JP2016167172A - Information processing method, information processing system, information processor and program thereof - Google Patents

Abstract

PROBLEM TO BE SOLVED: To estimate a person who decides to buy a commodity when the commodity is bought by a group of plural customers.SOLUTION: The information processing method includes; estimating a group of customers based on a relationship among the plural persons (step S201); estimating the level of interest to the commodity with respect to each of the customers (step S202); and outputting the estimation result whether the selection and decision to buy the commodity was made by a single person or plural persons in the group based on the estimated interest level to the commodity (step S203).SELECTED DRAWING: Figure 3

Description

  The present invention relates to an information processing method, an information processing system, an information processing apparatus, and a program for the customer and product.

  In stores and commercial facilities, membership cards and point-of-sale (POS) systems are used for marketing by collecting and analyzing data about when and what customers have selected (purchased) which products at which stores. ing.

  For example, Patent Document 1 discloses an invention for estimating who has decided to purchase a product for each purchased product when the product is purchased by a group of a plurality of people.

JP 2010-20718 A

  In the above-mentioned Patent Document 1, it can be understood who has decided to purchase in a group when purchasing a product in the group. However, when purchasing a product in a group, in addition to the case where one person decides to purchase, there is a case where a plurality of people in the group consult to decide the purchase.

  When you purchase a product in a group, if you know whether it was decided by the group's consensus or a part of the group, it will help you plan new products. For example, if it is understood that there are many cases where two couples consult and purchase products, data useful for planning products for two people or products of preference can be obtained. If it is understood that there are many cases where a family of three or four people consults and purchases products, data useful for planning products for families and products of preference can be obtained.

  In order to solve the conventional problem, an information processing method according to the present invention includes a step of estimating a group from a correlation of a plurality of persons in a group estimation unit, and an interest degree estimation unit estimating an interest in a product for each person. And, in the analysis means, for the product selected in the estimated group, outputting whether or not the selection of the product is determined by a plurality of people in the group from the degree of interest regarding the product estimated for each person, and It is equipped with.

  According to this, it is possible to know whether or not the products selected in the group have been decided by a plurality of people in the group, which is useful for planning new products, for example.

  For example, if it is found that a certain confectionery is often purchased by a plurality of people in a group, reference data for planning a product for a plurality of people such as a product for a family or a product for a couple can be acquired.

Overall configuration diagram of information processing system according to Embodiment 1 Detailed configuration diagram of information processing system according to the first embodiment Conceptual flowchart of the information processing system of the first embodiment Detailed flowchart of the information processing system of the first embodiment The figure explaining the correlation of the visitor person of this Embodiment 1. The figure explaining the interest degree with respect to the goods of the visitor visitor of this Embodiment 1. The figure explaining the analysis method of the purchase decision person of the goods of this Embodiment 1. The figure explaining another analysis method of the purchase decision person of goods of this Embodiment 1 The figure explaining the merchandise data containing the group attribute of this Embodiment 1 The figure explaining the customer data containing the group attribute of this Embodiment 1. Configuration diagram of information processing system according to the second embodiment Configuration diagram of another information processing system according to the third embodiment

  The first invention includes a step of estimating a group from a correlation of a plurality of persons in a group estimation unit, a step of estimating an interest in the product for each person in an interest level estimation unit, and a selection in the estimated group in an analysis unit The information processing method includes a step of outputting whether or not the selection of the product is determined by a plurality of people in the group based on the degree of interest regarding the product estimated for each person.

  According to this, it is possible to know whether or not the products selected in the group have been decided by a plurality of people in the group, which is useful for planning new products, for example.

  A second invention is an information processing method according to the first invention, in which, when there are a plurality of persons with an interest level related to a product of a predetermined value or more, the selection of the product is determined by a plurality of people in the group. .

  According to this, it turns out that the interest degree regarding goods was selected by several people more than predetermined value.

  According to a third aspect of the present invention, in the first aspect, when there are a plurality of persons having a difference in the degree of interest regarding a commodity within a predetermined value, the selection of the commodity is determined by a plurality of persons in the group. Is the method.

  According to this, it turns out that the difference in the interest level regarding goods was selected by several persons within a predetermined value.

