DETAILED ACTION
Status of Claims
Claims 1-5, 9, and 10 submitted on 12/23/2025 are pending and have been examined. Claims 6-8 have been cancelled. Claim 1 has been amended. Claim 10 has been newly added.
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 .
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been filed in parent application No. JP2021-160671, filed on 09/30/2021.
Acknowledgement is made of applicant’s claim for a 371 of international application. The certified copy has been filed in application No. PCT/JP2022/031590, filed on 08/22/2022.
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 1-5, 9, and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1
Claims 1-5 and 9 are directed to a machine and claim 10 is directed to a process (see MPEP 2106.03).
Step 2A, Prong 1
Claim 1, taken as representative, recites at least the following limitations that recite an abstract idea:
an action predicting, comprising:
store an action probability, which is a probability that a person in a predetermined area is going to be involved in actions, and a movement probability, which is a probability that a person in a predetermined area is going to move to a movement destination area that is an area of a movement destination;
acquire population distribution data regarding a population for each area;
predict a population involved in actions for each movement destination area based on the acquired population distribution data, the stored action probability and the stored movement probability; and
create an around a movement destination area based on a prediction result and guide a person to a store by sending information related to the store to the person when the person checks.
The above limitation, under its broadest reasonable interpretation, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that it recites a commercial interaction, see ¶0010 of the instant specification. Claim 10 recites similar limitations as claim 1.
Thus, under Prong 1 of Step 2A, claims 1 and 10 recite an abstract idea.
Step 2A, Prong 2
Claim 1 includes the following additional elements that are bolded:
an action predicting device, comprising processing circuitry configured to:
store an action probability, which is a probability that a person in a predetermined area is going to be involved in actions, and a movement probability, which is a probability that a person in a predetermined area is going to move to a movement destination area that is an area of a movement destination;
acquire population distribution data regarding a population for each area;
predict a population involved in actions for each movement destination area based on the acquired population distribution data, the stored action probability and the stored movement probability; and
create a geofence around a movement destination area based on a prediction result and guide a person to a store by sending information related to the store to a mobile terminal of the person when the person checks into the geofence.
Claim 10 includes the same additional elements as claim 1.
The additional elements recited in claims 1 and 10 merely invoke such elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment (see MPEP 2106.05(f) and MPEP 2106.05(h). These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see ¶¶0095-0097).
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the additional elements do not integrate the judicial exception into a practical application and, thus, claims 1 and 10 are directed to an abstract idea.
Step 2B
As noted above, while the recitation of the additional elements in independent claims 1 and 10 are acknowledged, claims 1 and 10 merely invoke such additional elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment of electronic devices and geofences (see MPEP 2106.05(f) and MPEP 2106.05(h)).
Even when considered as an ordered combination, the additional elements of claim 1 and 10 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1 and 10 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1 and 10 are ineligible.
Dependent claims 2-5 and 9 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-5 and 9 merely further define the abstract limitations of claims 1 and 10 or provide further embellishments of the limitations recited in independent claims 1 and 10. Claims 2-5 and 9 do not introduce any further additional elements. Thus, dependent claims 2-5 and 9 are ineligible.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim(s) 1-3, 5, 9, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Itoh et al. (US 2019/0122233 A1 [previously cited]) in view of Paulrajan et al. (US 2016/0155187 A1).
