Patent Publication Number: US-2021165825-A1

Title: Information processing apparatus, information processing method, and program

Description:
TECHNICAL FIELD 
     The present disclosure relates to an information processing apparatus, an information processing method, and a program. 
     BACKGROUND ART 
     An electronic device referred to as an agent that provides information in accordance with a spoken request is proposed (for example, refer to PTL 1). 
     CITATION LIST 
     Patent Literature 
     [PTL 1] 
     JP 2008-90545A 
     SUMMARY 
     Technical Problem 
     In such a field, usability improves if, when an ambiguous utterance is made by a user, the user is able to recognize an index (a criterion) based on which a determination of information corresponding to the ambiguous utterance had been made. 
     An object of the present disclosure is to provide an information processing apparatus, an information processing method, and a program which, for example, when there are a plurality of pieces of information based on a search result, notifies the pieces of information by making an index corresponding to each piece of information recognizable. 
     Solution to Problem 
     The present disclosure is, for example, 
     an information processing apparatus including: 
     a control unit configured to perform, when there are a plurality of pieces of information corresponding to a predetermined term having been associated with a plurality of pieces of attribute information as candidates of a search result, control to notify each piece of information by making an index calculated with respect to each term recognizable. 
     The present disclosure is, for example, 
     an information processing method including: 
     a control unit performing, when there are a plurality of pieces of information corresponding to a predetermined term having been associated with a plurality of pieces of attribute information as candidates of a search result, control to notify each piece of information by making an index calculated with respect to each term recognizable. 
     The present disclosure is, for example, 
     a program that causes a computer to execute an information processing method including: 
     a control unit performing, when there are a plurality of pieces of information corresponding to a predetermined term having been associated with a plurality of pieces of attribute information as candidates of a search result, control to notify each piece of information by making an index calculated with respect to each term recognizable. 
     Advantageous Effects of Invention 
     According to at least one embodiment of the present disclosure, when a plurality of pieces of information are notified, a user can recognize indices corresponding to the pieces of information. It should be noted that the advantageous effect described above is not necessarily restrictive and any of the advantageous effects described in the present disclosure may apply. In addition, it is to be understood that contents of the present disclosure are not to be interpreted in a limited manner according to the exemplified advantageous effects. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram showing a configuration example of an agent according to an embodiment. 
         FIG. 2  is a diagram for explaining functions of a control unit according to a first embodiment. 
         FIG. 3  is a diagram showing an example of information stored in a database according to the first embodiment. 
         FIG. 4  is a diagram showing an example of accuracy scores and subscores according to the first embodiment. 
         FIG. 5  is a diagram for explaining an example of communication that takes place between a user and an agent. 
         FIG. 6  is a diagram for explaining an example of communication that takes place between a user and an agent. 
         FIG. 7  is a diagram for explaining an example of communication that takes place between a user and an agent. 
         FIG. 8  is a diagram for explaining an example of communication that takes place between a user and an agent. 
         FIG. 9  is a diagram for explaining an example of communication that takes place between a user and an agent. 
         FIG. 10  is a diagram for explaining an example of communication that takes place between a user and an agent. 
         FIG. 11  is a diagram for explaining an example of communication that takes place between a user and an agent. 
         FIG. 12  is a flow chart showing a flow of processing performed in the first embodiment. 
         FIG. 13  is a flow chart showing a flow of processing performed in the first embodiment. 
         FIG. 14  is a diagram for explaining functions of a control unit according to a second embodiment. 
         FIG. 15  is a diagram to be referred to for explaining a specific example of information stored in a database according to the second embodiment. 
         FIG. 16  is a diagram showing an example of accuracy scores and subscores according to the second embodiment. 
         FIG. 17  is a diagram for explaining functions of a control unit according to a third embodiment. 
         FIG. 18  is a diagram showing an example of information stored in a database according to the third embodiment. 
         FIG. 19  is a diagram showing an example of accuracy scores and subscores according to the third embodiment. 