  A fourth invention is an information processing system comprising a customer behavior sensor that detects customer behavior and an information processing device that acquires customer behavior information from the customer behavior sensor. The group selection means for estimating the interest of the product for each person, the interest level estimation means for estimating the interest about the product for each person, and the product selected for the estimated group based on the degree of interest for the product estimated for each person. And an analysis means for outputting whether or not a plurality of persons have decided.

  According to this, it is possible to know whether or not the products selected in the group have been decided by a plurality of people in the group, which is useful for planning new products, for example.

  5th invention is connected with the customer behavior sensor which detects customer behavior, and in the information processor which acquires customer behavior information from a customer behavior sensor, the group estimation means which presumes a group from correlation of a plurality of people, Whether the selection of the product was decided by multiple members of the group based on the interest level estimation means for estimating the interest of the product for each person and the product selected for the estimated group based on the interest level for the product estimated for each person. Is an information processing apparatus.

  According to this, it is possible to know whether or not the products selected in the group have been decided by a plurality of people in the group, which is useful for planning new products, for example.

  A sixth invention is a program for causing a computer to execute the information processing method of the first invention.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the present invention is not limited to the embodiments.

(Embodiment 1)
FIG. 1 is a diagram showing an overall configuration of an information processing system according to Embodiment 1 of the present invention.

  The information processing system shown in FIG. 1 includes a camera 2 that is installed in a store 1 to detect the behavior of a customer 3, a product shelf 7 on which products 6 are displayed, and a POS device 8 that the customer 3 purchases when purchasing the products.

  In the first embodiment, although described in the store 1, it includes scenes and spaces where purchases and contracts of products and services such as commercial facilities are performed.

  Moreover, although the camera 2 demonstrates as a sensor which detects a customer action, it is not restricted to this. For example, a customer's line-of-sight sensor, infrared sensor, ultrasonic sensor, sound sensor, radio wave sensor, temperature sensor, human sensor, etc. may be included, and these may be configured singly or in combination. The POS device 8 may be, for example, a tablet terminal or a portable terminal as long as it performs purchase processing.

  The information processing system includes an information processing device 9 and a storage device 10. The information processing device 9 and the storage device 10 may be connected by a communication line or may be integrated. Moreover, there is no limitation in these installation places, You may prepare in the shop 1. FIG.

  Details of the information processing system will be described below.

  The cameras 2a, 2b, and 2c are customer behavior sensors that are installed in the store 1 to detect customer behavior. Specifically, position information related to the behavior of the customer and information on the speed and direction of movement are acquired.

  For example, the customer behavior sensor 2 (cameras 2a, 2b, and 2c in the present embodiment) detects customer visit and exit behavior, the flow line when moving to the location of the product shelf, its speed and direction, the product shelf. Behavior that stopped at a place, behavior that is looking for or looking carefully at the product, behavior that picked up the product from the product shelf, behavior that returned the product to the product shelf, behavior when lost to purchase, product compared Detecting customer behavior when purchasing a product, such as the type and number of products picked up and conversation between customers.

  Furthermore, it is possible to detect the behavior of the customer at the parking lot or the site of the store 1 and acquire the behavior about the store visit means (walking, bicycle, passenger car), for example.

  The customer behavior sensor 2 also acquires time information related to the customer behavior. For example, the store visit time, the store exit time, the travel time in the store, the time spent in front of the product shelves, and the time lost in purchasing the product.

  The customer behavior information acquired by these customer behavior sensors 2 is obtained for each customer by distinguishing a plurality of customers by using, for example, a camera image and identifying the customer's face and clothes.

  The POS device 8 performs a purchase process on the product 6 brought from the product shelf 7 by the customer 3. That is, calculation, billing and payment of purchase price. By associating the product 6 with the POS device 8, the product selected by the customer (the product to be purchased) is fixed.

  In recent years, membership cards have been increasingly used during purchase processing. Customer information registered in association with the storage medium or identification information by registering customer identification information or customer information in advance in the membership card, which is a storage medium, and reading the membership card into the POS device 8 during purchase processing Are associated with the information of the product purchased this time, and the history is stored for each customer in the store on the store side.

  In this way, the membership card and the POS system can be used for marketing by accumulating and analyzing data collection of when and what customers purchased which products.

  The membership card may be a mobile phone or an information terminal owned by the customer.

