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 .
Introduction
The following is a non-final Office Action in response to Applicant’s submission filed on July 24, 2024.
Currently claims 1-9 are pending, claims 1, 4 and 7 are independent.
Priority
Applicant claims the priority of a foreign application No. JP 2023-125994 filed on 08/02/2023. However, a certified copy of the foreign application is not found.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/24/2024 appears to be in compliance with the previsions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Title
The title of the invention is objected because it is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Examiner suggests “A System and Method for Generating Visualized Sales Information” as an example.
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 an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-3 are directed to a non-transitory computer-readable storage medium storing computer instructions, which falls within the statutory category of a product; claims 4-6.are directed to a method for generating visualized information without tied to a particular machine for performing the steps, which falls outside of the four statutory categories. However, claims 4-6 will be included in Step 2 Analysis for the purpose of compact prosecution. Claims 7-9 are directed to a device comprising a memory and a processor, which falls within the statutory category of a machine.
With respect to claims 4-6, the claims are directed to non-statutory subject matter because the claims are directed to a method without tied to a particular machine in the body of the claims for performing the steps. One factor to consider when determining whether a claim recites a §101 patent eligible process is to determine if the claimed process (1) is tied to a particular machine or; (2) transforms a particular article to a different state or thing. See In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008) (en banc) aff’d, Bilski v. Kappos, 561 U.S. ___, 130 S.Ct. 3218, 95 USPQ2d 1001 (U.S. 2010). (Machine-or-Transformation Test).
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking the method as representative, claim 4 recites the limitations of “setting a weight to each combination of product categories, generating visualized information that visualizes and indicates a behavior history of a user, combining basis visualized information that visualizes and indicates a behavior history of the user”, and dependent claims 4-6 further describe the attributes and narrow the limitations of claim 4 by “simulating a movement of an agent that corresponds to the user in the store”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to methods, that allow user to manage commercial interaction (including marketing or sales activities) and manage personal behavior or relationships or interactions between people, which are certain methods of organizing human activity. The Specification supports this view, for example: the interaction of the users in the store ST1 includes waiting for a cash register, waiting, congestion, avoiding passing, or the like…the waiting and the congestion occur at an extremely limited time within business hours” (see ¶ 82). Thus, the claims fall within the certain methods of organizing human activity grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, claim 4 recites no additional element for performing the steps, when given the broadest reasonable interpretation, a machine is not required in the claim. Even if claim 4 recites the additional elements of “a memory” and “a processor” as recited in claim 7 for performing the steps. The specification discloses these additional elements at a high level of generality and merely invoked as tools to perform the generic computer functions including receive interaction data over a network. For example, the specification discloses “a computer 200 includes a CPU 201 that executes various type of arithmetic processing, an input device 202 that receives a data input, a monitor 203, and a speaker 204…the computer 200 also includes a random access memory (RAM) 208 that temporarily stores various types of information, and a hard disk device 209.” (See ¶ 90). Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, or reflect any improvement to the functioning of a computer itself or another technology. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, claim 4 recites no additional element for performing the steps, when given the broadest reasonable interpretation, a machine is not required in the claim. Even if claim 4 recites the additional elements of “a memory” and “a processor” as recited in claim 7 for performing the steps. The specification discloses these additional elements at a high level of generality and merely invoked as tools to perform the generic computer functions including receive interaction data over a network. However, generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 4-6 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims 1-3 and 7-9 parallel claims 4-6—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
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 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.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Eppley et al., (US 2019/0213607, hereinafter: Eppley), and in view of Geng, (CN 109426974), and further in view of Puglis et al., (JP 2004534984; hereinafter: Puglis).
(Non-US patent references are cited by page number on the document unless they don’t have page numbers, then cited by PDF page number)
Regarding claim 1, Eppley discloses the a non-transitory computer-readable recording medium (see ¶ 10) storing a generation program for causing a computer to execute processing comprising:
generating visualized information that visualizes and indicates a behavior history of a user that corresponds to the sales information, by combining basis visualized information that visualizes and indicates a behavior history of the user in a store, for each of a plurality of purchase patterns that has different combinations of the product categories of the purchased products, stored in a memory, based on the set weight (see Abstract; Fig. 12; ¶ 9-10, ¶ 16, ¶ 59-63, ¶ 67, ¶ 72-73, ¶ 78).
