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 .
DETAILED ACTION
Status of Claims
The following is a Final Office Action in response to Applicant’s amendment received 03/12/2026.
In accordance with Applicant’s amendment, claims 1, 9, and 17 are amended. Claims 1-20 are currently pending.
Response to Amendment
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
The amended title filed on 03/12/2026 has been entered and the objection to the Title/Specification is withdrawn in response.
The 35 U.S.C. §102(a)(1) rejection of claims 1-20 is withdrawn in response to the amendment, however a new ground of rejection is applied to these claims under §103 in the instant office action.
Response to Arguments
Response to §101 arguments – Applicant’s arguments (Remarks at pgs. 12-14) with respect to the §101 rejection of claims 1-20 have been considered, but are not persuasive.
In response to applicant’s argument that “the amended claims overcome this rejection by clearly defining a specific technical solution to a technical problem inherent in retail analytics” (Remarks at pg. 12), citing the alleged technical problem of “the difficulty and high cost associated with acquiring customer movement trajectories in a store” and the alleged “specific technical solution” to infer customer movement by “leveraging more readily available, less granular data” (Remarks at pg. 13).
In response, the Examiner first emphasizes that retail analytics and acquiring customer movement trajectories in a store is not reasonably considered as a technical problem, but instead is a business-centric problem addressed plainly rooted in marketing and customer behavior analysis. None of the acquiring, determining, or calculating steps invoke any technical elements, but instead fall within the scope of the abstract idea itself, which at most is implemented with generic computing elements (as discussed in the Step 2 analysis in the §101 rejection), and any benefit from such generic computer implementation is merely the result of using a generic computer as a tool to perform the step(s) rather than the sequence of steps/activities recited in the method itself and does not materially alter the patent eligibility of the claim. See Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”) (cited in the Federal Circuit's FairWarning decision). Therefore, the additional elements in the form of generic computer implementation to perform the acquiring, acquiring, determining, or calculating merely serves to tie the abstract idea to a particular operating environment, which neither renders the problem/solution as technical nor transforms the abstract idea into eligible subject matter. See, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See also, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
In response to applicant’s suggestion that the claims amount to a “specific technical solution” (Remarks at pg. 13), the Examiner notes that the claims have not been shown to yield any technical result, technical improvement, or any even a modification to any technical elements (software or hardware), but instead yield the result in the form of “calculating an evaluation value for evaluation of how much the product is considered to have come into sight of the plurality of customers based on the first information and the estimation region,” which falls under the scope of the abstract idea itself (e.g., mental evaluation with the aid of pen and paper), devoid of any technical improvement or any nexus whatsoever to a technical result. Even if the “calculating” could not be performed mentally (which is not conceded), this step would, at most, amount to a mathematical calculation and thus fall under the “Mathematical Concepts” abstract idea grouping, however “Adding one abstract idea (math) to another abstract idea” (fundamental economic practice) “does not render the claim non-abstract.” See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1326-27, 122 USPQ2d 1377, 1379-80 (Fed. Cir. 2017) (claim reciting multiple abstract ideas, i.e., the manipulation of information through a series of mental steps and a mathematical calculation, was held directed to an abstract idea and thus subjected to further analysis in part two of the Alice/Mayo test).
Applicant’s remarks (Remarks at pg. 14) concerning the USPTO Memorandum published Aug. 4, 2025 have been reviewed, but are not deemed persuasive at showing the claims are eligible. As plainly noted in the memorandum, it “is not intended to announce any new USPTO practice or procedure and is meant to be consistent with existing USPTO guidance.”