  In the purchase behavior as described above, when a customer visits a store with multiple people (group) and purchases a product, who in the group decides to purchase, in particular, multiple people in the group consult to decide the purchase. The data analysis of whether the individual in the group has decided to purchase is very useful for planning new products that target multiple people.

  The operation of the information processing system described above will be described in detail with reference to the detailed flowchart of FIG. 2, the conceptual flowchart of FIG. 3, and the detailed block diagram of FIG.

  In the conceptual flowchart of FIG. 3, in step S201, a group is estimated from the correlation of a plurality of people, in step S202, the interest about the product is estimated for each visitor, and in step S203, for the product selected in the group in step S201, Based on the degree of interest related to the product estimated for each visitor in step S202, it is output whether or not the product has been selected by a plurality of people in the group.

  Note that steps S201 and S202 may be performed in the reverse order of FIG. 3, or steps S201 and S202 may be performed simultaneously or in parallel.

  Details will be described with reference to FIGS.

  In step S1, registration is performed in advance (before visiting the store) regarding customers and products.

  The customer is registered in the customer data storage means 102 in the information processing apparatus 9 in association with the customer identification information and customer information registered in advance in the membership card.

  In addition, regarding the product, the identification information (barcode or wireless tag) of the product 6 and the product information are associated with each other and registered in the product data storage means 105 in the information processing device 9 so that the POS device 8 can perform a purchase procedure. I do.

The merchandise data storage means 105 registers the location information of the merchandise shelves displaying the merchandise so that the customer behavior location detected by the customer behavior sensor 2 such as a camera can be associated with the merchandise. Details will be described later.

  In step S 2, the customer behavior sensor 2 such as a camera senses the purchase behavior (position, time, etc.) of the customer 3 and outputs it to the group customer estimation means 101 and the product interest level estimation means 104 in the information processing apparatus 9. Further, customer identification information and customer information may be acquired by reading the membership card into the POS device 8.

  Here, steps S3 and S4 are performed in parallel.

  First, in step S3, the correlation between customers of the store customers is calculated, and it is estimated whether a plurality of people are group customers or individual customers.

  Therefore, the group customer estimation means 101 acquires position information, movement speed information, movement direction information, time information, and conversation for each customer from the customer behavior sensor 2, and multiplies each by a predetermined weighting factor. Calculate the degree of correlation in the store.

  As a result, for example, the position in the store is within a predetermined distance, the moving speed and the moving direction are close (accompanying), the time difference between the store visit time and the store exit time is within a predetermined time, and the time stopped at the same product shelf location is longer than the predetermined time The degree of correlation between people is obtained in consideration of certain factors.

  Further, when the group customer estimation unit 101 acquires the customer identification information, the group customer estimation unit 101 reads customer information (for example, age, address, family relationship, past visit history) from the customer data storage unit 102 registered in advance. Is multiplied by a predetermined weighting factor to calculate the degree of correlation. Thus, the degree of correlation between people is obtained in consideration of the same age, close address, family, frequency of visiting the store together in the past, and the like.

  As described above, the group customer estimation unit 101 uses the customer information from the customer data storage unit 102 and the customer behavior sensor 2 to determine the location information, the speed and direction information of the customer, and the time information from the customer behavior sensor 2. Estimate whether multiple people are in a group relationship.

  This is shown in FIG. Here, it is assumed that there are four customers (Mr. A, Mr. B, Mr. C, and Ms. D) in a store within a certain time period.

  The correlation degree (0.0-1.0) of the persons calculated by the group customer estimation means 101 as mentioned above is shown. If a combination with a correlation level greater than or equal to a predetermined value (here 0.5 or more) is considered as a group customer, A, C and D are group customers, and B is a different customer from A and others. Is done. Therefore, for example, it is possible that Mr. A, Mr. C, someone of Mr. D, or some of three people decided to purchase a product that Mr. A purchased with the POS device 8 (that is, paid for the purchase price). .

  Next, step S4 calculates the degree of interest in the product for each customer.

  Therefore, the merchandise interest level estimation means 104 reads the location information of the merchandise shelves displaying the merchandise from the pre-registered merchandise data storage means 105. Also, customer behavior information about each customer is obtained from the customer behavior sensor 2 and the degree of correlation between the two pieces of information is calculated. The customer's characteristic behavior detected by the customer behavior sensor 2 is compared with the location of the product shelf.

