DETAILED ACTION
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 .
Status of Claims
This non-final office action is responsive to Applicant’s submission filed 02/27/2025. Currently, claims 1-9 are pending. Claims 1-8 have been amended. Claim 9 is newly added.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., abstract idea) without significantly more.
The claims recite method, system and computer program product for identifying a payment transaction using a user intention model.
Exemplary claim 1 recites in part,
“estimating, based on history information, a purchase purpose of a purchase of a product, product information, and date and time information of the purchase of the product…;
generating learning data from the history information and the purchase purpose of respective customers of a plurality of customers…;
extracting, based on the purchase purpose in the learning data, by clustering the learning data, a plurality of product groups associated with the respective customers…; and
extracting, based on similarity among the plurality of product groups associated with the respective customers, a customer group…”
The above limitations describe the steps of, 1) determining product purchase attributes from purchase history information, 2) generating learning data based on purchase history information and one or more purchase attribute (purchase purpose), 3) extracting product group clusters based on purchase purpose from the learning data, and 4) extracting similarity amongst the product group clusters.
The above steps describe the process of extracting product groupings from purchase history information. The above limitations, under their broadest reasonable interpretation, encompass "Certain Methods of Organizing Human Activity" (commercial or legal interactions – sales activities or behaviors and business relations) enumerated in MPEP 2106.04(a)(2)(II)(B). If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions – sales activities or behaviors and business relations, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. The cited claim recites additional elements in the form of a computing device (processor) to perform the limitations encompassing the abstract idea identified above. The computing device (processor) represents using a computer as a tool to perform the judicial exception as in MPEP 2106.05(f).
When considered both individually and as a whole, the additional elements do not integrate the abstract idea into a practical application.
The recitation of additional elements is acknowledged as identified above. The discussion with respect to practical application is equally applicable to consideration of whether the additional elements amount to significantly more. The computing device (processor) represents using a computer as a tool to perform the judicial exception as in MPEP 2106.05(f).
Therefore, there are no meaningful recitations, considered in combination, that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
Accordingly, claim 1 is directed to a judicial exception (i.e., abstract idea) without significantly more.
Claims 7 and 8 recite similar limitations as set forth in claim 1, and therefore are rejected based on similar rationale.
Dependent claims 2-6 and 9 recite limitations directed to the abstract idea, and do not integrate the abstract idea into a practical application nor amount to significantly more.
For example, claims 2-6 describe classifying (sorting) the purchase history information into groups, and claims 6 and 9 describe presenting data. These steps represent sorting/filtering and displaying data, which amounts to insignificant extra-solution activities that do not impose meaningful limits on the abstract idea. See MPEP 2106.05(g)
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.
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.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over JP-2014-109898 A (Ryuichi et al. – hereinafter Ryuichi) in view of U.S. Patent Appl. Pub. No. 2020/0193501 (Kumar et al. – hereinafter Kumar), and further in view of U.S. Patent Appl. Pub. No. 2019/0205905 (Raghunathan et al. – hereinafter Raghunathan).
Referring to claim 1, Ryuichi discloses a product group and customer group extraction device comprising a processor configured to execute operations comprising:
extracting, based on the purchase purpose in the learning data, by clustering the learning data, a plurality of product groups associated with the respective customers, wherein the plurality of product groups comprises a product group, and the product group comprises the product purchased by the customer; and [See page 3, line 21-page 4, line 11; page 4, line 41-page 5, line 16; page 6, line 20-page 7, line 40; page 9, line 33-page 11, line 11]
extracting, based on similarity among the plurality of product groups associated with the respective customers, a customer group, wherein the customer group comprises the customer. [See page 3, line 21-page 4, line 11; page 4, line 41-page 5, line 16; page 6, line 20-page 7, line 40; page 9, line 33-page 11, line 11]
Ryuichi does not explicitly disclose the limitations:
estimating, based on history information, a purchase purpose of a purchase of a product, product information, and date and time information of the purchase of the product, wherein the purchase purpose is of which a customer has purchased the product, the history information comprises customer information of the customer, the product information is of the product purchased by the customer; and
generating learning data from the history information and the purchase purpose of respective customers of a plurality of customers, wherein the respective customers comprise the customer.
