Patent Publication Number: US-2023140512-A1

Title: Processing apparatus, processing method, and non-transitory storage medium

Description:
TECHNICAL FIELD 
     The present invention relates to a processing apparatus, a processing method, and a program. 
     BACKGROUND ART 
     Patent Document 1 discloses that, in face recognition processing of searching a program video for a person to be searched, sorting that uses a precision indicating a possibility of each performer being captured in a program video is performed. 
     RELATED DOCUMENT 
     Patent Document 
     
         
         [Patent Document 1] Japanese Patent Application Publication No. 2017-33372 
       
    
     DISCLOSURE OF THE INVENTION 
     Technical Problem 
     An inference device generated by machine learning or the like may sometimes give an erroneous inference result. A technique for detecting that there is a possibility that an inference result may be erroneous and correcting the error is desired. Such a technique can prevent inconvenience that subsequent processing is performed based on an erroneous inference result. Patent Document 1 does not disclose such a problem and a solution therefor. 
     The present invention addresses a problem of providing a technique for detecting that there is a possibility that an inference result of an inference device generated by machine learning or the like may be erroneous and correcting the error. 
     Solution to Problem 
     According to the present invention, provided is a processing apparatus including: 
     a classification execution means for executing class classification, based on an inference device; 
     a computation means for computing a precision of a class determined by the class classification; and 
     a processing means for outputting a class determined by the class classification and accepting a correct/incorrect input from an operator when the precision is less than a reference value. 
     Further, according to the present invention, provided is a processing method including: 
     by a computer, 
     executing class classification, based on an inference device; 
     computing a precision of a class determined by the class classification; and 
     outputting a class determined by the class classification and accepting a correct/incorrect input from an operator when the precision is less than a reference value. 
     Further, according to the present invention, provided is a program causing a computer to function as: 
     a classification execution means for executing class classification, based on an inference device; 
     a computation means for computing a precision of a class determined by the class classification; and 
     a processing means for outputting a class determined by the class classification and accepting a correct/incorrect input from an operator when the precision is less than a reference value. 
     Advantageous Effects of Invention 
     According to the present invention, a technique for detecting that there is a possibility that an inference result of an inference device generated by machine learning or the like may be erroneous and correcting the error is achieved. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagram illustrating one example of a hardware configuration of a processing apparatus according to a present example embodiment. 
         FIG.  2    is one example of a function block diagram of the processing apparatus according to the present example embodiment. 
         FIG.  3    is a diagram schematically illustrating one example of information processed by the processing apparatus according to the present example embodiment. 
         FIG.  4    is a diagram schematically illustrating one example of information processed by the processing apparatus according to the present example embodiment. 
         FIG.  5    is a diagram schematically illustrating one example of a screen output by the processing apparatus according to the present example embodiment. 
         FIG.  6    is a diagram schematically illustrating one example of a screen output by the processing apparatus according to the present example embodiment. 
         FIG.  7    is a diagram schematically illustrating one example of a screen output by the processing apparatus according to the present example embodiment. 
         FIG.  8    is a flowchart illustrating one example of a flow of processing of the processing apparatus according to the present example embodiment. 
         FIG.  9    is a flowchart illustrating one example of a flow of processing of the processing apparatus according to the present example embodiment. 
         FIG.  10    is a diagram schematically illustrating one example of information processed by the processing apparatus according to the present example embodiment. 
         FIG.  11    is a diagram schematically illustrating one example of information processed by the processing apparatus according to the present example embodiment. 
         FIG.  12    is a diagram schematically illustrating one example of information processed by the processing apparatus according to the present example embodiment. 
         FIG.  13    is a diagram schematically illustrating one example of information processed by the processing apparatus according to the present example embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     First Example Embodiment 
     Overview 
     First, an overview of processing executed by a processing apparatus according to a present example embodiment will be described. The processing apparatus executes class classification, based on an inference device generated by machine learning or the like, and then computes a precision of a class determined by the class classification, based on a prior distribution of the determined class. Then, when the precision is equal to or more than a reference value, the processing apparatus executes subsequent processing, based on the determined class. On the other hand, when the precision is less than a reference value, the processing apparatus outputs the determined class and accepts a correct/incorrect input from an operator, and executes subsequent processing, based on a correct class determined by the correct/incorrect input. 