For example, when a customer is looking for or looking for a product that has a short line of movement or a fast movement speed (moved to the product shelf in a short time), or a long stoppage at the product shelf location. If there is an action that takes a long time or frequently picks up or returns a product, the degree of interest in the product is calculated by multiplying each action by a predetermined weighting factor for each product shelf location or product. To do.

  Further, the product interest level estimation unit 104 reads information on products previously purchased by the customer from the customer data storage unit 102 and information on products with a high level of interest in the past, and multiplies each by a predetermined weighting factor to calculate the interest level. Is calculated.

  As described above, the merchandise interest level estimation means 104 receives the position information, the speed and direction information of each customer from the customer behavior sensor 2 and the merchandise data storage means 105, time information, merchandise shelf location information, From the past history information from the data storage means 102, the degree of interest in the product for each visitor is estimated.

  This is shown in FIG. Here, it is assumed that there are four customers (Mr. A, Mr. B, Mr. C, and Ms. D) in a store within a certain time period.

  The interest level (0.0 to 1.0) for the product calculated by the product interest level estimation unit 104 for the products 1, 2, and 3 as described above is shown. If the interest level is a predetermined value or more (here, 0.5 or more), it is considered that the customer 1 is highly interested in the product 1, the product A is the product of A, B, and C, the product 2 is the product of B and D, the product 3 It is estimated that B and D are highly interested.

  Next, steps S5, S6, and S7 are processes when the customer receives purchase processing at the POS device (purchase processing device) 8.

  The group attribute analysis unit 103 acquires the group correlation (FIG. 5) from the group customer estimation unit 101 and the interest level (FIG. 6) regarding the product from the product interest level estimation unit 104 for the purchase processed customer. Analyze customers who are interested in each product within the same group as the processed customers.

  For example, it is estimated from FIG. 5 that Mr. A, Mr. C, and Mr. D are in the same group, and from FIG.

  As an example, a case where Mr. A purchases (purchases) products 1, 2, and 3 will be described.

  The group attribute analysis unit 103 analyzes the group attribute of the product 1 as shown in FIG. That is, this is a method of comparing the degree of interest in the product 1 by Mr. C and Mr. D who are estimated to be in the same group as Mr. A.

  If there are a plurality of customers whose degree of interest is equal to or greater than a predetermined value (0.5 in this case), it is analyzed that the product has been decided by the consensus of the group. In other words, in FIG. 7, it is assumed that the group attribute of the product 1 is determined to be purchased by Mr. A and Mr. C, and Mr. D is not much involved in it.

  The advantage of this method is that it is analyzed by the high degree of interest (absolute value), so that it can be seen that it has been actively purchased by members of the group.

  Moreover, the group attribute analysis means 103 analyzes a group attribute about the goods 2, for example as shown in FIG. That is, this is a method of comparing the degree of interest in the product 2 of Mr. C and Mr. D, who are estimated to be in the same group as Mr. A.

If there are a plurality of customers having a difference in interest level within a predetermined value (0.2 in this case), it is analyzed that the product has been decided by the collective consensus of the group. In other words, in FIG. 8, it is assumed that the group attribute of the product 2 is determined to be purchased by Mr. A, Mr. C, and Mr. D.

  The advantage of this method is that it is analyzed by the difference (relative value) in the degree of interest, so that it can be seen that the members of the group have been purchased with uniform likability.

  As shown in FIGS. 7 and 8, there are a plurality of group attribute estimation methods, and it is determined in step S8 which estimation method is preferred.

  Therefore, the group attribute determination unit 106 compares a plurality of analysis results and outputs the result as a group attribute if they are the same. If they are different, either is adopted and output as a group attribute.

  Which analysis result is adopted is not shown, but it is conceivable that customer information is obtained from the customer data storage means 102 and product data is obtained from the product data storage means 105 for determination. For example, the age / gender of the customer and the past purchase history are referred to the characteristics and purchase history of the product, and the group attribute determination unit 106 determines that one of a plurality of estimation methods is optimized.

  Further, other information (information on other stores, etc.) may be acquired from the purchase processing device (POS device) 8, and any one of a plurality of estimation methods may determine optimization (or receive an instruction).