Regarding Claim 1, Itoh et al., hereinafter, Itoh, discloses an action predicting device, comprising processing circuitry configured to (Fig. 1; Abstract [A simulation device includes an acquisition unit that acquires people flow information indicating a flow of people moving in a facility such as a store]):
store an action probability, which is a probability that a person in a predetermined area is going to be involved in actions, and a movement probability, which is a probability that a person in a predetermined area is going to move to a movement destination area that is an area of a movement destination (Fig. 1[element 30]; ¶0040[Storage unit 30 stores information received by receiver 11 or information generated by controller 20. For example, storage unit 30 stores traffic line information (people flow information) 31 indicating a flow of people moving in the store, area information 32 indicating a range of each of a plurality of areas that constitute the inside of the store, transition probability information 33 indicating a probability of a move of a person between adjacent areas in the store, POS data 34 transmitted from POS terminal device 200, item information 35 indicating locations and prices of items arranged in the store, purchase rate information 36 indicating a purchase rate of an item, and average sales amount per customer information 37]);
acquire population distribution data regarding a population for each area (Fig. 4[showing each area] and Figs. 5A-5C; ¶0046[FIG. 4 is an enlarged view of a part of the inside of the store shown in FIG. 3. FIG. 4 shows a situation that a person identified as a person ID “0001” in images moves from the area “A-01” to the area “A-02”, a person identified as a person ID “0002” moves from the area “A-01” to the area “A-03”, and no one moves from the area “A-01” to the area “A-04”. FIG. 4 further shows a situation that item shelf S1 including item A is disposed in the area “A-01”, and item shelf S2 including item B and item C is disposed in the area “A-02”] in view of ¶0042 disclosing the acquiring step; Examiner notes that the persons are comparable to population data);
predict a population involved in actions for each movement destination area based on the acquired population distribution data, the stored action probability and the stored movement probability (Fig. 6; ¶¶0048-0051[Transition probability information generator 22 first selects one of the areas in the store (S601) and searches for an area to which a move is allowed from the area thus selected (move origin area) based on area information 32 (S602). For example, with respect to the move origin area “A-01” shown in FIG. 4, the move allowable areas “A-02”, “A-03”, “A-04” are found. Transition probability information generator 22 extracts all people located in the move origin area based on traffic line information 31 and area information 32 (S603). A person located in each of the areas can be extracted based on a location of the person (an X coordinate and a Y coordinate) indicated by traffic line information 31 and a range of the area (an X coordinate, a Y coordinate, a width, and a height) indicated by area information 32. For example, two people located in the move origin area “A-01” are extracted (the number of people N=2). Transition probability information generator 22 searches for move destination areas to which the people located in the move origin area “A-01” have moved based on traffic line information 31 and area information 32 (S604). The move destination areas to which the people have moved can be found based on locations of the people indicated by traffic line information 31 (X coordinates and Y coordinates) and ranges of areas (X coordinates, Y coordinates, widths, and heights) indicated by area information 32. In the example shown in FIG. 4, “A-02” and “A-03” are found as move destination areas. Transition probability information generator 22 calculates a transition probability for each of move destination allowable areas from “transition probability P=the number of people who move to move destination allowable areas/the number of people N located in the move origin area (S605)]); and
based on a prediction result sending information related to the store to a mobile terminal (Fig. 1; ¶¶0065-0067[simulation system 1 of the present disclosure executes a simulation of calculating a predicted value of a sales amount (an average sales amount per customer) of a store when a current shopping pattern (a transition probability and a purchase rate) is modeled from traffic line information 31 acquired from images taken by monitoring camera 100, POS data 34 acquired from POS terminal device 200, area information 32 on an inside of a store, and item information 35, and a flow of people, a layout of the store, or a location of an item is changed on the model.]).
Although Itoh discloses a prediction result and sending information related to a store, Itoh does not explicitly disclose create a geofence around a movement destination area and guide a person to a store by sending information to a device of the person when the person checks into the geofence.
However, Paulrajan et al., hereinafter, Paulrajan, teaches creating a geofence and guiding a person to a store by sending information related to the store to the device of a person when the person checks into a geofence (Fig. 5G; ¶0048[Additionally, or alternatively, when a user selects a product to be placed on a wish-list, virtual reality control device 230 may delay providing the customized virtual reality user environment for content regarding the product until the product becomes available for purchase at a brick-and-mortar store within a threshold distance of the user's location (e.g., that may be determined based on information provided by virtual reality device 210, based on a user-established geo-fence, etc.). For example, a user may configure a geo-fence for a particular location (e.g., around the user's house, around a route that the user intends to use, etc.), and may associate the geo-fence with a wish-list of products to be purchased. In this case, virtual reality control device 230 may provide an indication of the wish-list of products to one or more physical stores located within the geo-fence, and, upon receiving a notification, from a particular physical store of the one or more physical stores, that a particular product of the wish-list of products is available, may provide a customized virtual reality user environment for purchasing the particular product, for directions to the particular physical store, or the like.]).