     An application scene of the processing apparatus according to the present example embodiment will be described. The processing apparatus according to the present example embodiment is used in accounting processing at a store such as a convenience store or a supermarket. One example of the processing apparatus according to the present example embodiment is a so-called point of sales (POS) register. The processing apparatus executes, based on an image photographed of a purchase target product and an inference device, class classification for determining a product included in the image. Then, the processing apparatus computes a precision of the determined product, based on a prior distribution indicating frequency (purchase frequency) with which the determined product is registered as a purchase target. Then, when the precision is equal to or more than a reference value, a processing apparatus  10  registers the determined product as a purchase target. On the other hand, when the precision is less than a reference value, the processing apparatus  10  outputs a determination result and accepts a correct/incorrect input from an operator, and registers a product determined by the correct/incorrect input as a purchase target. 
     The processing apparatus according to the present example embodiment as described above achieves a technique for detecting that there is a possibility that an inference result of an inference device generated by machine learning or the like may be erroneous and correcting the error. Consequently, inconvenience that subsequent processing is performed based on an erroneous inference result can be prevented. In a case of the above-described application scene, inconvenience that a product different from a product included in an image is registered as a purchase target and accounting processing is performed is prevented. 
     “Hardware Configuration” 
     Next, one example of a hardware configuration of the processing apparatus will be described. Each function unit of the processing apparatus is achieved by any combination of hardware and software, mainly including a central processing unit (CPU) of any computer, a memory, a program to be loaded in a memory, a storage unit (in which a program downloaded from a storage medium such as a compact disc (CD), a server on the Internet, or the like can be stored as well as a program stored in advance in a stage of shipping the apparatus) such as a hard disk for storing the program, and an interface for network connection. Then, it should be understood by a person skilled in the art that there are a variety of modification examples for a method or an apparatus for achieving the same. 
       FIG.  1    is a block diagram illustrating a hardware configuration of the processing apparatus. As illustrated in  FIG.  1   , the processing apparatus includes a processor  1 A, a memory  2 A, an input/output interface  3 A, a peripheral circuit  4 A, and a bus  5 A. The peripheral circuit  4 A includes various modules. The processing apparatus may not include the peripheral circuit  4 A. Note that, the processing apparatus may be configured by a plurality of physically and/or logically separated apparatuses, or may be configured by one physically and/or logically unified apparatus. When the processing apparatus is configured by a plurality of physically and/or logically separated apparatuses, the plurality of individual apparatuses can include the above hardware configuration. 
     The bus  5 A is a data transmission path through which the processor  1 A, the memory  2 A, the peripheral circuit  4 A, and the input/output interface  3 A transmit and receive data to and from one another. The processor  1 A is an arithmetic processing apparatus such as, for example, a CPU or a graphics processing unit (GPU). The memory  2 A is a memory such as, for example, a random access memory (RAM) or a read only memory (ROM). The input/output interface  3 A includes an interface for acquiring information from an input apparatus, an external apparatus, an external server, an external sensor, a camera, and the like, an interface for outputting information to an output apparatus, an external apparatus, an external server, and the like, and the like. The input apparatus is, for example, a keyboard, a mouse, a microphone, a physical button, a touch panel, and the like. The output apparatus is, for example, a display, a speaker, a printer, a mailer, and the like. The processor  1 A can give an instruction to each module to perform an operation, based on an operation result thereof 
     “Function Configuration” 
     Next, a function configuration of the processing apparatus will be described. As illustrated in  FIG.  2   , a processing apparatus  10  includes a storage unit  11 , a classification execution unit  12 , a computation unit  13 , a processing unit  14 , and a registration unit  15 . 
     The storage unit  11  stores various kinds of information necessary for processing in the present example embodiment. For example, the storage unit  11  stores an inference device (estimation model) that determines a product included in an image. The inference device determines a product included in an image. For example, the inference device is generated by machine learning based on training data in which a product image is associated with product identification information (product name, product code, or the like). 
     The classification execution unit  12  executes class classification, based on the inference device. First, the classification execution unit  12  acquires an image photographed of a purchase target product. The photographing is performed by an operator (example: a store clerk or a customer) who executes accounting processing. Then, the classification execution unit  12  determines, by the image and the class classification based on the inference device stored in the storage unit  11 , a product included in the image. The classification execution unit  12  may input the acquired image as is to the inference device, or may input the acquired image to the inference device after predetermined processing (example: noise removal, object detection, background removal, trimming, or the like) is performed thereon. An inference result of the inference device indicates product identification information or the like on a product determined as a product included in an image. Note that, a configuration of a camera for photographing a product, a photographing method, a method of inputting an image to the processing apparatus  10 , or the like is a matter of design choice, and any technique can be employed. 
     The computation unit  13  computes a precision of a class determined by the class classification of the classification execution unit  12 . In other words, the computation unit  13  computes a precision of a product determined as a product included in an image. 
     Herein, a detail of processing of computing a precision will be described. The computation unit  13  computes a precision of a class (product) determined by class classification, based on a prior distribution of the class. Specifically, the computation unit  13  computes a precision P, based on a following equation (1). 
     