  In the present embodiment, the group attribute determination unit 106 is provided and the results of the absolute value analysis and the relative value analysis are selected in step S8. However, the condition is determined in advance and either one is analyzed. (Group attribute determination means 106, step S8 may be omitted). Moreover, the group attribute determination means 106 may be provided with an input means so that a store clerk selects it.

  As described above, the group attribute is determined in step S8, and temporarily output to the storage device 10 as group information in step S9.

  The reason why the provisional output is used here is that the purchase behavior of the customer in the store continues, and the group attribute may need to be reviewed due to the behavior after the purchase processing.

  In step S10, after the purchase processing, as in step S2, the group customer estimation means in the information processing apparatus 9 is sensed by sensing the purchase behavior (position, time, conversation, etc.) of the customer 3 by the customer behavior sensor 2 such as a camera. 101 and the product interest level estimation means 104.

  When it is detected in step S11 that the customer 3 has opened a store, the correlation between the customers of the store visitor is calculated in the same manner as in step S3, and it is determined whether the plurality of people who opened the store are group customers or individual customers. presume.

  In this step S11, the purchase behavior from when the customer visits the store until the store is opened may be collectively performed, or purchase after the purchase processing (payment of payment) is performed until the store is opened. You may carry out about action and may compare with the result of step S3.

  In addition, the degree of product interest may be estimated after the purchase process until the store is opened.

The store opening is detected and the group customer estimation is confirmed, and if it matches the estimation in step S3, the group attribute is determined and output to the storage device 10. (Step S14).

  The store opening is detected and the group customer estimation is confirmed. If it does not coincide with the estimation in step S6, the group customer estimation is recalculated (S12). An analysis is performed (S13), and this is output to the storage device 10 as a group attribute (step S14).

  In addition, even when a customer who has made a purchase action once with the POS device 8 makes a second purchase action, the degree of interest in the product (step S4) is recalculated and added to the group attribute to the storage device 10. Output.

  Except for steps S10 to S14, when the purchase processing is performed by the POS device 8, the group attribute of step S8 may be output to the storage device 10 as confirmed information.

  An example of group information stored in the storage device 10 is shown in FIGS.

  FIG. 9 shows group information recorded for each product.

  You can see whether each product was purchased individually or as a group. Especially for products purchased in a group, the maximum and average values for how many people decided to purchase (for example, how many people can make a packaged product for sale) and the interest level of multiple purchasers , You can see the range of interest (whether the product is actively purchased, whether it is purchased with a favorable rate by the members, etc.).

  Here, there is a case where there is a group purchase by two people for the product 1, and the customer is a man in his 20s and a woman in his 20s (such as a couple). The age and sex of the customer may be identified by the customer's face recognition or clothing recognition using the image of the camera 2 or may be obtained from a membership card read by the POS device 8.

  Further, there is a case where there is a group purchase of three people for the product 2 and the customer is a man in his 30s, a woman in his 20s and a man under 10 (eg, a family). Regarding the customer's age and gender, family composition information may be obtained when obtaining from a membership card, which can be referred to.

  FIG. 10 shows group information recorded for each customer.

  Here, a group of Mr. A, Mr. C, and Mr. D is shown. Each customer data is associated with a membership card, and the gender and age are acquired. In addition, the date and time of visit and the correlation between members can be known.

  In Fig. 5, the correlation between the three people, that is, the correlation between Mr. A and Mr. C, the correlation between Mr. A and Mr. D, and the correlation between Mr. C and D, Is more than a predetermined value (for example, 0.5 or more), it is estimated that Mr. A is in the central position of a group of three people, Mr. C and Mr. D. For example, it can be seen that it is particularly effective to inform Mr. A of a campaign for encouraging a group of three to visit the store.

  The group attribute output unit 106 may output the customer information and the product data history to the customer data storage unit 102 and the product data storage unit 105 for the group information as described above.

  Of course, FIGS. 9 and 10 are examples, and other information obtained, calculated, and analyzed in the middle of the present embodiment may be output and stored as group information.

  Further, a part of the information processing device 9 may be configured to be performed by another device (for example, a server connected by a communication network) apart from the information processing device 9.

  Moreover, you may implement | achieve a part of customer behavior sensor of this Embodiment, an information processing apparatus, a POS apparatus, and a memory | storage device with a computer or a computer program.

(Embodiment 2)
FIG. 11 is a diagram showing an overall configuration of an information processing system according to Embodiment 2 of the present invention.