Kumar teaches a device with the limitation: estimating, based on history information, a purchase purpose of a purchase of a product, product information, and date and time information of the purchase of the product, wherein the purchase purpose is of which a customer has purchased the product, the history information comprises customer information of the customer, the product information is of the product purchased by the customer. [See paragraphs 0021, 0039]
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified the system executing the method of Ryuichi to have incorporated an estimation feature as in Kumar with the motivation of determining user behavior associated with one or more products. [See Kumar paragraphs 0009-0013; Ryuichi page 2, line 9-page 3, line 1]
Raghunathan teaches a device with the limitation: generating learning data from the history information and the purchase purpose of respective customers of a plurality of customers, wherein the respective customers comprise the customer. [See paragraphs 0069, 0071, 0097-0099, 0122]
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified the system executing the method of the combined teachings of Ryuichi and Kumar to have incorporated a machine learning feature as in Raghunathan with the motivation of determining user behavior associated with one or more products. [See Raghunathan paragraphs 0052, 0069, 0075, 0097; Ryuichi page 2, line 9-page 3, line 1]
Referring to claim 2, the combination of Ryuichi, Kumar and Raghunathan discloses the product group and customer group extraction device according to claim 1, wherein the estimating further comprises estimating a category of the product as a part of the product information of the product indicates the purchase purpose. [See Kumar paragraphs 0014, 0021, 0039]
Referring to claim 3, the combination of Ryuichi, Kumar and Raghunathan discloses the product group and customer group extraction device according to claim 1, wherein the estimating further comprises classifying a purchase time in the date and time information into a time period, and estimating the time period as a part of the purchase purpose. [See Kumar paragraphs 0031, 0034, 0052; Ryuichi page 3, line 21-page 4, line 45; page 8, line 6-page 10, line 34]
Referring to claim 4, the combination of Ryuichi, Kumar and Raghunathan discloses the product group and customer group extraction device according to claim 3, wherein the estimating further comprises updating a segment of each time period for a respective piece of the customer information. [See Kumar paragraphs 0031, 0034, 0052; Ryuichi page 3, line 21-page 4, line 45; page 8, line 6-page 10, line 34]
Referring to claim 5, the combination of Ryuichi, Kumar and Raghunathan discloses the product group and customer group extraction device according to claim 1, wherein the estimating further comprises classifying a purchase date and time in the date and time information into a season, and estimating the classified season as a part of the purchase purpose. [See Kumar paragraphs 0031, 0034, 0052; Ryuichi page 3, line 21-page 4, line 45; page 8, line 6-page 10, line 34]
Referring to claim 6, the combination of Ryuichi, Kumar and Raghunathan discloses the product group and customer group extraction device according to claim 1, the processor further configured to execute operations comprising: displaying, on a display, a number of store visitors per day for receiving a product in the product group in exchange for a resource, a number of receiving resources received per day in exchange for the product the product group, distribution of the respective customers in the customer group relative to the product group, and distribution of a plurality of products in the product group, wherein the plurality of products comprises the product. [See Raghunathan paragraphs 0059, 0061, 0065, 0066, 0087, 0088-0090, 0104, 0105]
Referring to claim 7, it recites similar limitations as set forth in claim 1, and therefore is rejected based on similar rationale.
Referring to claim 8, it recites similar limitations as set forth in claim 1, and therefore is rejected based on similar rationale.
Referring to claim 9, the combination of Ryuichi, Kumar and Raghunathan discloses the product group and customer group extraction device according to claim 6, wherein the displaying further comprises graphically presenting the number of store visitors per day through a graphical user interface. [See Raghunathan paragraphs 0087, 0088, 0104, 0105]
Conclusion
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/OLUSEGUN GOYEA/ Primary Examiner, Art Unit 3627
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