The system of Paulrajan is applicable to the system of Itoh as they share characteristics and capabilities, namely, they are both targeted to providing online information related to a store. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction result and information related to a store as disclosed by Itoh to include creating a geofence and guiding a user to a store as taught by Paulrajan. One of ordinary skill in the art would have been motivated to expand the system of Itoh in order to generate a customized virtual reality user environment based on a particular nearby store or based on another store location (¶0040).
Regarding Claim 2, Itoh in view of Paulrajan teaches the action predicting device according to claim 1, Itoh further discloses
wherein the action probability is a probability that a person in a predetermined area at a predetermined time is going to be involved in actions (Fig. 1[element 30]; ¶0040[Storage unit 30 stores information received by receiver 11 or information generated by controller 20. For example, storage unit 30 stores traffic line information (people flow information) 31 indicating a flow of people moving in the store, area information 32 indicating a range of each of a plurality of areas that constitute the inside of the store, transition probability information 33 indicating a probability of a move of a person between adjacent areas in the store, POS data 34 transmitted from POS terminal device 200, item information 35 indicating locations and prices of items arranged in the store, purchase rate information 36 indicating a purchase rate of an item, and average sales amount per customer information 37]; Examiner notes that transition probability is comparable to action probability),
the movement probability is a probability that a person in a predetermined area at a predetermined time is going to move to the movement destination area (Fig. 1[element 30]; ¶0040[Storage unit 30 stores information received by receiver 11 or information generated by controller 20. For example, storage unit 30 stores traffic line information (people flow information) 31 indicating a flow of people moving in the store, area information 32 indicating a range of each of a plurality of areas that constitute the inside of the store, transition probability information 33 indicating a probability of a move of a person between adjacent areas in the store, POS data 34 transmitted from POS terminal device 200, item information 35 indicating locations and prices of items arranged in the store, purchase rate information 36 indicating a purchase rate of an item, and average sales amount per customer information 37]; Examiner notes that transition probability is comparable to movement probability), and
the population of the population distribution data is further determined for each time (Figs. 1-2; ¶0042[Controller 20 includes traffic line information generator 21 that identifies a person shown in images taken by monitoring camera 100 and generates traffic line information 31 indicating, on a time-series basis, locations of the person thus identified, transition probability information generator (people flow model generator) 22 that generates transition probability information 33 from traffic line information 31 and area information 32, purchase rate information generator (purchase model generator) 23 that generates purchase rate information 36 based on item information 35, POS data 34, area information 32, and traffic line information 31]; Examiner notes that a time-series basis is comparable to each time).
