DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
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.
Status of the Claims
This communication is in response to communications received on 5/18/26. Claim(s) 1-3, 6, 8, and 9 is/are amended, claim(s) 5 is/are cancelled, claim(s) 10-15 is/are new, and applicant states support can be found at instant specification [0117, 0159, 0161, and 0178-0179]. Therefore, Claims 1-4 and 6-15 is/are pending and have been addressed below.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application(s) JP 2024-006795 filed in Japan on January 19, 2024. Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Claims Without Prior Art Rejections
Claim(s) 1-4 and 6-15 do/does not have prior art rejections. The remaining rejections are 101 as noted below.
Closest prior art to the invention claims without rejections include
Nakano et al. (WO 2020/045459 A1) in view of Romero published December 17, 2020, Nagata et al. (US 2012/0135749 A1), and Nguyen et at. published February 10, 2022 (reference U on the Notice of References Cited) for claim(s) 1-4 and 6-15.
The combined arts do not teach
wherein the analyzing step performs at least part of the first estimation processing or the second estimation processing using generative AI by causing the generative AI to output processing information as output information using a function of function calling, and further inputs instruction information further including information indicating an instruction to draw the estimated designated store trading area on a map and map information indicating a map of a region including the designated store to the generative AI as input information to cause the generative AI to output map information indicating the designated store trading area on the map, and wherein the instruction information includes information for instructing estimation of a level of a frequency of posting concerning the store to a Social Network Service (SNS) by the store user in the trading area or a level of a frequency of posting concerning the store to a communication service by the store user in the trading area, as an estimate of the characteristics of the trading area.
Regarding claim(s) 2-4, 6-7, and 10-15 is/are rejected because they depend on claim(s) 1, and 8-9.
The limitation wherein the analyzing step performs at least part of the first estimation processing or the second estimation processing using generative AI by causing the generative AI to output processing information as output information using a function of function calling, and further inputs instruction information further including information indicating an instruction to draw the estimated designated store trading area on a map and map information indicating a map of a region including the designated store to the generative AI as input information to cause the generative AI to output map information indicating the designated store trading area on the map, and wherein the instruction information includes information for instructing estimation of a level of a frequency of posting concerning the store to a Social Network Service (SNS) by the store user in the trading area or a level of a frequency of posting concerning the store to a communication service by the store user in the trading area, as an estimate of the characteristics of the trading area
is interpreted as
wherein the analyzing step:
performs at least part of the first estimation processing or the second estimation processing using generative AI by causing the generative AI to output processing information as output information using a function of function calling, and
further inputs instruction information further including information indicating an instruction to draw the estimated designated store trading area on a map and map information indicating a map of a region including the designated store to the generative AI as input information to cause the generative AI to output map information indicating the designated store trading area on the map, and
wherein the instruction information includes information for instructing estimation of a level of a frequency of posting concerning the store to a Social Network Service (SNS) by the store user in the trading area or a level of a frequency of posting concerning the store to a communication service by the store user in the trading area, as an estimate of the characteristics of the trading area.
The prior art teaching are provided below for reference only.
Nakano teaches a receiving step of receiving {a reception unit that receives – claim 1} designation of a store [see at least [0016] location can be a store “Here, the points set on the map may be, for example, shopping centers, supermarkets, department stores, etc., but are not limited to these and may also be tourist spots, public facilities such as libraries and hospitals, or transportation facilities such as stations. In the following description, the locations that are the subject of trade area analysis may be referred to as visited locations.”;
[0017] receive location information “The customer terminal 100 is an information processing terminal used by a customer, and is realized by, for example, a smartphone or a tablet terminal.
In this embodiment, a customer refers to a person who owns an information processing terminal on which a program (for example, a smartphone-specific app) that causes the server device 300 to acquire location information used in the data analysis system 1 is installed, but this is not limited to this, and any form is acceptable as long as the server device 300 can acquire location information about the terminal itself, and may include members or regular customers of the stores mentioned above, customers who have only visited once, and customers who just drop by without purchasing any services or making any commercial transactions.”;
[0054] “Returning to FIG. 3, the control unit 330 will be described.
The control unit 330 is, for example, a controller, and is realized by a CPU, an MPU, etc., executing various programs (corresponding to an example of a judgment program) stored in a storage device inside the server device 300 using RAM as a working area. The control unit 330 is a controller, and is realized by an integrated circuit such as an ASIC or FPGA.”;
[0055] “The control unit 330 has an acquisition unit 331, an estimation unit 332, a visit rate calculation unit 333, an average calculation unit 334, an identification unit 335, and an output unit 336, and realizes or executes the functions and actions of the information processing described below.”;
[0056] “The acquisition unit 331 acquires various types of information.
