2-7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach by Rezazadeh (2/4/2020 version) hereafter Reza, US20180181903A1 to Kojima et al and One Explanation Does Not Fit All by Sokol et al.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach by Rezazadeh (2/4/2020 version) hereafter Reza, US20180181903A1 to Kojima et al, One Explanation Does Not Fit All by Sokol et al and US20050222929A1 to Steier et al.
Reza teaches claims 1, 16 and 17. A growth potential estimation system comprising:
a memory storing instructions; and
one or more processors configured to execute the instructions to: (Reza abs “a practical Machine Learning (ML) workflow to empower B2B sales outcome (win/lose) prediction within a cloud-based computing platform: Microsoft Azure Machine Learning Service (Azure ML).”)
estimate a growth potential of an intended company after a second period based on an estimation model representing a relation between transaction information, (Reza Fig. 2 below shows the account information for a first period, “WonValueMean”. The growth potential from the first period is the “WinRate”. The growth potential after the second period is the estimated “likelihood of winning new sales opportunities”. Reza abs. Reza fig. 3 shows that CRM sales/win data “Enhane [sic] with Engineered Features” is plugged into the prediction engine, “ML Pipeline: Trains various classification models on closed opportunities data after an extensive data cleaning and feature engineering. (B) Prediction Pipeline: Uses the optimal ML model to make predictions on new opportunities as well as inferring optimal decision boundaries.”)
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Reza doesn’t look at a second time period and its data isn’t a time-series.
However, Kojima teaches estimate a growth potential of an intended company after a second period based on an estimation model representing a relation between transaction information, account time-series information, and intended company attribute information of the intended company in a first period, (Kojima fig. 12 shows the last three time periods as three consecutive years of Decembers, see below. The different types of information are outlined below.)
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the transaction information represents a time-series change in a company-to-company transaction relation of the intended company, (Kojima fig. 12 Balance sheet B/S.)
the account time-series information represents a time-series change in deposits and withdrawals of account of the intended company, and (Kojima fig. 12 profit and loss P/L.)
the intended company attribute information represents a time-series change in an attribute of the intended company. (Kojima fig. 12 operating profit.)
The claims, Kojima and Reza all teach economic calculations. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to include all the metrics in Kojima’s time-series-change form because Kojima says that “priority should be changed into (1) balance sheet, (2) cash flow statement, and (3) income statement…” Kojima para 3.
Kojima teaches claim 2. The growth potential estimation system according to claim 1, wherein the one or more processors are further configured to execute the instructions to:
control a display device to display a (Kojima para 124 “The real-time balance sheet generating system 1 outputs and visualizes KPI that supports creating flow on monitor display…” The trend, which is growth estimate, is shown in fig. 12 as a part of the output.)
Kojima doesn’t teach displaying a reason.
However, Sokol teaches how to display a reason. (Sokol fig. 3 below shows explanations for financial results displayed to a user.)
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The claims, Reza, Kojima and Sokol all calculate economic results for user interpretation. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to give reasons to the user “for improved transparency of black-box systems using the example of contrastive explanations – a state-of-the-art approach to Interpretable Machine Learning.” Sokol abstract.
Reza teaches claim 3. The growth potential estimation system according to claim 2, wherein
the transaction information includes at least one of capital, sales, net profit, and a transaction duration or a transaction start timing with the intended company, regarding a transaction company that performs a transaction with the intended company. (Reza sec. II(B) “Total Contract Value of opportunities and won opportunities was computed…” Sales necessarily happen with another transaction company, because Reza’s data set is a “real sales dataset of a B2B consulting firm.” Reza abs.)
Kojima teaches claim 4. The growth potential estimation system according to claim 2, wherein
the transaction information includes at least one of a transaction amount, a number of transactions, and a transaction product with a transaction company that performs a transaction with the intended company. (Kojima fig. 12 Balance sheet B/S, shows trade receivables which is a transaction amount and a transaction product with a transaction company.)
Kojima teaches claim 5. The growth potential estimation system according to claim 2, wherein
the account time-series information includes at least one of a balance of an account of the intended company, an amount of money deposited in the account, and an amount of money withdrawn from the account. (Kojima fig. 12 P/L shows revenue which is an amount of money deposited into an account.)
Kojima teaches claim 6. The growth potential estimation system according to claim 2, wherein
the intended company attribute information includes at least one of capital, sales, and net profit of the intended company. (Kojima fig. 12 shows operating profit which is net profit.)
Kojima teaches claim 7. The growth potential estimation system according to claim 2, wherein the one or more processors are further configured to execute the instructions to:
generate a graph representing the transaction information. (Kojima para 47 “FIG. 9 is a graph showing an example of a change in balance sheet quality (BSQ)…”)
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Kojima teaches claim 8. The growth potential estimation system according to claim 7, wherein
the graph includes a node representing a company including the intended company (Kojima para 117 “FIG. 11 express the change in performance indicators for creating flow, that is, BSQ, … of four manufacturing Companies (a, b, c, d) that belongs to the same type of industry.”)
Kojima doesn’t teach an edge that represents a transaction between two companies.
However, Steier teaches an edge representing the company-to-company transaction relation. (Steier teaches Steier para 115 “The edges of the graph in FIG. 12 are derived from the transaction data from XYZ Company's financial accounting system, over a given time period. An edge between two account nodes is created if the two accounts appear in the same transaction.” Some of the accounts are companies, e.g. “postage” “Office Supplies” “mortgage payable”.)
The claims, Reza, Kojima and Steier teach modeling economic transactions. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to model the graph with edges representing transactions because “[f]low analysis methods are also used to reveal useful business information found in money flow graphs of financial data.” Steier abs.
Reza teaches claim 9. The growth potential estimation system according to claim 7, wherein the one or more processors are further configured to execute the instructions to:
generate the estimation model based on, the transaction information, the account time-series information, and the intended company attribute information in the first period, and the growth potential of the intended company after the first period. (Reza Fig. 2 below shows the account information for a first period, “WonValueMean”. The growth potential from the first period is the “WinRate”. The growth potential after the second period is the estimated “likelihood of winning new sales opportunities”. Reza abs. Reza fig. 3 shows that CRM sales/win data “Enhane [sic] with Engineered Features” is plugged into the prediction engine, “ML Pipeline: Trains various classification models on closed opportunities data after an extensive data cleaning and feature engineering. (B) Prediction Pipeline: Uses the optimal ML model to make predictions on new opportunities as well as inferring optimal decision boundaries.” Training a model is generating a model.)
Conclusion
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/AUSTIN HICKS/ Primary Examiner, Art Unit 2142