Regarding Claim 3, Itoh in view of Paulrajan teaches the action predicting device according to claim 1, Itoh further discloses
wherein the action probability is a probability for each attribute of a person (Figs. 5A [Examiner notes that each column in Fig. 5A is comparable to an attribute of each person]; ¶0047[FIG. 5A shows an example of traffic line information 31 generated by identification, from images, of people moving in the store as shown in FIG. 3 and FIG. 4. Traffic line information 31 includes identification information (ID) on a person identified in the images, and information indicating where the person is located (an X coordinate and a Y coordinate) on a time-series basis.]; Examiner notes that the coordinates, date, and time associated with a person are comparable to attributes of a person),
the movement probability is a probability for each attribute of a person (Figs. 5A; ¶0047[FIG. 5A shows an example of traffic line information 31 generated by identification, from images, of people moving in the store as shown in FIG. 3 and FIG. 4. Traffic line information 31 includes identification information (ID) on a person identified in the images, and information indicating where the person is located (an X coordinate and a Y coordinate) on a time-series basis.]),
the population of the population distribution data is further determined for each attribute of a person (Figs. 5A; ¶0051[Furthermore, the example where the transition probability is calculated based on all people located in the move origin area has been given. This indicates that a transition probability of a move from the move origin area is constant for all visiting customers. For a more precise simulation, it is also effective that a transition probability is obtained for each specific customer group. In this case, the number of people located in an area is obtained for each customer group, and each transition probability is calculated. For such a customer group, it is effective that a customer stratum (housewives, white-collar workers, or blue-collar workers) estimated from a gender, an age, clothing, or the like is used. Alternatively, it is also effective that customer groups similar to each other in a certain respect such as a purchased item, a behavior in the store, or the like are automatically grouped by a clustering method such as k-means clustering based on POS data and a traffic line from entrance to the store until payment at a checkout.]), and
the processing circuitry is configured to predict a population involved in actions for each movement destination area and each attribute of a person (Fig. Fig. 4[showing each area] and Figs. 5A-5C[showing attributes of each person]; ¶0046[FIG. 4 is an enlarged view of a part of the inside of the store shown in FIG. 3. FIG. 4 shows a situation that a person identified as a person ID “0001” in images moves from the area “A-01” to the area “A-02”, a person identified as a person ID “0002” moves from the area “A-01” to the area “A-03”, and no one moves from the area “A-01” to the area “A-04”. FIG. 4 further shows a situation that item shelf S1 including item A is disposed in the area “A-01”, and item shelf S2 including item B and item C is disposed in the area “A-02”] in view of ¶0051; Examiner notes that the persons are comparable to population data).
Regarding Claim 5, Itoh in view of Paulrajan teaches the action predicting device according to claim 1, Itoh further discloses
wherein the processing circuitry is further configured to predict a probability that a person is going to be involved in actions based on the predicted population involved in actions (Fig. 1[element 30]; ¶0040[Storage unit 30 stores information received by receiver 11 or information generated by controller 20. For example, storage unit 30 stores traffic line information (people flow information) 31 indicating a flow of people moving in the store, area information 32 indicating a range of each of a plurality of areas that constitute the inside of the store, transition probability information 33 indicating a probability of a move of a person between adjacent areas in the store, POS data 34 transmitted from POS terminal device 200, item information 35 indicating locations and prices of items arranged in the store, purchase rate information 36 indicating a purchase rate of an item, and average sales amount per customer information 37] in view of ¶0064[a transition probability and a purchase rate of an item are obtained based on a change by time of day and a customer group, which makes it possible to execute a simulation with high accuracy]; Examiner notes that transition probability is comparable to action probability, and a customer group is comparable to a population).