       
         
           
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     R is a recall, and S is specificity. In the present example embodiment, the recall R and the specificity S are fixed values. In other words, the recall R and the specificity S are identical values, regardless of an environment (time of day, weather, or the like) at a time of accounting processing or a result of class classification. p is a prior distribution. In the present example embodiment, the prior distribution p indicates frequency with which a determined product is registered by the processing apparatus  10  as a purchase target. The prior distribution p is computed based on a registration history of each product at a time of accounting processing for the product. 
     In other words, the computation unit  13  retrieves a value of the recall R and the specificity S stored in advance in the storage unit  11 . Further, the computation unit  13  computes the latest prior distribution p of a determined product, based on a registration history of each product at a time of accounting processing for the product. Then, the computation unit  13  inputs, to the above equation (1), the retrieved recall R and the specificity S and the computed prior distribution p to compute the precision P. 
     Herein, the above equation (1) will be described. First, following parameters are defined. 
     TP: true positive: the number of correct answers inferred to be correct
 
FP: false positive: the number of incorrect answers inferred to be correct
 
FN: false negative: the number of correct answers inferred to be incorrect
 
TN: true negative: the number of incorrect answers inferred to be incorrect
 
     In general, it is known that the precision P, the recall R, the specificity S, and the prior distribution p are computed by following equations (2) to (5). The above equation (1) is acquired by modifying the equations.
     [Mathematical 2]   

         P=TP /( TP+FP )  Equation (2)
 
       [Mathematical 3] 
         R=TP /( TP+FN )  Equation(3)
 
       [Mathematical 4] 
         S=TN /( TN+FP )  Equation (4)
 
       [Mathematical 5] 
         p =( TP+FN )/( TP+FN+TN+FP )  Equation (5)
 