  Differences from the first embodiment will be described below with reference to FIGS. 1 and 11. Since FIG. 2 and subsequent figures are the same, a detailed description is omitted.

  11 replaces the camera 2 which is a customer behavior sensor in FIG. 1 with a customer 3 having a communication device 4 (for example, a mobile phone, an information terminal, a wireless device), and a communication device 5 provided in the store 1, for example a wireless LAN, By communicating with Bluetooth (registered trademark), the in-store behavior of the customer in step 1 of FIG. 4 is sensed, and estimation and analysis after steps S3 and S4 are performed.

  The customer communication device 4 is provided with individual identification information (for example, wireless ID), and is transmitted to the communication device 5 of the store 1 so that the communication device 5 that has received the identification code is highly reliable for each customer. It can be easily distinguished.

  Further, by using the identification code of the communication device 4, the membership card of the first embodiment is not necessarily required (of course, the identification code of the communication device 4 and the membership card may be used together). By detecting the identification code of the communication device 4 existing in the communication area, the behavior of all customers who have the communication device 4 (mobile phone, information terminal, wireless device) can be identified.

  Further, by determining the reception intensity and directivity of the signal transmitted from the customer communication device 4, the communication device 5 of the store 1 also includes customer behavior such as the position of the customer having the communication device 4, the speed and direction of movement, and the like. Can be detected.

  Further, in FIG. 11, the communication device 5 of the store 1 is drawn as one unit. However, if there are a plurality of the communication devices 5, the communication area with the customer communication device 4 is widened, and the reception, position, and speed of the customer identification code described above. The accuracy of direction detection can be increased.

  As one form, although not shown, it is conceivable that the commodities shelf 7 includes the communication device 5 and communicates with the customer's communication device 4 using, for example, a wireless tag or Bluetooth (registered trademark). It is also conceivable that the product 6 is equipped with a wireless tag and communicates with the customer communication device 4. Thus, the quality and quantity of information communicated between the product shelf 7 and the product 3 and the customer communication device 4 can be increased, and the detection accuracy of the customer behavior sensor (communication device 4) can be increased.

  In addition to the form carried by the customer, the communication device 4 may be in the form of moving together with the customer in preparation for a shopping basket or shopping cart.

(Embodiment 3)
FIG. 12 is a diagram showing an overall configuration of an information processing system according to Embodiment 3 of the present invention.

  Differences from the first embodiment will be described below with reference to FIG. 1 and FIG. Since FIG. 2 and subsequent figures are the same, a detailed description is omitted.

  In FIG. 12, instead of the camera 2 which is a customer behavior sensor in FIG. 1, the walking sensor 11 detects the walking state of the customer with a pressure sensor, a piezoelectric sensor, a sound microphone, a radar, or the like.

  The walking sensor 11 detects customer characteristics (for example, footprint shape, footprint pressure distribution, stride, walking speed, footsteps, etc.) to distinguish each customer, and detects the position, moving speed, and direction of the customer. In addition, gender and age are determined from the customer's characteristics regarding walking. Thereby, the in-store behavior of the customer in step 1 of FIG. 4 is sensed, and estimation and analysis after steps S3 and S4 are performed.

  By using the walking sensor 11, the membership card of the first embodiment and the communication device 4 of the second embodiment are not necessarily required (of course, the membership card, the communication device 4, and the walking sensor may be used in combination). It is possible to detect all customers who have visited the store. In addition, customers with different walks such as wheelchairs, strollers, and canes can be distinguished and detected.

  Purchasing products such as detection of customer visits and exit behaviors from walking detected by the walking sensor 11, flow lines and speeds and directions when moving to the product shelf, and actions stopped at the product shelf Detect customer behavior when you do.

  In addition, although Embodiment 1, 2, and 3 were demonstrated, you may implement in the combination. For example, the customer behavior sensor may combine two or more of a camera, a communication device, and a walking sensor.

  Although the present embodiment deals with the purchase of products by customers in the group, the present invention is not limited to this, and it goes without saying that the present invention is effective for selecting products by a plurality of people.

  As described above, according to the present embodiment, the step of estimating a group from the correlation of a plurality of persons in the group estimation means, the step of estimating the interest regarding the product for each person in the interest level estimation means, and the analysis means And, for the product selected in the estimated group, outputting whether or not the selection of the product is determined by a plurality of people in the group from the degree of interest related to the product estimated for each person. A method can be realized.