Regarding Claim 9, Itoh in view of Paulrajan teaches the action predicting device according to claim 2, Itoh further discloses
wherein the action probability is a probability for each attribute of a person (Figs. 5A [Examiner notes that each column in Fig. 5A is comparable to an attribute of each person]; ¶0047[FIG. 5A shows an example of traffic line information 31 generated by identification, from images, of people moving in the store as shown in FIG. 3 and FIG. 4. Traffic line information 31 includes identification information (ID) on a person identified in the images, and information indicating where the person is located (an X coordinate and a Y coordinate) on a time-series basis.]; Examiner notes that the coordinates, date, and time associated with a person are comparable to attributes of a person),
the movement probability is a probability for each attribute of a person (Figs. 5A; ¶0047[FIG. 5A shows an example of traffic line information 31 generated by identification, from images, of people moving in the store as shown in FIG. 3 and FIG. 4. Traffic line information 31 includes identification information (ID) on a person identified in the images, and information indicating where the person is located (an X coordinate and a Y coordinate) on a time-series basis.]),
the population of the population distribution data is further determined for each attribute of a person (Figs. 5A; ¶0051[Furthermore, the example where the transition probability is calculated based on all people located in the move origin area has been given. This indicates that a transition probability of a move from the move origin area is constant for all visiting customers. For a more precise simulation, it is also effective that a transition probability is obtained for each specific customer group. In this case, the number of people located in an area is obtained for each customer group, and each transition probability is calculated. For such a customer group, it is effective that a customer stratum (housewives, white-collar workers, or blue-collar workers) estimated from a gender, an age, clothing, or the like is used. Alternatively, it is also effective that customer groups similar to each other in a certain respect such as a purchased item, a behavior in the store, or the like are automatically grouped by a clustering method such as k-means clustering based on POS data and a traffic line from entrance to the store until payment at a checkout.]), and
the processing circuitry is configured to predict a population involved in actions foreach movement destination area and each attribute of a person (Fig. Fig. 4[showing each area] and Figs. 5A-5C[showing attributes of each person]; ¶0046[FIG. 4 is an enlarged view of a part of the inside of the store shown in FIG. 3. FIG. 4 shows a situation that a person identified as a person ID “0001” in images moves from the area “A-01” to the area “A-02”, a person identified as a person ID “0002” moves from the area “A-01” to the area “A-03”, and no one moves from the area “A-01” to the area “A-04”. FIG. 4 further shows a situation that item shelf S1 including item A is disposed in the area “A-01”, and item shelf S2 including item B and item C is disposed in the area “A-02”] in view of ¶0051; Examiner notes that the persons are comparable to population data).
Regarding Claim 10, Itoh discloses a method, implemented by processing circuitry of an action predicting device, comprising (Fig. 1; Abstract [A simulation device includes an acquisition unit that acquires people flow information indicating a flow of people moving in a facility such as a store]):
storing an action probability, which is a probability that a person in a predetermined area is going to be involved in actions, and a movement probability, which is a probability that a person in a predetermined area is going to move to a movement destination area that is an area of a movement destination (Fig. 1[element 30]; ¶0040[Storage unit 30 stores information received by receiver 11 or information generated by controller 20. For example, storage unit 30 stores traffic line information (people flow information) 31 indicating a flow of people moving in the store, area information 32 indicating a range of each of a plurality of areas that constitute the inside of the store, transition probability information 33 indicating a probability of a move of a person between adjacent areas in the store, POS data 34 transmitted from POS terminal device 200, item information 35 indicating locations and prices of items arranged in the store, purchase rate information 36 indicating a purchase rate of an item, and average sales amount per customer information 37]);
acquiring population distribution data regarding a population for each area (Fig. 4[showing each area] and Figs. 5A-5C; ¶0046[FIG. 4 is an enlarged view of a part of the inside of the store shown in FIG. 3. FIG. 4 shows a situation that a person identified as a person ID “0001” in images moves from the area “A-01” to the area “A-02”, a person identified as a person ID “0002” moves from the area “A-01” to the area “A-03”, and no one moves from the area “A-01” to the area “A-04”. FIG. 4 further shows a situation that item shelf S1 including item A is disposed in the area “A-01”, and item shelf S2 including item B and item C is disposed in the area “A-02”] in view of ¶0042 disclosing the acquiring step; Examiner notes that the persons are comparable to population data);
