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 3/7/25. Claim(s) none is/are amended, claim(s) none is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification as there are no amendments. Therefore, Claims 1-9 is/are pending and have been addressed below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 1/10/25 was/were considered by the examiner.
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.
Response to Arguments
There are no arguments.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 3 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim(s) 3 is/are rejected. Claim 3 state(s) the limitation “wherein the analysis unit, in the second estimation processing, estimates the characteristics of the trading area based on a geographical relationship between the store, the designation of which has been received by the reception unit, and the trading area.” Thus claim(s) 3 is/are indefinite because it is unclear if the designation refers to a) a geographical relationship between the store or b) a geographical relationship between the store and trading area. Appropriate correction/clarification is required.
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) 1-9 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) 1-7 and 10 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; 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 2-7 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 information processing apparatus, units (claim(s) 1), computer (claim(s) 8), non-transitory computer-readable storage medium, computer (claim(s) 9), unit (claim 2-7).
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)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claim(s) 1-4 and 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nakano et al. (WO 2020/045459 A1) in view of Romero published December 17, 2020 (reference U on the Notice of References Cited).
Regarding claim 1, 8, and 9, Nakano teaches an information processing method executed by a computer, the information processing method comprising {an information processing apparatus comprising: - claim 1}
{a non-transitory computer-readable storage medium storing an information processing program for causing a computer to execute: - claim 9} [see at least [0016] “In FIG. 1, the data analysis system 1 is a system for analyzing the trade area of a point set on a map, and includes a plurality of customer terminals 100, an information processing terminal 200, and a server device 300.
The server device 300 is communicably connected to a plurality of customer terminals 100 and information processing terminals 200 via a network NW.”]:
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.”].
Nakano teaches user analysis to determine trading areas but doesn’t/don’t explicitly teach how that analysis is done however, in the field pertinent to the particular problem with which the applicant was concerned such as heat map analysis of a store, 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).”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nakano with Romero discloses to include the limitation(s) above as disclosed by Romero. Doing so would improve Nakano’s (Nakano) trade zone analysis to include heat maps to determine both profitable and unprofitable area [see at least Romero [pg 2] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Nakano and b) Romero and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 2, modified Nakano teaches the information processing apparatus according to claim 1,
and Nakano teaches wherein the analysis unit performs an action on the data, the designation of which has been received by the reception unit [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.”].
Modified Nakano (Nakano) teaches user analysis to determine trading areas but doesn’t/don’t explicitly teach how that analysis is done however, in the field pertinent to the particular problem with which the applicant was concerned such as heat map analysis of a store, Romero discloses
in the first estimation processing, estimates the trading area based on information indicating a place of sojourn of the user of the store [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).”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Nakano with Romero discloses to include the limitation(s) above as disclosed by Romero. Doing so would improve modified Nakano’s (Nakano) trade zone analysis to include heat maps to determine both profitable and unprofitable area [see at least Romero [pg 2] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Nakano and b) Romero and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 3, modified Nakano teaches the information processing apparatus according to claim 1,
and Nakano teaches wherein the analysis unit performs an action on the data, the designation of which has been received by the reception unit [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.”].
Modified Nakano (Nakano) teaches user analysis to determine trading areas but doesn’t/don’t explicitly teach how that analysis is done however, in the field pertinent to the particular problem with which the applicant was concerned such as heat map analysis of a store, Romero discloses
in the second estimation processing, estimates the characteristics of the trading area based on a geographical relationship between the store and the trading area [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).”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Nakano with Romero discloses to include the limitation(s) above as disclosed by Romero. Doing so would improve modified Nakano’s (Nakano) trade zone analysis to include heat maps to determine both profitable and unprofitable area [see at least Romero [pg 2] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Nakano and b) Romero and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 4, modified Nakano teaches the information processing apparatus according to claim 1, as well as wherein the analysis unit, in the second estimation processing, estimates the characteristics of the trading area
and Nakano teaches wherein the analysis unit performs an action on the data based on information concerning another store or facility around the store, the designation of which has been received by the reception unit, the other store or facility having a predetermined relationship with the store, the designation of which has been received by the reception unit [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.”;
[0100] competing stores have a relationship with the store of being in a similar area where the relationship is predefined as the location is predefined “For example, factors such as the presence of competing stores in such characteristic areas may result in low visitation rates, or good access to highways may result in high visitation rates even for stores located far away. When analyzing a trade area, the user is presented with characteristic areas, which gives them an opportunity to analyze the unique circumstances of that area more carefully.”;
[0091] predetermined areas “As described above, the identification unit 335 identifies a characteristic area based on whether the confidence interval of the visit rate estimated for each predetermined area deviates from the average visit rate for the distance from the visit place to the area.”].
Regarding claim 7, modified Nakano teaches the information processing apparatus according to claim 1, as well as trading area estimated by the analysis unit
and Nakano teaches wherein the provision unit provides data [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.”].
Modified Nakano (Nakano) teaches user analysis to determine trading areas but doesn’t/don’t explicitly teach how that analysis is done however, in the field pertinent to the particular problem with which the applicant was concerned such as heat map analysis of a store, Romero discloses
provides, as the map information, information concerning a state in which the trading area is highlighted on a map [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).”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Nakano with Romero discloses to include the limitation(s) above as disclosed by Romero. Doing so would improve modified Nakano’s (Nakano) trade zone analysis to include heat maps to determine both profitable and unprofitable area [see at least Romero [pg 2] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Nakano and b) Romero and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nakano in view of Romero as applied to claim(s) 1 above and further in view of Green et al. published July 24, 2023 (reference V on the Notice of References Cited).
Regarding claim 5, modified Nakano teaches the information processing apparatus according to claim 1, wherein the analysis unit performs the second estimation processing.
Modified Nakano (Romero) teaches estimates data but doesn’t/don’t explicitly teach the methodology used in the estimation, however in the field pertinent to the particular problem with which the applicant was concerned such estimation based on user data, Green discloses
performs the second estimation processing using generative Al [see at least [pg 2] “Business forecasting and scenario analysis can be improved through generative AI's predictive modeling capabilities. By integrating economic data, market trends, and other relevant variables, businesses can simulate different scenarios and assess their potential impact on financial performance.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Nakano with Green discloses to include the limitation(s) above as disclosed by Green. Doing so would improve modified Nakano’s (Nakano) trade zone analysis by “empowers organizations to make informed strategic decisions, identify opportunities, and mitigate risks” [see at least Green [pg 2] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Nakano and b) Green and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 6, modified Nakano teaches the information processing apparatus according to claim 5, wherein the analysis unit performs the first estimation processing.
Modified Nakano (Romero) teaches estimates data but doesn’t/don’t explicitly teach the methodology used in the estimation, however in the field pertinent to the particular problem with which the applicant was concerned such estimation based on user data, Green discloses
performs the first estimation processing using generative Al [see at least [pg 2] “Business forecasting and scenario analysis can be improved through generative AI's predictive modeling capabilities. By integrating economic data, market trends, and other relevant variables, businesses can simulate different scenarios and assess their potential impact on financial performance.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Nakano with Green discloses to include the limitation(s) above as disclosed by Green. Doing so would improve modified Nakano’s (Nakano) trade zone analysis by “empowers organizations to make informed strategic decisions, identify opportunities, and mitigate risks” [see at least Green [pg 2] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Nakano and b) Green and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nakano et al. – JP 7246037 B2 (relevant because it teaches the same as WO 2020/045459 A1)
Retailing Management – Attention Shoppers: Store is Tracking Your Cell (relevant because it teaches same as Romero sans mapping)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES WEBB/Examiner, Art Unit 3624