Therefore, the Examiner maintains that the §101 rejection applied to claims 1-20 strictly adheres to the step-by-step guidance set forth in MPEP 2106, including factual findings and clearly articulated identification and analysis under Step 1, Step 2A1, Step 2A2, and Step 2B of the eligibility inquiry, including a step-by-step explanation of the reasons why certain limitations are interpreted as setting forth activity falling under one or more of the abstract idea groupings, followed by explicit identification and analysis of the additional elements to support the conclusion that the additional elements, considered both individually and as an ordered combination, are insufficient to integrate the abstract idea into a practical application or add significantly more. Applicant has not effectively rebutted or even discussed these findings or the rationale supporting them. Notably, applicant’s arguments fail to discuss or specifically point out any supposed errors in the findings set forth in the Step 2A Prong One analysis that provides specific reasons why each of the identified limitations is interpreted as setting forth or describing activity falling under one or more of the abstract idea groupings, nor does applicant identify which additional elements yield the alleged “specific technical solution.” Moreover, the Examiner notes the additional elements in applicant’s claims are limited to generic computing elements, which have not been shown to be modified or improved upon in any manner, but instead are merely invoked in a manner similar to adding the words “apply it” (or an equivalent), which at most serves to link the use of the judicial exception to a particular technological environment (generic computing environment), which is not enough to integrate the abstract idea into a practical application or add significantly more. See MPEP 2106.05(f) and 2106.05(h). See also, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015
For the reasons above along with the reasons below in the updated §101 rejection, the amendments and supporting arguments are not sufficient to overcome the §101 rejection.
Response to §102(a)(1) arguments – Applicant’s arguments (Remarks at pgs. 14-15) with respect to the §102 rejection of claims 1-20 have been considered, but are primarily raised in support of the amendment to independent claims 1/9/17 and are therefore believed to be fully addressed below in the new ground of rejection set forth below under §103.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the subject matter eligibility guidance set forth in MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106.03), it is first noted that the claimed non-transitory computer-readable recording medium (claims 1-8), method (claims 9-16), and device (claims 17-20) are each directed to a potentially eligible category of subject matter (i.e., article of manufacture, process, and machine). Accordingly, claims 1-20 satisfy Step 1 of the eligibility inquiry.
With respect to Step 2A Prong One of the eligibility inquiry (as explained in MPEP 2106.04), it is next noted that the claims recite an abstract idea that falls under the “Certain methods of organizing human activity” abstract idea grouping by reciting limitations that describe activities considered commercial interactions (sales or marketing activity) pursuant to evaluating customer sighting of a product, and steps that, but for the generic computer implementation, may be implemented as “Mental Processes” (e.g., observation, evaluation, judgment, or opinion). The limitations reciting the abstract idea as set forth in independent claim 1 are identified in bold text below, whereas the additional elements are presented in plain text and are separately evaluated under Step 2A Prong Two and Step 2B:
acquiring first information that includes information related to arrangement of a plurality of products in a store (This step describes activity for gathering product arrangement information, which is considered sales and marketing activity, and furthermore this step, but for the generic computer implementation, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion and/or with the aid of pen and paper. In addition, the “acquiring” step may be considered insignificant extra-solution activity, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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));
acquiring second information that includes information that specifies one or a plurality of the products purchased by each of a plurality of customers, the second information being acquired without directly acquiring continuous movement trajectory data of the plurality of customers within the store (This step describes activity for gathering customer-product purchasing information, which is considered sales and marketing activity, and furthermore this step, but for the generic computer implementation, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion and/or with the aid of pen and paper. In addition, the “acquiring” step may be considered insignificant extra-solution activity, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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));
determining an estimation region in which each of the plurality of customers is estimated to have moved in the store based on an estimated purchase order of the plurality of products purchased by the customer and the first information and the second information, wherein the estimated purchase order is determined based on at least one of an entrance through which the customer has passed and a cash register used by the customer, and wherein the determination of the estimation region avoids the need for continuous movement trajectory data (This step describes sales and marketing activity since the determination is considered customer behavior intelligence, and furthermore this step, but for the generic computer implementation, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion and/or with the aid of pen and paper); and
calculating an evaluation value for evaluation of how much the product is considered to have come into sight of the plurality of customers based on the first information and the estimation region (This step describes sales and marketing activity since the calculated evaluation value is indicative of a customer’s visualization of a product, which is customer behavior intelligence, and furthermore this step, but for the generic computer implementation, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion and/or with the aid of pen and paper).