predicting a population involved in actions for each movement destination area based on the acquired population distribution data, the stored action probability and the stored movement probability (Fig. 6; ¶¶0048-0051[Transition probability information generator 22 first selects one of the areas in the store (S601) and searches for an area to which a move is allowed from the area thus selected (move origin area) based on area information 32 (S602). For example, with respect to the move origin area “A-01” shown in FIG. 4, the move allowable areas “A-02”, “A-03”, “A-04” are found. Transition probability information generator 22 extracts all people located in the move origin area based on traffic line information 31 and area information 32 (S603). A person located in each of the areas can be extracted based on a location of the person (an X coordinate and a Y coordinate) indicated by traffic line information 31 and a range of the area (an X coordinate, a Y coordinate, a width, and a height) indicated by area information 32. For example, two people located in the move origin area “A-01” are extracted (the number of people N=2). Transition probability information generator 22 searches for move destination areas to which the people located in the move origin area “A-01” have moved based on traffic line information 31 and area information 32 (S604). The move destination areas to which the people have moved can be found based on locations of the people indicated by traffic line information 31 (X coordinates and Y coordinates) and ranges of areas (X coordinates, Y coordinates, widths, and heights) indicated by area information 32. In the example shown in FIG. 4, “A-02” and “A-03” are found as move destination areas. Transition probability information generator 22 calculates a transition probability for each of move destination allowable areas from “transition probability P=the number of people who move to move destination allowable areas/the number of people N located in the move origin area (S605)]); and
based on a prediction result sending information related to the store to a mobile terminal (Fig. 1; ¶¶0065-0067[simulation system 1 of the present disclosure executes a simulation of calculating a predicted value of a sales amount (an average sales amount per customer) of a store when a current shopping pattern (a transition probability and a purchase rate) is modeled from traffic line information 31 acquired from images taken by monitoring camera 100, POS data 34 acquired from POS terminal device 200, area information 32 on an inside of a store, and item information 35, and a flow of people, a layout of the store, or a location of an item is changed on the model.]).
Although Itoh discloses a prediction result and sending information related to a store, Itoh does not explicitly disclose creating a geofence around a movement destination area and guiding a person to a store by sending information to a device of the person when the person checks into the geofence.
However, Paulrajan teaches creating a geofence and guiding a person to a store by sending information related to the store to the device of a person when the person checks into a geofence (Fig. 5G; ¶0048[Additionally, or alternatively, when a user selects a product to be placed on a wish-list, virtual reality control device 230 may delay providing the customized virtual reality user environment for content regarding the product until the product becomes available for purchase at a brick-and-mortar store within a threshold distance of the user's location (e.g., that may be determined based on information provided by virtual reality device 210, based on a user-established geo-fence, etc.). For example, a user may configure a geo-fence for a particular location (e.g., around the user's house, around a route that the user intends to use, etc.), and may associate the geo-fence with a wish-list of products to be purchased. In this case, virtual reality control device 230 may provide an indication of the wish-list of products to one or more physical stores located within the geo-fence, and, upon receiving a notification, from a particular physical store of the one or more physical stores, that a particular product of the wish-list of products is available, may provide a customized virtual reality user environment for purchasing the particular product, for directions to the particular physical store, or the like.]).
The method of Paulrajan is applicable to the method of Itoh as they share characteristics and capabilities, namely, they are both targeted to providing online information related to a store. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction result and information related to a store as disclosed by Itoh to include creating a geofence and guiding a user to a store as taught by Paulrajan. One of ordinary skill in the art would have been motivated to expand the method of Itoh in order to generate a customized virtual reality user environment based on a particular nearby store or based on another store location (¶0040).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Itoh in view of Paulrajan in view of Willen et al. (US 2004/0225556 A1 [previously cited]).