Eppley discloses ranked the product categories based on the impressions (see Fig. 12); and combining the POS transaction data contains information regarding each customer transaction, which items were purchased (see ¶ 67).
Eppley does not explicitly disclose setting a weight to each combination of product categories; however, Geng in an analogous art for competitive product analysis discloses
setting a weight to each combination of product categories of purchased products, based on sales information of a product (see pg. 11, ¶ 6-8); and
combining basis visualized information that visualizes and indicates a behavior history of the user in a store (see pg. 9, ¶ 4; pg. 13, ¶ 2-8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Eppley to include teaching of Geng in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Eppley discloses obtaining the POS transaction data based on purchased items (see ¶ 9, ¶ 95).
Eppley and Geng do not explicitly disclose a history of a user corresponding to the sales information; however, Puglis in an analogous art for sales information analysis discloses
a history of a user corresponding to the sales information (see ¶ 60: items 11, 13; ¶ 68; ¶ 126, ¶ 141).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Eppley and in view of Geng to include teaching of Puglis in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Eppley discloses generating a best case route presumably taken by each customer based on a sequence of travel nodes based on their relevant transaction receipt (see ¶ 78, claim 9).
Eppley and Geng do not explicitly disclose the following limitations; however, Puglis discloses the non-transitory computer-readable recording medium according to claim 1, for causing a computer to execute processing further comprising:
generating the basis visualized information by simulating a movement of an agent that corresponds to the user in the store, for each of the plurality of purchase patterns, based on store information that indicates arrangement in the store for each product category (see ¶ 12, ¶ 24, ¶ 311).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Eppley and in view of Geng to include teaching of Puglis in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3, Eppley discloses the non-transitory computer-readable recording medium according to claim 1, wherein the processing of generating the visualized information combines the basis visualized information based on the weight for each time band set based on the sales information for each time band and generates the visualized information for each time band (see Abstract; Fig. 12; ¶ 9, ¶ 16, ¶ 31, ¶ 61-63, ¶ 72-73, ¶ 91).
Regarding claim 4, Eppley discloses a generation method comprising:
generating visualized information that visualizes and indicates a behavior history of a user that corresponds to the sales information by combining basis visualized information that visualizes and indicates a behavior history of the user in a store, for each of a plurality of purchase patterns that has different combinations of the product categories of the purchased products, stored in a memory, based on the set weight (see Abstract; Fig. 12; ¶ 9-10, ¶ 16, ¶ 59-63, ¶ 67, ¶ 72-73, ¶ 78).
Eppley discloses ranked the product categories based on the impressions (see Fig. 12); and combining the POS transaction data contains information regarding each customer transaction, which items were purchased (see ¶ 67).
Eppley does not explicitly disclose setting a weight to each combination of product categories; however, Geng in an analogous art for competitive product analysis discloses
setting a weight to each combination of product categories of purchased products, based on sales information of a product (see pg. 11, ¶ 6-8); and
combining basis visualized information that visualizes and indicates a behavior history of the user in a store (see pg. 9, ¶ 4; pg. 13, ¶ 2-8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Eppley to include teaching of Geng in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Eppley discloses obtaining the POS transaction data based on purchased items (see ¶ 9, ¶ 95).
Eppley and Geng do not explicitly disclose a history of a user corresponding to the sales information; however, Puglis in an analogous art for sales information analysis discloses
a history of a user corresponding to the sales information (see ¶ 60: items 11, 13; ¶ 68; ¶ 126, ¶ 141).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Eppley and in view of Geng to include teaching of Puglis in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 5, Eppley discloses generating a best case route presumably taken by each customer based on a sequence of travel nodes based on their relevant transaction receipt (see ¶ 78, claim 9).