  According to this, it is possible to know whether or not the products selected in the group have been decided by a plurality of people in the group, which is useful for planning new products, for example.

  In addition, when there are a plurality of persons whose degree of interest in a product is a predetermined value or more, it is assumed that the selection of the product is determined by a plurality of people in the group.

  According to this, it turns out that the interest degree regarding goods was selected by several people more than predetermined value.

  In addition, when there are a plurality of people whose interest level differences are within a predetermined value, the selection of the product is determined by a plurality of people in the group.

  According to this, it turns out that the difference in the interest level regarding goods was selected by several persons within a predetermined value.

Further, in an information processing system including a customer behavior sensor that detects customer behavior and an information processing device that acquires customer behavior information from the customer behavior sensor, the information processing device is a group that estimates a group from a correlation of a plurality of people. From the estimation means, the interest level estimation means for estimating the interest in the product for each person, and the products selected in the estimated group, the product selection is made by multiple people in the group based on the interest level for the product estimated for each person. An information processing system including analysis means for outputting whether or not the decision has been made can be realized.

  In addition, in an information processing apparatus connected to a customer behavior sensor for detecting customer behavior and acquiring customer behavior information from the customer behavior sensor, a group estimation means for estimating a group from the correlation of a plurality of people, and a product for each person An analysis that outputs whether the selection of a product is decided by multiple members of the group from the interest level estimation means for estimating the interest and the product selected in the estimated group based on the interest level related to the product estimated for each person An information processing apparatus including the means can be realized.

  As described above, the information processing method, the information processing system, the information processing apparatus, and the program thereof according to the present invention include a step of estimating a group from a correlation between a plurality of people, and a step of estimating an interest in a product for each person. And whether the purchase of the product is determined by the group from the degree of interest regarding the product for each person for the product purchased by the group is output as a group attribute.

  In addition, as explained in stores and commercial facilities, it is not limited to these locations, but in some places such as factories, offices, public places such as train stations and vehicles, and private places such as homes, some group attribute is determined from the relationship between human relationships and objects. The present invention can be used for those that output.

1 store 2 camera (customer behavior sensor)
3 customer 4 customer communication device (customer behavior sensor)
5 store communication device 6 product 7 product shelf 8 POS device (purchase processing device)
9 Information processing device 10 Storage device 11 Walking sensor (customer behavior sensor)
101 Group customer estimation means (group estimation means)
103 Group attribute analysis means (analysis means)
104 Product interest level estimation means (interest level estimation means)

Claims (6)

In the group estimation means, a step of estimating a group from a correlation between a plurality of people,
In the degree-of-interest estimation means, estimating the interest regarding the product for each person in the plurality of people,
In the analysis means, for the product selected in the estimated group, from the degree of interest regarding the product estimated for each person, outputting whether or not the selection of the product is determined by a plurality of people in the group;
An information processing method comprising:   The information processing method according to claim 1, wherein when there are a plurality of persons having an interest level related to the product of a predetermined value or more, the selection of the product is determined by a plurality of people in the group.   The information processing method according to claim 1, wherein when there are a plurality of people whose difference in the degree of interest regarding the product is within a predetermined value, the selection of the product is determined by a plurality of people in the group. In an information processing system comprising a customer behavior sensor that detects customer behavior and an information processing device that acquires customer behavior information from the customer behavior sensor, the information processing device comprises:
A group estimation means for estimating a group from the correlation of a plurality of people;
Interest level estimation means for estimating interest related to the product for each person in the plurality of people,
For the product selected in the estimated group, from the degree of interest regarding the product estimated for each person, the analysis means for outputting whether the selection of the product is determined by a plurality of people in the group,
Information processing system with In an information processing apparatus that connects to a customer behavior sensor that detects customer behavior and acquires customer behavior information from the customer behavior sensor,
A group estimation means for estimating a group from the correlation of a plurality of people;
Interest level estimation means for estimating interest related to the product for each person in the plurality of people,
For the product selected in the estimated group, from the degree of interest regarding the product estimated for each person, the analysis means for outputting whether the selection of the product is determined by a plurality of people in the group,
An information processing apparatus comprising:   The program which makes a computer perform the information processing method of Claim 1.

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