Regarding Claim 4, Itoh in view of Paulrajan teaches the action predicting device according to claim 1, Itoh further discloses
wherein the action probability is a probability (Fig. 1[element 30]; ¶0040[Storage unit 30 stores information received by receiver 11 or information generated by controller 20. For example, storage unit 30 stores traffic line information (people flow information) 31 indicating a flow of people moving in the store, area information 32 indicating a range of each of a plurality of areas that constitute the inside of the store, transition probability information 33 indicating a probability of a move of a person between adjacent areas in the store, POS data 34 transmitted from POS terminal device 200, item information 35 indicating locations and prices of items arranged in the store, purchase rate information 36 indicating a purchase rate of an item, and average sales amount per customer information 37]),
the processing circuitry is further configured to acquire data (Fig. 1[element 30]; ¶0040[Storage unit 30 stores information received by receiver 11 or information generated by controller 20. For example, storage unit 30 stores traffic line information (people flow information) 31 indicating a flow of people moving in the store, area information 32 indicating a range of each of a plurality of areas that constitute the inside of the store, transition probability information 33 indicating a probability of a move of a person between adjacent areas in the store, POS data 34 transmitted from POS terminal device 200, item information 35 indicating locations and prices of items arranged in the store, purchase rate information 36 indicating a purchase rate of an item, and average sales amount per customer information 37]), and
the processing circuitry is configured to make predictions further based on the acquired data (Fig. 6; ¶¶0048-0051[Transition probability information generator 22 first selects one of the areas in the store (S601) and searches for an area to which a move is allowed from the area thus selected (move origin area) based on area information 32 (S602)… Transition probability information generator 22 calculates a transition probability for each of move destination allowable areas from “transition probability P=the number of people who move to move destination allowable areas/the number of people N located in the move origin area (S605)]).
Although Itoh discloses an action probability, acquiring data, and making predictions based on the acquired data, Itoh in view of Paulrajan does not explicitly teach a probability for each weather, acquiring weather data related to weather, and making predictions based on the acquired weather data.
However, Willen et al., hereinafter, Willen, teaches a probability for weather, acquiring weather data and making predictions based on the acquired data (Fig. 1; ¶0072[The present invention provides a system for forecasting weather-based demand. FIG. 1 is a block diagram of a system 100 for forecasting weather-based demand. System 100 comprises a recombination processor 102, optionally a volatility scaling processor 104, and optionally a deaggregation processor 106. Recombination processor 102 can receive input data from weather metric data 108 and weather factor relationship knowledgebase 110 and can produce normalized weather factor metric data 112. Normalized weather factor metric data 112 can be indicative of weather-based demand.]).
The system of Willen is applicable to the system of Itoh in view of Paulrajan as they share characteristics and capabilities, namely, they are all targeted to predicting customer behavior. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the probability and prediction determination as taught by Itoh in view of Paulrajan to include weather-based behavior as taught by Willen. One of ordinary skill in the art would have been motivated to expand the system of Itoh in view of Paulrajan in order to forecast the weather-based demand (Abstract).
Response to Arguments
Applicant’s arguments on pages 5-7 of the remarks filed 12/23/2025, with respect to the previous 35
USC § 101 rejections have been fully considered but are not persuasive.
Applicant argues on pages 5-7 of the remarks that the amended claims are not directed to methods of organizing human activity. Examiner respectfully disagrees. According to the MPEP 2106.04, the question of whether a claim is “directed to” a judicial exception in Step 2A is now evaluated using a two-prong inquiry. Prong One asks if the claim “recites” an abstract idea, law of nature, or natural phenomenon. Under that prong, the mere inclusion of a judicial exception such as a method of organizing human activity in a claim means that the claim “recites” a judicial exception (see MPEP 2106.04 [“The mere inclusion of a judicial exception such as a mathematical formula (which is one of the mathematical concepts identified as an abstract idea in MPEP § 2106.04(a)) in a claim means that the claim "recites" a judicial exception under Step 2A Prong One.”]). Additionally, MPEP 2106.04 instructs examiners to refer to the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) (i.e., mathematical concepts, certain methods of organizing human activities, and mental processes) in order to identify abstract ideas. As noted above and in the previous office action, the claims recite item recommendation based on predictions. This is an abstract idea because it is a concept of business relations which makes it a method of organizing human activity (i.e., one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2)).