Eppley and Geng do not explicitly disclose the following limitations; however, Puglis discloses the generation method according to claim 4, for causing a computer to execute processing further comprising:
generating the basis visualized information by simulating a movement of an agent that corresponds to the user in the store, for each of the plurality of purchase patterns, based on store information that indicates arrangement in the store for each product category (see ¶ 12, ¶ 24, ¶ 311).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Eppley and in view of Geng to include teaching of Puglis in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6, Eppley discloses the generation method according to claim 4, wherein the processing of generating the visualized information combines the basis visualized information based on the weight for each time band set based on the sales information for each time band and generates the visualized information for each time band (see Abstract; Fig. 12; ¶ 9, ¶ 16, ¶ 31, ¶ 61-63, ¶ 72-73, ¶ 91).
Regarding claim 7, Eppley discloses an information processing device comprising:
a memory (see Fig. 1, ¶ 10); and
a processor coupled to the memory (see ¶ Fig. 1, ¶ 10) and configured to:
generate visualized information that visualizes and indicates a behavior history of a user that corresponds to the sales information by combining basis visualized information that visualizes and indicates a behavior history of the user in a store, for each of a plurality of purchase patterns that has different combinations of the product categories of the purchased products, stored in the memory, based on the set weight (see Abstract; Fig. 12; ¶ 9-10, ¶ 16, ¶ 59-63, ¶ 67, ¶ 72-73, ¶ 78).
Eppley discloses ranked the product categories based on the impressions (see Fig. 12); and combining the POS transaction data contains information regarding each customer transaction, which items were purchased (see ¶ 67).
Eppley does not explicitly disclose setting a weight to each combination of product categories; however, Geng in an analogous art for competitive product analysis discloses
set a weight to each combination of product categories of purchased products, based on sales information of a product (see pg. 11, ¶ 6-8); and
combining basis visualized information that visualizes and indicates a behavior history of the user in a store (see pg. 9, ¶ 4; pg. 13, ¶ 2-8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Eppley to include teaching of Geng in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Eppley discloses obtaining the POS transaction data based on purchased items (see ¶ 9, ¶ 95).
Eppley and Geng do not explicitly disclose a history of a user corresponding to the sales information; however, Puglis in an analogous art for sales information analysis discloses
a history of a user corresponding to the sales information (see ¶ 60: items 11, 13; ¶ 68; ¶ 126, ¶ 141).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Eppley and in view of Geng to include teaching of Puglis in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8, Eppley discloses generating a best case route presumably taken by each customer based on a sequence of travel nodes based on their relevant transaction receipt (see ¶ 78, claim 9).
Eppley and Geng do not explicitly disclose the following limitations; however, Puglis discloses the information processing device according to claim 7, wherein the processor generates the basis visualized information by simulating a movement of an agent that corresponds to the user in the store, for each of the plurality of purchase patterns, based on store information that indicates arrangement in the store for each product category.
Regarding claim 9, Eppley discloses the information processing device according to claim 7, wherein a process to generate the visualized information combines the basis visualized information based on the weight for each time band set based on the sales information for each time band and generates the visualized information for each time band (see ¶ 12, ¶ 24, ¶ 311).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Maezawa et al., (JP 4859892 B2) discloses a method for determining a product category into a classified based on the feature word extracted by the feature word extracting unit and a related word registered in the category classification database.
Van Der Riet, (US 2007/0260521) discloses a method for managing personalized customer relationships based on historical purchasing preferences and habits of each customer.
Murakami et al., (JP 6427640 B2) disclose a method for simulating activity of a user in the behavior of an agent in a numerical model space related to a facility.
Kim et al., (WO 2022075314 A1) discloses a method for predicting an order rate of products associated with sales activities tends using a machine learning model.
Smith “Consumer Behavior and Analytics”, Taylor & Francis Group, London and New York, Published 2020 by Routledge.
Sharma et al., “Data Visualization and its impact in decision making in business”, Research Paper 2023/03/31. Deerwalk Institute of Technology, Tribuhuvan University, Kathmandu, Nepal.
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/PAN G CHOY/Primary Examiner, Art Unit 3624