Independent claims 9 and 17 recite similar limitations as those set forth in claim 1 as discussed above, and have therefore been determined to recite the same abstract idea as claim 1.
With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP 2106.04(d)), the judicial exception is not integrated into a practical application. Independent claims 1, 9, and 17 recite the additional elements of a non-transitory computer-readable recording medium storing an information processing program, a computer, and an information processing device comprising a processor and a memory. The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g). In addition, these limitations, when considered individually or as an ordered combination, fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry (as explained in MPEP 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent claims 1, 9, and 17 recite the additional elements of a non-transitory computer-readable recording medium storing an information processing program, a computer, and an information processing device comprising a processor and a memory. These additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions/software to perform the abstract idea, which merely serves to tie the abstract idea to a particular technological environment (generic computing environment), similar to adding the words “apply it” (or an equivalent). Accordingly, the generic computer implementation merely serves to link the use of the judicial exception to a particular technological environment and therefore does not amount to significantly more than the abstract idea itself. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is well-understood, routine, and conventional activity and thus insufficient to add significantly more to the claims. 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-8, 10-16, and 18-20 recite the same abstract idea(s) as recited in the independent claims, and have been determined to recite further details/steps falling under the “Certain methods of organizing human activity” and/or “Mental Processes” abstract idea groupings discussed above along with the same generic computing elements recited in the independent claims which, merely serve the purpose of tying the invention to a particular technological environment and which, as discussed above, is insufficient to integrate the abstract idea into a practical application or add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1-20 are rejected under 35 U.S.C. §103 as unpatentable over Sorensen (US 2002/0178085) in view of Aihara et al. (US 2015/0363798, hereinafter “Aihara”).
Claims 1/9/17: As per claim 1, Sorensen teaches a non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing (pars. 6, 10, and 66: storage medium for analyzing shopper behavior of one or more shoppers within a shopping environment; storage medium typically is readable by a computer and has a program of instructions embodied therein that is executable by the computer) comprising:
acquiring first information that includes information related to arrangement of a plurality of products in a store (pars. 8-10, 37, 59-61, and Figs. 1, 5, and 8: e.g., shopping environment having one or more products placed at predetermined locations therein; providing a shopping environment including products placed at predetermined locations in the shopping environment; determining the position of a product within a shopping environment, at step 302. Typically, the positions of each product are recorded, in three dimensions, by a scanning device that is operated by a user traversing the shopping environment; product positions may be recorded in only two-dimensions, or may be recorded in three dimensions in another manner, for example, by using a tape measure to record a height of each product. A record of these product positions is made, and the predefined regions are defined around certain products or groups of products; Data analyzer 16 is configured to recognize a predefined region 42 relative to each product);
acquiring second information that includes information that specifies one or a plurality of the products purchased by each of a plurality of customers (pars. 10, 29, 46, 52, 54, and Figs. 8 and 11: e.g., determining a shopping behavior of a shopper within the predefined region, wherein the shopping behavior is selected from the group consisting of … the shopper purchasing a product within the predefined region; data analyzer may also be configured to detect a destination pattern 64, in which the shopper path enters the shopping environment, travels to a region from which one or more products are purchased; behaviors 108 include a wide variety of behaviors, including the shopper being physically present within the predefined region (VISIT OR PASS) 118, the shopper slowing down or stopping within the predefined region (PAUSE OR SHOP) 120, the shopper purchasing a product within the predefined region; determine whether a product was purchased within the predefined region as indicated at 122, the data analyzer 16 is typically configured to examine the purchase data 24 (i.e., record 204) associated with the shopping path 38, in order to detect whether any products from the predefined region were purchased by the shopper);