     Next, setting the recall R and the specificity S as fixed values and a method of determining the value will be described. 
     The recall R and the specificity S vary depending on difficulty of recognition by an inference device. The difficulty of recognition largely depends on how an inference target included in an image is captured, and differs depending on, for example, an optical environment (light from outside, indoor light, type of indoor light, a relative relationship (distance, direction, or the like) between light and a subject, or the like) at a time of photographing, a relative relationship (distance, direction, or the like) between an inference target (subject) and a camera, and the like. 
     However, in a case of the present example embodiment in which the processing apparatus  10  is used in accounting processing at a store, it can be assumed that the difficulty of recognition is invariable regardless of an execution environment. In other words, in a case of the present example embodiment, light giving influence on photographing is indoor light, and influence of light from outside is ignorable. Then, intensity or the like of indoor light is generally invariable regardless of time or the like. Further, a room light or a camera for use in accounting processing is generally fixed at a predetermined position, and thus, a relative relationship (distance, direction, or the like) therebetween is generally invariable regardless of time or the like. Furthermore, a method of photographing a purchase target product with a camera at a time of accounting processing is manualized, and thus, a relative relationship (distance, direction, or the like) between an inference target (subject) and a camera does not largely vary even with some variation by each operator. 
     The present inventor newly found that the present example embodiment is in such an environment where the recall R and the specificity S can be assumed as invariable, and newly conceived of setting the values as fixed values. 
     A value of the recall R and the specificity S can be computed, for example, based on a result of a test for the class classification of the classification execution unit  12 , executed at a predetermined place before the processing apparatus  10  is actually used at a store, and the above equations (3) and (4). A place for executing the test is preferably a store, but may be another place. 
     Next, a method of computing the prior distribution p will be described. In a case of the present example embodiment in which the processing apparatus  10  is used in accounting processing at a store, the prior distribution p indicates frequency (purchase frequency) with which a product determined by class classification is registered as a purchase target. 
     For example, the prior distribution p in the present example embodiment is a ratio of “the number of times registering a product determined by class classification as a purchase target” to “the total number of times registering individual products being handled at a store as a purchase target”. “Registration as a purchase target” herein is processing achieved by the registration unit  15  to be described below, and is registration as a purchase target with an accounting apparatus such as a POS register at a time of accounting processing. 
     The storage unit  11  stores prior distribution information in which product identification information on individual products being handled at a store is associated with the number of times each product is registered as a purchase target, as illustrated in  FIG.  3   . The prior distribution information is updated every time accounting processing is performed. Then, the computation unit  13  computes the above-described prior distribution p, based on the prior distribution information. 
     Note that, “the total number of times registering individual products being handled at a store as a purchase target” may be the total number of times registering all of products being handled at the store. 
     Besides the above, “the total number of times registering individual products being handled at a store as a purchase target” may be the total number of times registering a part of products being handled at the store. A part of products can be, for example, a group of products (a product group having a similar appearance) that have a similar appearance to a product determined by class classification and are relatively difficult to distinguish from each other by the class classification of the classification execution unit  12 . A group of products that are relatively difficult to distinguish from each other by class classification may be, for example, a group of products (beverages, shampoos, toothpastes, or the like) of a same kind, may be a group of products of a same kind of a same manufacturer, may be a group of products of a same series of a same manufacturer, or may be others. 
     The storage unit  11  may store group information indicating a group of products that are relatively difficult to distinguish from each other by the class classification of the classification execution unit  12 , as illustrated in  FIG.  4   . Then, the computation unit  13  may determine, based on the group information, a product associated with a same group number as a product determined by class classification, and may compute the total “number of times registering a product (including a product determined by class classification) associated with a same group number as a product determined by class classification, as “the total number of times registering individual products being handled at a store as a purchase target”. 
     Note that, in the present example embodiment, the frequency is frequency with which each product is registered as a purchase target at one store in which the processing apparatus  10  is installed. In other words, the frequency indicates purchase frequency of each product at the store in which the processing apparatus  10  is installed. As a modification example, the frequency may be frequency with which the product is registered as a purchase target at a group of a plurality of stores. In other words, the frequency may be purchase product frequency of each product at all of a plurality of stores. A group of a plurality of stores is preferably a group of stores having a similar tendency of a product purchased by a customer, and examples thereof include, for example, a group of stores having a similar location condition (example: there is a station within 100 m, there is a university within 100 m, or the like). 
     Returning to  FIG.  2   , the processing unit  14  outputs a class (product) determined by the class classification of the classification execution unit  12  and accepts a correct/incorrect input from an operator when the precision P computed by the computation unit  13  is less than a reference value. The processing unit  14  outputs the information toward an operator who executes accounting processing, and accepts a correct/incorrect input from the operator. Output of information is achieved via any output apparatus such as a display, a speaker, or a projection apparatus. Further, acceptance of an input is achieved via any input apparatus such as a touch panel, a physical button, a microphone, a mouse, or a keyboard. 
     For example, the processing unit  14  may display a screen as illustrated in  FIG.  5    on a touch panel display, and may accept an input. On the screen illustrated in  FIG.  5   , information identifying a product determined by the class classification of the classification execution unit  12  is displayed, and an input answering whether the information is correct is accepted. Then, when accepting an input “No” on the screen, the processing unit  14  may display a screen as illustrated in  FIG.  6    on a touch panel display, and may accept an input. On the screen illustrated in  FIG.  6   , a list of products being handled at a store is displayed, and an input specifying one of the products is accepted. 
     Besides the above, the processing unit  14  may display a screen as illustrated in  FIG.  7    on a touch panel display, and may accept an input. On the screen illustrated in  FIG.  7   , a part of a plurality of products selected from among products being handled at a store is displayed as candidates for a purchase target product. Then, on the screen, an input specifying one of the candidates is accepted. When accepting an input “None of them” on the screen, the processing unit  14  may display the screen as illustrated in  FIG.  6    on a touch panel display, and may accept an input. 
     Herein, one example of a method of selecting a candidate for a product to be displayed on the screen illustrated in  FIG.  7    will be described. 
     (Selection method 1) A predetermined number of products are selected in descending order of a value of a prior distribution at that point in time.
 