For example, the acquisition unit 331 acquires information about visited places from the information processing terminal 200 and stores it in the visited place information 322. Also, for example, the acquisition unit 331 identifies the customer terminal 100 based on information such as when the customer terminal 100 uses a dedicated application or website, and continuously acquires the location information of the customer terminal 100 from the identified customer terminal 100. Specifically, the acquisition unit 331 acquires various pieces of information detected or acquired by the customer terminal 100 as location information.”];
an analyzing step of performing {an analysis unit that performs - claim 1}, based on information concerning a user of the store, analysis, the designation of which has been received in the receiving step, [see at least [0055] “The control unit 330 has an acquisition unit 331, an estimation unit 332, a visit rate calculation unit 333, an average calculation unit 334, an identification unit 335, and an output unit 336, and realizes or executes the functions and actions of the information processing described below.”;
[0056] “The acquisition unit 331 acquires various types of information.
[0097] “The identification unit 335 may determine a significant difference using the feature score calculated as described above, and identify a feature region based on the value of the feature score.”;
[0098] “Returning to FIG. 3, the output unit 336 outputs the feature regions and/or feature scores identified as described above.”]; and
a providing step of providing {a provision unit that provides- claim 1} map information including information indicating the trading area estimated by the analyzing step and information indicating the characteristics of the trading area [see at least [0054] “Returning to FIG. 3, the control unit 330 will be described.
The control unit 330 is, for example, a controller, and is realized by a CPU, an MPU, etc., executing various programs (corresponding to an example of a judgment program) stored in a storage device inside the server device 300 using RAM as a working area. The control unit 330 is a controller, and is realized by an integrated circuit such as an ASIC or FPGA.”;
[0055] “The control unit 330 has an acquisition unit 331, an estimation unit 332, a visit rate calculation unit 333, an average calculation unit 334, an identification unit 335, and an output unit 336, and realizes or executes the functions and actions of the information processing described below.”;
[0098] “Returning to FIG. 3, the output unit 336 outputs the feature regions and/or feature scores identified as described above.
For example, the output unit 336 superimposes the characteristic region on the map data of the map information 321 stored in the storage unit 320 and transmits the superimposed map data to the information processing terminal 200 via the communication unit 310 .
The user of the information processing terminal 200 can easily understand which areas particularly require analysis based on the characteristic regions and characteristic scores superimposed on the transmitted map data.”].
Romero discloses
an analyzing step of performing, based on information concerning a user of the store, first estimation processing of estimating a trading area of the store, and second estimation processing of estimating characteristics of the trading area estimated in the first estimation processing, and
a providing step of providing map information including information indicating the trading area estimated by the analyzing step and information indicating the characteristics of the trading area [for the limitations above, see at least [pg 2] a) user data such as in store navigation is used to generate areas where purchases occur hot and cold areas, b) hot and cold areas can be further defined (characterized) as areas of the store that produce the highest and lowest sales, the zones with greatest or lowest traffic, or the areas where shoppers stop and for how long (dwell time) within a store and c) the areas can be placed on a map (heat map) “The in-store navigation app constantly tracks the position of the shopper in the store and guides them to the exact shelf locations for the products in their shopping list, previously created in their mobile phone. The guidance is done by superimposing directions and indications on the shopper’s mobile phone, and it can be enhanced with augmented reality to help visualize the route better.
Retailers can leverage in-store navigation not only to help the shopper find the desired product quicker, but also to do real-time marketing and so increase basket size. For example, as the shopper navigates around the store, the system tracks their exact location and can flash up special offers as the shopper approaches them. This information would be tailored to the preferences of each shopper.
By analyzing the wealth of data produced by the navigation app, retailers can gain greater insight into the spatial performance of their stores and of promotions, and visualize the information in an intuitive fashion as heat maps.
Depending on the type of heat map, the warm and cold areas of the heat maps can show the areas of the store that produce the highest and lowest sales, the zones with greatest or lowest traffic, or the areas where shoppers stop and for how long (dwell time).”].
Nagata discloses
wherein, in the first estimation processing, the analyzing step estimates the trading area using an estimation method such as kernel density estimation based on the place of sojourns of store users and estimates, as the trading area of the designated store, one or more regions where density is equal to or larger than a predetermined value, and [see at least [0073] “As illustrated in FIG. 5( a), the all-user density estimation module 602A estimates density of point data of all the users in each of a plurality of zones partitioned on the two dimensional map data in advance (step S201). The first extraction module 602B extracts an area, as a haunt area, the area in which the estimated density of point data of all the users is equal to or more than a predetermined level (step S202). It should be noted that, for the area division mentioned above, it is possible to divide area into a mesh with many squares or into many polygons (the same applies to the area division described later).”].