determining an estimation region in which each of the plurality of customers is estimated to have moved in the store based on an estimated purchase order of the plurality of products purchased by the customer and the first information and the second information (pars. 46, 52, 54-55, and Figs. 1-8 and 11: data analyzer may also be configured to detect a destination pattern 64, in which the shopper path enters the shopping environment, travels to a region from which one or more products are purchased, and then travels to POS terminal; data analyzer 16 is typically configured to examine the purchase data 24 (i.e., record 204) associated with the shopping path 38, in order to detect whether any products from the predefined region [i.e., estimation region] were purchased by the shopper; data compilation 100 typically further includes a first pause or shop measure 120a, a first purchase measure 122a, and an order of purchase measure 122b. The first pause or shop measure is typically a measure of the number or percentage of shoppers who first slow down or stop in a particular predefined region. For example, 8% of shopper may first slow down during their shopping trips in the predefined region [i.e., estimation region] surrounding the beer cooler; Order of purchase is a measure indicating the relative order of a given purchase on a shopping trip. Typically the order of purchase measure is expressed on a scale of 1 to 10, such that a score of 2 indicates that a particular product or products from a particular zone are on average purchased earlier in a shopping trip; behaviors 108 include a wide variety of behaviors, including the shopper being physically present within the predefined region (VISIT OR PASS) 118, the shopper slowing down or stopping within the predefined region (PAUSE OR SHOP) 120, the shopper purchasing a product within the predefined region) wherein the estimated purchase order is determined based on [information] and wherein the determination of the estimation region avoids the need for continuous movement trajectory data (pars. 46, 52, 54-55, and Figs. 1-8 and 11: describing the use of a percentage measure with respect to a predefined region, i.e., estimation region, indicative of order of purchase of a shopper, which uses a statistical measure, i.e., percentage of shoppers exhibiting certain behavior, to determine “order of purchase” and which avoids the need for continuous movement trajectory data by using this statistical estimate/percentage or even a score to infer the order of purchase, as discussed in par. 55 – e.g., data compilation 100 typically further includes a first pause or shop measure 120a, a first purchase measure 122a, and an order of purchase measure 122b. The first pause or shop measure is typically a measure of the number or percentage of shoppers who first slow down or stop in a particular predefined region. For example, 8% of shopper may first slow down during their shopping trips in the predefined region [i.e., estimation region] surrounding the beer cooler; report may indicate that 6% of shoppers first purchased beer on their shopping trips to the shopping environment; Order of purchase is a measure indicating the relative order of a given purchase on a shopping trip. Typically the order of purchase measure is expressed on a scale of 1 to 10, such that a score of 2 indicates that a particular product or products from a particular zone are on average purchased earlier in a shopping trip).; and
calculating an evaluation value for evaluation of how much the product is considered to have come into sight of the plurality of customers based on the first information and the estimation region (pars. 8, 39, 41, 50, 62-63, and Figs. 3, 8, and 9: Product-shopper visibility measure 44 [i.e., evaluation value] is a measure of how long a product is visible to a shopper as a shopper travels along shopper path 38. The visibility measure is calculated using lines of sight; calculating a product-shopper visibility measure based in part on a simulated visibility of the product from a field of view or line of sight of the shopper, as the shopper travels along the shopping path. From this measure, it may be estimated how long a particular product was visible to a shopper. The field of view may be of varying scope and typically faces parallel to the velocity vector of the shopper traveling along the shopping path; generating a report that includes the product-shopper proximity measure and/or the product-shopper visibility measure [wherein Fig. 8 shows visibility measure, proximity measure, etc. based on zone/product information and path measurement information]).
Sorensen does not teach:
the second information being acquired without directly acquiring continuous movement trajectory data of the plurality of customers within the store;
…determined based on at least one of an entrance through which the customer has passed and a cash register used by the customer.