(Selection method 2) A product that is included in a predetermined number of products in descending order of a value of a prior distribution at that point in time and that has a value of the prior distribution equal to or more than a reference value is selected.
 
(Selection method 3) A product determined by the class classification of the classification execution unit  12  is selected in addition to a product selected by selection method 1 or 2.
 
(Selection method 4) A predetermined number of products are selected in descending order of a value of a prior distribution at that point in time from among products associated with a same group number as a product determined by the class classification of the classification execution unit  12 .
 
(Selection method 5) A product that is included in a predetermined number of products in descending order of a value of a prior distribution at that point in time among products associated with a same group number as a product determined by the class classification of the classification execution unit  12  and that has a value of the prior distribution equal to or more than a reference value is selected.
 
(Selection method 6) A product determined by the class classification of the classification execution unit  12  is selected in addition to a product selected by selection method 4 or 5.
 
(Selection method 7) When an inference device outputs a posterior distribution, a product is selected after replacing a value of a prior distribution for a value of the posterior distribution in selection methods 1 to 6.
 
     Returning to  FIG.  2   , the registration unit  15  registers a product determined by the class classification of the classification execution unit  12  as a purchase target when the precision P computed by the computation unit  13  is equal to or more than a reference value. Then, the registration unit  15  registers a product determined by a correct/incorrect input from an operator accepted by the processing unit  14  as a purchase target when the precision P is less than the reference value. 
     For example, the registration unit  15  acquires, from a product master stored in a store server, product information (product name, unit price, or the like) on a product to be registered as a purchase target, and stores the product information in the storage unit  11 . 
     Next, one example of a flow of processing of the processing apparatus  10  will be described by using a flowchart in  FIG.  8   . 
     In a case of the present example embodiment in which the processing apparatus  10  is used in accounting processing at a store, the processing apparatus  10  repeats product registration processing S 20  of registering a purchase target product and adjustment processing S 21  based on a registration content. 
     In the product registration processing S 20 , the processing apparatus  10  repeats processing illustrated by a flowchart in  FIG.  9   . 
     First, an operator performs an operation of photographing a purchase target product. The classification execution unit  12  acquires an image generated by the photographing (S 10 ). 
     Next, the classification execution unit  12  executes, based on an inference device stored in the storage unit  11  and the image acquired in S 10 , class classification for determining a product included in the image (S 11 ). 
     Next, the computation unit  13  computes the precision P of the product determined by the class classification in S 11  (S 12 ). Specifically, the computation unit  13  computes a prior distribution of the product determined by the class classification in S 11 , based on prior distribution information (see  FIG.  3   ) at that point in time. Then, the computation unit  13  computes the precision P, based on the recall R and the specificity S stored in advance in the storage unit  11 , the computed prior distribution, and the above-described equation (1). 
     When the computed precision P is equal to or more than a reference value (Yes in S 13 ), the registration unit  15  registers the product determined by the class classification in S 11  as a purchase target (S 16 ). For example, the registration unit  15  acquires product information (product name, unit price, or the like) on the product from a product master stored in a store server, and stores the product information in the storage unit  11 . Next, the registration unit  15  updates the prior distribution information (see  FIG.  3   ) (S 17 ). In other words, the registration unit  15  increments by “1” the number of times registering the product registered as a purchase target. 
     On the other hand, when the computed precision P is less than a reference value (No in S 13 ), the processing unit  14  executes correct/incorrect input acceptance processing of outputting the product determined by the class classification in S 11  and accepting a correct/incorrect input from an operator (S 14 ). Then, the registration unit  15  determines a correct product, based on a content of the correct/incorrect input in S 14  (S 15 ), and registers the product determined based on the content of the correct/incorrect input in S 14  as a purchase target (S 16 ). For example, the registration unit  15  acquires product information (product name, unit price, or the like) on the product from a product master stored in a store server, and stores the product information in the storage unit  11 . Next, the registration unit  15  updates the prior distribution information (see  FIG.  3   ) (S 17 ). In other words, the registration unit  15  increments by “1” the number of times registering the product registered as a purchase target. Processing in S 16  and S 17  is as described above. 
     After the processing illustrated by the flowchart in  FIG.  9    is performed at least once and at least one product is registered as a purchase target, the processing apparatus  10  becomes able to accept an input for proceeding to adjustment processing. Then, the processing apparatus  10  executes the adjustment processing S 21  in response to the input. 
     For example, the processing apparatus  10  performs, in the adjustment processing S 21 , adjustment for an accounting amount computed based on a registration content in the product registration processing S 20  by accepting an input of credit card information, communicating with a server of a credit card company to perform payment processing, accepting an input of a deposit amount, computing change, giving computed change, executing other processing such as code payment, and issuing a receipt. 