Nguyen discloses
wherein the analyzing step performs at least part of the first estimation processing or the second estimation processing using generative AI by causing the generative AI to output processing information as output information [see at least [pg 7-9] “Heatmap analytics
The output of human detection can be used for constructing business heatmaps. A heatmap can provide a visual summary of information by the two-dimensional representation of data, in which values are represented by colours as illustrated in Fig. 4. There can be a number of ways to display heatmaps, but they all share one thing in common—they use colours to draw the relationships between data values that would be much harder to understand if presented in a sheet of numerical values. For example, the following heatmap employs warmer colours for locations where all customers spend more time there. Heatmaps are being used to understand sales and marketing.”].
Response to Arguments
Applicant’s arguments, see applicant’s remarks, filed 5/18/26, with respect to rejections under 35 USC 112 for claim(s) 3 and 103 for claim(s) 1-4 and 6-15 have been fully considered and are persuasive in part. The Examiner respectfully withdraws rejections under 35 USC 112 for claim(s) 3 and 103 for claim(s) 1-4 and 6-15.
Applicant’s arguments, see applicant’s remarks, filed 5/18/26, with respect to rejections under 35 USC 101 for claim(s) 1-4 and 6-15 have been fully considered but they are not persuasive as far as they apply to the amended 103 rejection(s) below.
Applicant respectfully traversed the rejection on pg. 8-11.
The Examiner respectfully disagrees because while the applicant may be correct that application of social media data to heat mapping is new, it is not clear why the social media could data not have been incorporated prior to generative ai. It is not clear that this actually an improvement to technology or a new method to perform the evaluation. Instant specification of the published application [0178-0179] does not further clarify whether the inclusion of generative ai, kernel density, and social media data is more than additional data being analyzed and/or using additional methods of analysis.
It is noted that a more concrete argument on this point may overcome the 101 rejection.
Applicant is relying on 2106.05(d) “well understood, routine, and conventional” however Examiner is relying on 2106.05(f) “apply it.” Examiner relied on “apply it” because of item (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process of 2106.05(f).
Thus, the argument(s) are unpersuasive.
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.
Claim(s) 1-4 and 6-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 1, 8, and 9 that, under its broadest reasonable interpretation, is directed to estimating a trading area of a store and characteristics of the trading area.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 8 recites a series of steps) and system (claim(s) 1-4, 6-7, and 9-15 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s):
Claim 1: a receiving step of receiving designation of a store;
an analyzing step of performing, based on information concerning a user of the store, first estimation processing of estimating a trading area of the store, the designation of which has been received in the receiving step, and second estimation processing of estimating characteristics of the trading area estimated in the first estimation processing,
wherein, in the first estimation processing, the analyzing step estimates the trading area using an estimation method such as kernel density estimation based on the place of sojourns of store users and estimates, as the trading area of the designated store, one or more regions where density is equal to or larger than a predetermined value, and
wherein the analyzing step performs at least part of the first estimation processing or the second estimation processing using generative AI by causing the generative AI to output processing information as output information using a function of function calling, and further inputs instruction information further including information indicating an instruction to draw the estimated designated store trading area on a map and map information indicating a map of a region including the designated store to the generative AI as input information to cause the generative AI to output map information indicating the designated store trading area on the map, and wherein the instruction information includes information for instructing estimation of a level of a frequency of posting concerning the store to a Social Network Service (SNS) by the store user in the trading area or a level of a frequency of posting concerning the store to a communication service by the store user in the trading area, as an estimate of the characteristics of the trading area; and
a providing step of providing map information including information indicating the trading area estimated by the analyzing step and information indicating the characteristics of the trading area.
Claim(s) 1 and 9: same analysis as claim(s) 8.
Claim 1 additionally: a reception unit;
an analysis unit;
a provision unit.
Dependent claims 2-4, 6-7, and 10-15 recite the same or similar abstract idea(s) as independent claim(s) 1, 8, and 9 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with estimating a trading area of a store and characteristics of the trading area.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include kernel density estimation, generative AI (claim(s) 1, 8-9), information processing apparatus, units (claim(s) 1), computer (claim(s) 8), non-transitory computer-readable storage medium, computer (claim(s) 9), unit (claim 2-4, 6-7), generative ai (claim(s) 6, 10-11, 13-14), large language model (claim(s) 11-12), transformer-based model (claim(s) 12).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [pg 64 first full para]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [pg 64 first full para]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
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.
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