Applicant argues on pages 5-7 of the remarks that the amended claims that the amended claims integrate the abstract idea into a practical application and provide a technological improvement. Examiner respectfully disagrees. An action predicting, comprising: store an action probability, which is a probability that a person in a predetermined area is going to be involved in actions, and a movement probability, which is a probability that a person in a predetermined area is going to move to a movement destination area that is an area of a movement destination; acquire population distribution data regarding a population for each area; predict a population involved in actions for each movement destination area based on the acquired population distribution data, the stored action probability and the stored movement probability; and create a around a movement destination area based on a prediction result and guide a person to a store by sending information related to the store to the person when the person checks are all part of the abstract idea. The mere execution of the abstract idea on generic components which are recited at a high level does not amount to integrating the abstract idea into a practical application or provide a technical improvement. The components of a device, processing circuitry, geofence, and mobile terminal are described at a high level and as generic in the instant specification, see ¶0018, ¶0071, ¶0095, and Fig. 21.
Accordingly, Examiner maintains that the invention is directed to a judicial exception without
significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the 35 USC §101 rejections are maintained.
Applicant’s arguments on pages 7-9 of the remarks filed 12/23/2025, with respect to the previous 35 USC § 102/103 rejections have been fully considered but are mostly moot in view of the new 103 rejection of the amended claims.
Applicant argues on pages 8-9 that Itoh fails to disclose “store an action probability, which is a probability that a person in a predetermined area is going to be involved in actions, and a movement probability, which is a probability that a person in a predetermined area is going to move to a movement destination area that is an area of a movement destination; acquire population distribution data regarding a population for each area; predict a population involved in actions for each movement destination area based on the acquired population distribution data, the stored action probability and the stored movement probability; and create a geofence around a movement destination area based on a prediction result and guide a person to a store by sending information related to the store to a mobile terminal of the person when the person checks into the geofence” as recited in amended claim 1. Examiner respectfully disagrees.
Itoh discloses a storage unit that stores information such as people flow, area categories, and transition probability which is the probability of a move of a person from one area to another in a store, see ¶0040. Examiner notes that storing people flow, area categories and transition probability as described by Itoh is comparable to storing an action probability that is a probability that a person in an area is going to be involved in actions and a movement probability which indicates that a person in an area will move to a destination area as stated in amended claim 1.
Furthermore, Itoh discloses identifying individuals, items, and area sections as individuals move from one area to another. Itoh ascribes identifiers to the various areas, items, and individuals moving in the store, see ¶¶0042-0046. The aforementioned step is comparable to acquiring population distribution data regarding a population of each area.
Furthermore, Itoh discloses making a prediction regarding a population involved in actions for movement areas via a “transition probability information generator”, see ¶¶0048-0051. Itoh describes that a transition probability information generator determines “move allowable areas” within a store based on customer and area locations which are extracted, identified, and stored. The probability generator of Itoh uses the extracted information to determine a transition probability for the customer which is comparable to predicting a population involved in actions for each movement destination area based on acquired information.
Lastly, Itoh describes a transition probability and calculating a predicted value of sales which is comparable to a prediction result, see ¶¶0065-0067. Itoh does not explicitly disclose the newly added limitation of “create a geofence around a movement destination area and guide a person to a store by sending information to a device of the person when the person checks into the geofence”. However, the newly added reference Paulrajan teaches creating a geofence and guiding a person to a store via information transmitted to their device, see ¶0048 and Fig. 5G.
The system of Paulrajan is applicable to the system of Itoh as they share characteristics and capabilities, namely, they are both targeted to providing online information related to a store. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction result and information related to a store as disclosed by Itoh to include creating a geofence and guiding a user to a store as taught by Paulrajan.
As per MPEP 2111, the pending claims must be given their broadest reasonable interpretation consistent with the specification. Applicant’s arguments regarding amended claim 1 are a narrow interpretation of the claim. Furthermore, according to the MPEP 2111.01(II), it is improper to import claim limitations from the specification when interpreting the claims under broadest reasonable interpretation.
Accordingly, references Itoh and Willen have been maintained and reference Paulrajan has been added in view of the claim amendments.
Conclusion
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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/AHOORA LADONI/ Examiner, Art Unit 3689
/MARISSA THEIN/ Supervisory Patent Examiner, Art Unit 3689