     Besides the above, the processing apparatus  10  may transmit, in the adjustment processing S 21 , an accounting amount computed based on a registration content in the product registration processing S 20  or a detailed statement or the like of a purchase target product to an adjustment apparatus physically and/or logically separated from the processing apparatus  10 . Then, the adjustment apparatus may accept an input of credit card information, communicate with a server of a credit card company to perform payment processing, accept an input of a deposit amount, compute change, give computed change, execute other processing such as code payment, and issue a receipt. This example assumes a situation in which an apparatus for registering a purchase target product and an apparatus for performing adjustment processing are physically and/or logically separated. 
     “Advantageous Effect” 
     The processing apparatus  10  according to the present example embodiment described above achieves a technique for detecting that there is a possibility that an inference result of an inference device generated by machine learning or the like may be erroneous and correcting the error. Thus, inconvenience that subsequent processing is executed based on an erroneous inference result can be reduced. In a case of the present example embodiment in which the processing apparatus  10  is used in accounting processing at a store, inconvenience that a product different from a product included in an image is registered as a purchase target and accounting processing is performed is prevented. 
     Further, the processing apparatus  10  according to the present example embodiment can compute the precision P of each product, based on a prior distribution indicating frequency with which each product is registered as a purchase target. By using such a characteristic prior distribution, the precision P can be computed with high accuracy and an error of an inference result can be detected with high accuracy. 
     Further, the processing apparatus  10  according to the present example embodiment can compute the precision P, based on the characteristic equation (1) as described above. Thus, the highly accurate precision P can be computed relatively simply. 
     Further, the processing apparatus  10  according to the present example embodiment is used under an environment where the recall R and the specificity S can be assumed as invariable regardless of an execution environment, and thus, the recall R and the specificity S can be set as fixed values among the parameters included in the above equation (1). Thus, burden on processing of the processing apparatus  10  can be reduced. 
     Second Example Embodiment 
     A processing apparatus  10  according to a present example embodiment is different from the first example embodiment in terms of a way of computing a prior distribution (purchase frequency). Specifically, the processing apparatus  10  determines an environment at a time of accounting processing, and computes a prior distribution (purchase frequency) under the environment. 
     A storage unit  11  according to the present example embodiment stores prior distribution information as illustrated in  FIG.  10    and environment information as illustrated in  FIG.  11   . As illustrated in  FIG.  10   , the prior distribution information according to the present example embodiment indicates the number of times each product is registered as a purchase target under each environment. In other words, the prior distribution information records an environment at a time when each product is registered as a purchase target. Each environment may be determined by one or more of parameters such as day of week, time of day, season, weather, product manufacturer, product shape, product type, purchaser information, and stock information (stock availability or stock quantity). 
     A computation unit  13  first determines an environment at that time when computing a prior distribution and a precision P. The computation unit  13  may acquire a value of various kinds of parameters determining an environment as described above, based on information stored in the processing apparatus  10 , or may acquire from an external apparatus. Then, the computation unit  13  determines an environment (environment number) at that time, based on the acquired value of various kinds of parameters and the environment information as illustrated in  FIG.  11   . 
     After determining the environment, the computation unit  13  refers to the prior distribution information as illustrated in  FIG.  10   , and computes a ratio of “the number of times registering a product determined by class classification as a purchase target under the determined environment” to “the total number of times registering individual products being handled at a store as a purchase target under the determined environment”, as a prior distribution of the product determined by the class classification under the environment. 
     Other configurations of the processing apparatus  10  are similar to the first example embodiment. 
     The processing apparatus  10  according to the present example embodiment achieves an advantageous effect similar to the first example embodiment. Further, the processing apparatus according to the present example embodiment in which a prior distribution and the precision P of each product can be computed in consideration of an environment at a time of accounting processing is able to more accurately detect that there is a possibility that an inference result of an inference device generated by machine learning or the like may be erroneous. 
     Third Example Embodiment 
     A processing apparatus  10  according to a present example embodiment prepares a recall R and specificity S for each environment at a time of accounting processing, rather than setting as fixed values. Then, the processing apparatus  10  computes a precision P by using the recall R and the specificity S associated with an environment at a time of accounting processing. 