Aihara teaches:
the second information being acquired without directly acquiring continuous movement trajectory data of the plurality of customers within the store (pars. 2, 7, and 37: technique that estimates purchase behavior of a customer. In particular, the present invention relates to a technique that estimates a traffic line of a customer in a store or across stores; estimated purchase order, and estimate the traffic line of the target customer in the store);
…determined based on at least one of an entrance through which the customer has passed and a cash register used by the customer (pars. 179, 187, 245, 247: sensor is attached to the entrance/exit so as to determine that the target customer enters the store; computer system (101) estimates that the target customer (461) enters the store from an entrance/exit 2, travels in the order of the shelf with the article 1, the shelf with the article 5, the shelf with the article 1, the shelf with the article 2, the shelf with the article 3, the shelf with the article 4, the shelf with the article 2, the shelf with the article 6, and the shelf with the article 7 and then travels to an article receiving place; computer system (101) estimates that the target customer (511) enters the store from the entrance/exit; computer system (101) estimates that the article is purchased (taken into a cart) at a position where the customer travels, or optionally checks the article).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Sorensen with Aihara because the references are analogous since they are each directed to computer implemented features for analyzing customer shopping/purchase behavior, which is within Applicant’s field of endeavor of evaluating visibility of products to a customer, and because modifying Sorensen with Aihara’s features for acquiring second information being acquired without directly acquiring continuous movement trajectory data of the plurality of customers within the store and modifying Sorensen’s determination such that it is made based on an entrance through which a customer passed, as claimed, would provide the expected benefit of gleaning marketing intelligence by accurately estimating customer traffic lines (Aihara at par. 57), which would serve Sorensen’s motivation to obtain accurate marketing information concerning a customer’s shopping habits (Sorensen at par. 3); and further obvious because the claimed invention is merely a combination of old elements, and in the combination 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.
Claims 9 and 17 are directed to an information processing method and an information processing device comprising memory and a processor for performing substantially similar limitations as those recited in claim 1 and addressed above. Sorensen, in view of Aihara, teaches an information processing method and an information processing device comprising memory and a processor for performing the limitations discussed above (Sorensen at pars. 6, 10, and 66: system, method, data compilation, and storage medium for analyzing shopper behavior; storage medium typically is readable by a computer and has a program of instructions embodied therein that is executable by the computer to perform the steps; See also, Aihara at pars. 7 and 38: method of estimating purchase behavior; computer-system-readable recording media), and claims 9/17 are therefore rejected using the same reference and for substantially the same reasons as set forth above.
Claims 2/10/18: Sorensen further teaches:
the first information further includes information related to arrangement of one or a plurality of cash registers in the store or information related to arrangement of one or a plurality of entrances in the store or a combination of both the pieces of information (pars. 27, 33, 48, and Figs. 1, 6-7, and 11: e.g., Point-of-sale terminal 14a typically includes a scanner and cash register [wherein Fig. 1 shows POS terminal 14a, and Fig. 11 shows Checkout Counter number]; Data compilation 100 includes various statistics…data compilation may arranged according to zones or subzones…the zones may correspond to a physical portion of shopping environment 20, such as the entrance/exit; Shopping environment 20 also typically includes an entrance/exit 20e [shown in Figs. 6-7]),
the second information further includes information related to a cash register used by each of the plurality of customers among the cash registers or information related to an entrance through which each of the plurality of customers has passed or is estimated to have passed among the entrances or a combination of both the pieces of information (pars. 29, 33-35, 46, and Fig. 11: shopper path is determined to have started when motion of a particular transmitter is detected near a cart return area near the entrance of the shopping environment; identify and record purchased products 40a, thereby generating a purchase record 204 (shown in FIG. 11) for each shopper. Point-of-sale terminal 14a typically includes a scanner and cash register; purchase records are sent as purchase data 24, via purchase record computer 14b, to data analyzer 16; As shown in FIG. 11, each purchase record 204 of purchase data 24 typically includes a list of items 204a along with a date and a time 204b of checkout, as well as a POS terminal identifier 204c; purchase record computer 14b. POS terminal 14a is configured to identify and record purchased products 40a, thereby generating a purchase record 204 (shown in FIG. 11) for each shopper [wherein Fig. 11 shows the checkout counter, i.e. cash register, used by the customer]; data analyzer may also be configured to detect a destination pattern 64, in which the shopper path enters the shopping environment, travels to a region from which one or more products are purchased, and then travels to POS terminal), and