     A storage unit  11  according to the present example embodiment stores RS information as illustrated in  FIG.  12    and RS environment information as illustrated in  FIG.  13   . As illustrated in the figures, in the present example embodiment, an environment at a time of accounting processing is classified into a plurality of environments, and the recall R and the specificity S are stored in advance in the storage unit  11  for each environment. For example, a test for class classification of a classification execution unit  12  to be performed at a predetermined place before the processing apparatus  10  is actually used at a store is performed under a plurality of individual environments, and a plurality of test results under each environment are collected. Then, the recall R and the specificity S can be computed for each environment, based on the result of the test under each environment and the above equations (3) and (4). 
     A computation unit  13  first determines an environment at that time when computing the precision P. The computation unit  13  may acquire a value of various kinds of parameters determining an environment as described above, based on information stored in the processing apparatus  10 , or may acquire from an external apparatus. Then, the computation unit  13  determines an environment (RS environment number) at that time, based on the acquired value of various kinds of parameters and the RS environment information as illustrated in  FIG.  13   . After determining the environment, the computation unit  13  acquires the recall R and the specificity S for use in computation of the precision P, based on the RS information as illustrated in  FIG.  12   . 
     Other configurations of the processing apparatus  10  are similar to the first and second example embodiments. 
     The processing apparatus  10  according to the present example embodiment achieves an advantageous effect similar to the first and second example embodiments. Further, the processing apparatus  10  according to the present example embodiment in which the recall R and the specificity S can be determined in consideration of an environment at a time of accounting processing is able to more accurately detect that there is a possibility that an inference result of an inference device generated by machine learning or the like may be erroneous. 
     The processing apparatus  10  according to the present example embodiment cannot assume that the recall R and the specificity S are invariable regardless of an execution environment, but can be used under such a situation in which an execution environment is classified and the recall R and the specificity S can be assumed as invariable under each execution environment. In a case of the present example embodiment in which the processing apparatus  10  is used in accounting processing at a store, for example, a case in which the processing apparatus  10  is installed near a window or a door and influence of light from outside is not ignorable for photographing of a product or the like pertains to the situation. 
     Modification Example 
     In the first to third example embodiments, the processing apparatus  10  is used in accounting processing at a store. However, a usage scene of the processing apparatus  10  is not limited thereto. The processing apparatus  10  according to the first to second example embodiments can be used in various kinds of scenes in which the recall R and the specificity S can be assumed as invariable regardless of an execution environment. Further, the processing apparatus  10  according to the third example embodiment can be used in various kinds of scenes in which an execution environment is classified and the recall R and the specificity S can be assumed as invariable under each execution environment. Note that, in a modification example, a processing apparatus  10  may include, in place of the registration unit  15 , an execution unit that executes predetermined processing, based on a determination result of class classification. The execution unit executes predetermined processing, based on a determination result of class classification when a precision is equal to or more than a reference value, and executes predetermined processing, based on a determination result based on a correct/incorrect input from an operator when the precision is less than the reference value. A detail of predetermined processing is a matter of design choice. 
     Note that, “acquisition” in the present description may include “fetching (active acquisition), by an own apparatus, data stored in another apparatus or storage medium” based on a user input or based on an instruction of a program, for example, requesting or inquiring another apparatus to receive data, accessing another apparatus or storage medium to read out data therefrom, and the like. Further, “acquisition” may include “inputting (passive acquisition), to an own apparatus, data output from another apparatus” based on a user input or based on an instruction of a program, for example, receiving delivered (transmitted, push-notified, or the like) data, and the like. Further, “acquisition” may include selectively acquiring received data or information, and “generating new data by editing data (such as conversion to a text, sorting of data, extraction of partial data, or change of a file format) or the like and acquiring the new data”. 
     While the present invention has been described with reference to the example embodiments (and the examples), the present invention is not limited to the above example embodiments (and the examples). Various modifications that can be understood by those skilled in the art can be made to the configurations and details of the present invention within the scope of the present invention. 
     The whole or part of the above-described example embodiments can be described as, but not limited to, the following supplementary notes. 
     1. A processing apparatus including: 
     a classification execution means for executing class classification, based on an inference device; 
     a computation means for computing a precision of a class determined by the class classification; and 
     a processing means for outputting a class determined by the class classification and accepting a correct/incorrect input from an operator when the precision is less than a reference value. 
     2. The processing apparatus according to supplementary note 1, wherein 
     the computation means computes the precision, based on a prior distribution of a class determined by the class classification. 
     3. The processing apparatus according to supplementary note 2, wherein
         the computation means computes the precision, based on the above equation (1).
 
4. The processing apparatus according to supplementary note 3, wherein
       

     R and S are fixed values. 
     5. The processing apparatus according to supplementary note 3, wherein 
     a value for R and S is prepared for each environment in advance, and R and S are determined based on an environment at a time of computing the precision. 
     6. The processing apparatus according to any of supplementary notes 2 to 5, wherein 
     the classification execution means determines, by an image photographed of a product and class classification based on the inference device, a product included in the image, 
     the processing apparatus further including 
     a registration means for registering a product determined by the class classification as a purchase target when the precision is equal to or more than the reference value, and registering a product determined by a correct/incorrect input from the operator as a purchase target when the precision is less than the reference value, wherein 
     the prior distribution indicates frequency with which a product determined by the class classification is registered as a purchase target. 
     7. The processing apparatus according to supplementary note 6, wherein 
     the prior distribution is a ratio of a number of times registering a product determined by the class classification as a purchase target to a total number of times registering individual products being handled at a store as a purchase target. 
     8. The processing apparatus according to supplementary note 6 or 7, wherein 
     the computation means computes the prior distribution of a product determined by the class classification under an environment at a time of computing the precision, based on information recording an environment at a time when each product is registered as a purchase target, and computes the precision, based on the computed prior distribution. 
     9. A processing method including: 
     by a computer, 
     executing class classification, based on an inference device; 
     computing a precision of a class determined by the class classification; and 
     outputting a class determined by the class classification and accepting a correct/incorrect input from an operator when the precision is less than a reference value. 
     10. A program causing a computer to function as: 
     a classification execution means for executing class classification, based on an inference device; 
     a computation means for computing a precision of a class determined by the class classification; and 
     a processing means for outputting a class determined by the class classification and accepting a correct/incorrect input from an operator when the precision is less than a reference value.