the computer is caused to further execute processing including:
estimating, for each of the plurality of customers, purchase order of a plurality of the products purchased by the customer based on the first information and the second information (pars. 34-36, 55, and Fig. 8: detect a transmitter/cart identifier and checkout time associated with a particular purchase record, in order to link the shopper path and purchase record; data analyzer may also be configured to impute or predict a path in the same or a different shopping environment for a particular shopper based at least in part on the historic shopper path data from prior shopping trips linked to the frequent shopping or discount card; measure indicating the relative order of a given purchase on a shopping trip); and
determining the estimation region based on the first information, the second information, and the estimated purchase order (par. 54 and Figs. 8 and 10-11: analyzer 16 is typically configured to examine the purchase data 24 (i.e., record 204) associated with the shopping path 38, in order to detect whether any products from the predefined region were purchased by the shopper [wherein Figs. 8 and 11 display exemplary output of determinations based on product arrangement, product purchase, and purchase order]).
Claims 3/11/19: Sorensen further teaches acquiring, for each of the plurality of customers, purchase order of a plurality of the products purchased by the customer; and determining the estimation region based on the first information, the second information, and the acquired purchase order (pars. 36, 48, 55, 66, and Figs. 5, 8, and 11: data compilation 100 typically further includes a first pause or shop measure 120a, a first purchase measure 122a, and an order of purchase measure; Order of purchase is a measure indicating the relative order of a given purchase on a shopping trip; generating a data compilation, also referred to as a report, of the shopping behavior within the predefined region. The report may take the form shown in FIG. 8, and may be product specific, zone (i.e., predefined region) specific, category or any other merchandising relevant grouping; FIG. 11 is a schematic view of a path record and a purchase record utilized by the data analyzer of the system of FIG. 1 [wherein Fig. 11 shows purchase order of a customer with timestamps in temporal sequence along with region coordinates and items purchased, and wherein Fig. 8 shows similar information via a zone/product report without timestamps, but nevertheless depicting a sequence of shopper behavior, including purchases]).
Claims 4/12/20: Sorensen further teaches determining, for each of the plurality of customers, the estimation region for a set of information that specifies an (i−1)-th product (i is a natural number equal to or larger than 2 and equal to or smaller than the number of products purchased by the customer) purchased (i−1)-th by the customer and information that specifies an i-th product purchased i-th by the customer among a plurality of the products purchased by the customer, based on information related to an (i−1) total set and the first information (pars. 29, 33, 55, and Figs. 8 and 11: describing/displaying features for determining zones corresponding to products, and corresponding shopper paths and order of purchase, wherein Fig. 11 displays Item 1 thru Item 6 purchased by a customer with coordinates related thereon, and Fig. 8 displays the relative order of a given purchase on a shopping trip, and for determined zones in the zone/product report, and as shown with context in Figs. 1-5 - e.g., As shown in FIG. 11, the series of positions is typically represented by an array of coordinate pairs in a path record 202. Typically a shopper path is determined to have started when motion of a particular transmitter is detected near a cart return area near the entrance of the shopping environment. To determine where one shopping path ends, data analyzer 16 is typically configured to detect whether the position of the transmitter is adjacent point of sale terminal 14a of purchaser record system 14, indicating that the shopper is at the check out counter, purchasing items. Alternatively, the cart may have a barcode or tag that can be scanned by the point of sale terminal or other scanner to link the shopper path and the purchase record; identify and record purchased products 40a, thereby generating a purchase record 204 (shown in FIG. 11) for each shopper).
Claims 5/13: Sorensen further teaches wherein the estimation region is a rectangular region that has one or more coordinates of the product purchased by the customer as a vertex (pars. 37, 40, 52, and Figs. 2-4: Data analyzer 16 is configured to recognize a predefined region 42 relative to each product 40. Predefined region 42 may also be referred to as a zone, and may have one or more subzones. Typically the product is located within the predefined region 42, and the predefined region extends around the product by a distance R, which may be constant, or more typically, variable. The predefined region may alternatively be adjacent the product or separated by some predetermined distance from the product. The predefined region may be virtually any shape suitable for detecting meaningful shopper behaviors. For example, the predefined region may be rectangular, curved, polygonal, etc. [wherein Fig. 2 displays an estimation region in the form of a rectangle, including product 40 shown as a vertex]; See also, pars. 52, 54-55, and Fig. 8: describing/displaying product purchase data and coordinates that may correspond to the product in the rectangle shown in Fig. 2).
Claims 6/14: Sorensen further teaches wherein the estimation region is a region that includes one or more routes that allows movement from coordinates of one product purchased by the customer to coordinates of another product purchased by the customer (pars. 46, 54-55, 64, and Figs. 1, 5, 7, and 8: e.g., data analyzer may also be configured to detect a destination pattern 64, in which the shopper path enters the shopping environment, travels to a region from which one or more products are purchased; detecting that the shopping path is within a predefined region associated with one or more products; data compilation 100 typically further includes a first pause or shop measure 120a, a first purchase measure 122a, and an order of purchase measure; Order of purchase is a measure indicating the relative order of a given purchase on a shopping trip; generating a data compilation; See also, pars. 52, 54-55, and Fig. 8: describing/displaying product purchase data and coordinates that may correspond to the products purchased along the shopper path within the estimated region/zone).
Claims 7/15: Sorensen further teaches setting weighting to the estimation region according to a distance from coordinates of the product purchased by the customer; and calculating the evaluation value based on the estimation region to which the weighting is set (pars. 6, 37, 61, and Figs. 8 and 11: Typically the product [i.e., purchased product, see pars. 52, 54-55, and Fig. 8] is located within the predefined region 42, and the predefined region extends around the product by a distance R, which may be constant, or more typically, variable; tracking system, and calculating a product-shopper proximity measure [weighting] based at least in part, on a physical distance of a shopper traveling along the shopping path, from the position of the product; calculating a product-shopper proximity measure 110 based at least in part on a physical distance of a shopper traveling along the shopping path from the position of the product. The physical distance may be a measured distance D between the product and the shopper path, or the physical distance may be defined by a predefined region 42, discussed above. Thus, the product-shopper proximity measure may be a measure of the percentage of number of shopper paths that pass within a predefined region around a particular product, or that pass within a predetermined distance D of a product).
Claims 8/16: Sorensen further teaches setting weighting to the estimation region such that the weighting increases as coordinates of the product purchased by the customer are approached; and calculating the evaluation value based on the estimation region to which the weighting is set (pars. 6, 37, 61, and Figs. 8 and 11: tracking system, and calculating a product-shopper proximity measure [weighting] ]based at least in part, on a physical distance of a shopper traveling along the shopping path, from the position of the product; calculating a product-shopper proximity measure 110 based at least in part on a physical distance of a shopper traveling along the shopping path from the position of the product. The physical distance may be a measured distance D between the product and the shopper path, or the physical distance may be defined by a predefined region 42, discussed above. Thus, the product-shopper proximity measure may be a measure of the percentage of number of shopper paths that pass within a predefined region around a particular product, or that pass within a predetermined distance D of a product; Typically the product [i.e., purchased product, see pars. 52, 54-55, and Fig. 8] is located within the predefined region 42, and the predefined region extends around the product by a distance R, which may be constant, or more typically, variable).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sorensen (US 2004/0111454): discloses shopping environment analysis techniques, including acquiring first information that includes information related to arrangement of a plurality of products in a store (pars. 26-28, 30, 34, 48, and Fig. 6: data received from the shopping environments includes shopper path data … product position data; Product position data 26 typically includes position data 118 including a series of product positions 120 indicated by an array of coordinate pairs; store map also typically includes information on the position of products within the shopping environment; track the location of…products throughout the shopping environment); acquiring second information that includes information that specifies one or a plurality of the products purchased by each of a plurality of customers (pars. 26-28, 30, 42, 44, 47, and Fig. 6: Purchase data 24 typically includes a list 114 of products purchased by the shopper, as indicated by purchase records from purchase records computer; purchase records and shopper paths may be linked by coordinating the time and location of checkout in the shopper data and purchase data; data received from the shopping environments includes…purchase data; link the shopper path and the purchase record; FIG. 6 shows a database record 100 including stored shopper path data 22, non-shopper path data 23, purchase data 24, product position data 26, and environment data); and determining an estimation region in which each of the plurality of customers is estimated to have moved in the store based on the first information and the second information (pars. 28, 35, 40, 73, and Figs. 4-5: track the position of a shopper; link the purchase data 24 with the shopper path data 22 to identify those purchase records that correspond with each shopper path; FIG. 4 shows exemplary shopper paths 92 and non-shopper paths 93 tracked by tracking system 36, within shopping environment; determine whether a predetermined shopping behavior is exhibited within the shopping path; detect whether a shopping path visits (i.e., passes through) the predetermined product region, shops…or purchases a product in the predetermined region; shopping path is considered to show purchasing behavior in a region of the shopping environment when it is determined that the shopper has selected an item in the region for purchase, and the shopping path has intersected that region).
Arai et al. (US 2023/0027388): discloses price determination features, including features for detecting a line or sight of a customer (par. 41).
Shivashankar et al. (US 2019/0108561): discloses purchase intent determination and real time in-store shopper assistance features, including multiple regions of interest within a field of view of a sensor (par. 46).
Sekine et al. (US 2011/0007152): discloses a flow line recognition system for monitoring paths of moving objects throughout an area, including generating flow line information indicative of a moving object and time information related thereto (at least paragraph 24).
Harada (US 2015/0066551): discloses features for analyzing flow line data, including analyzing stay time associated with a specific area (at least paragraph 72).
Wang et al. (US 2015/0324812): discloses a method/device for obtaining customer traffic distribution, including receiving coordinates from a plurality of mobile devices within an environment divided into a plurality of grids (at least paragraphs 42, 47, 52, 61, 93, 117, and Figs. 1-8), determining a grid to which the mobile devices belong based on the coordinate data of the mobile devices (at least paragraphs 47, 78, 62, and 65-66), and outputting a distribution map of customer traffic, flow, and density based on the number of mobile devices in each grid (paragraphs 38, 73, 87-88, 99-100, 128, and Fig. 7).
D. A. Mora Hernandez, O. Nalbach and D. Werth, "How Computer Vision Provides Physical Retail with a Better View on Customers," 2019 IEEE 21st Conference on Business Informatics (CBI), Moscow, Russia, 2019, pp. 462-471: discloses features for generating movement tracks over time for individual customers, including a visual acquisition system (IP cameras) and data analysis (statistics, supervised/unsupervised learning) to assist with shoplifting prevention, layout optimization, real-time recommendations, and staff deployment.
A. A. Pandit, J. Talreja, M. Agrawal, D. Prasad, S. Baheti and G. Khalsa, "Intelligent Recommender System Using Shopper's Path and Purchase Analysis," 2010 International Conference on Computational Intelligence and Communication Networks, Bhopal, India, 2010, pp. 597-602: discloses features for analyzing customer behavior (paths and purchases) in order to recommend changes to store layout and increase saleability of products.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Timothy A. Padot whose telephone number is 571.270.1252. The Examiner can normally be reached on Monday-Friday, 8:30 - 5:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian Epstein can be reached at 571.270.5389. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/TIMOTHY PADOT/
Primary Examiner, Art Unit 3625
04/01/2026