         FIG. 20  is a diagram for explaining a modification. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments and the like of the present disclosure will be described with reference to the drawings. The description will be given in the following order. 
     &lt;First Embodiment&gt; 
     &lt;Second Embodiment&gt; 
     &lt;Third Embodiment&gt; 
     &lt;Modifications&gt; 
     It is to be understood that the embodiments and the like described below are preferable specific examples of the present disclosure and that contents of the present disclosure are not to be limited to such embodiments and the like. 
     First Embodiment 
     Configuration Example of Agent 
     In the embodiment, an agent will be described as an example of an information processing apparatus. An agent according to the embodiment signifies, for example, a speech input/output apparatus of which a size is more or less portable or a spoken dialogue function with a user that is included in such an apparatus. Such an agent may also be referred to as a smart speaker or the like. It is needless to say that the agent is not limited to a smart speaker and may be a robot or the like or, alternatively, the agent itself may not be independent and may be built into various electronic devices such as smart phones, vehicle-mounted equipment, or home electrical appliances. 
       FIG. 1  is a block diagram showing a configuration example of an agent (an agent  1 ) according to a first embodiment. The agent  1  has, for example, a control unit  10 , a sensor unit  11 , an image input unit  12 , an operation input unit  13 , a communication unit  14 , a speech input/output unit  15 , a display  16 , and a database  17 . 
     The control unit  10  is constituted by a CPU (Central Processing Unit) or the like and controls the respective units of the agent  1 . The control unit  10  has a ROM (Read Only Memory) that stores a program and a RAM (Random Access Memory) to be used as a work memory when the control unit  10  executes the program (it should be noted that the ROM and the RAM are not illustrated). The control unit  10  performs, when there are a plurality of pieces of information corresponding to a predetermined term having been associated with a plurality of pieces of attribute information as candidates of a search result, control to notify each piece of information by making an index calculated with respect to each term recognizable. Specific examples of control to be performed by the control unit  10  will be described later. 
     The sensor unit  11  is, for example, a sensor apparatus capable of acquiring biological information of a user of the agent  1 . Examples of biological information include a fingerprint, blood pressure, a pulse, a sweat gland (a position of the sweat gland or a degree of perspiration from the sweat gland may suffice), and a body temperature of the user. It is needless to say that, alternatively, the sensor unit  11  may be a sensor apparatus (for example, a GPS (Global Positioning System) sensor or a gravity sensor) that acquires information other than biological information. Sensor information obtained by the sensor unit  11  is input to the control unit  10 . 
     The image input unit  12  is an interface that accepts image data (which may be still image data or moving image data) input from the outside. For example, image data is input to the image input unit  12  from an imaging apparatus or the like that differs from the agent  1 . The image data input to the image input unit  12  is input to the control unit  10 . Alternatively, image data may be input to the agent  1  via the communication unit  14 , in which case the image input unit  12  need not be provided. 
     The operation input unit  13  is for accepting an operation input from the user. Examples of the operation input unit  13  include a button, a lever, a switch, a touch panel, a microphone, and an eye-gaze tracking device. The operation input unit  13  generates an operation signal in accordance with an input made to the operation input unit  13  itself and supplies the operation signal to the control unit  10 . The control unit  10  executes processing in accordance with the operation signal. 
     The communication unit  14  communicates with other apparatuses that are connected via a network such as the Internet. The communication unit  14  has components such as a modulation/demodulation circuit and an antenna which correspond to a communication standard. Communication performed by the communication unit  14  may be wired communication or wireless communication. Examples of wireless communication include a LAN (Local Area Network), Bluetooth (registered trademark), Wi-Fi (registered trademark), and WUSB (Wireless USB). The agent  1  is capable of acquiring various types of information from a connection destination of the communication unit  14 . 
     The speech input/output unit  15  is a component that inputs speech to the agent  1  and a component that outputs speech to the user. An example of the component that inputs speech to the agent  1  is a microphone. In addition, an example of the component that outputs speech to the user is a speaker apparatus. For example, an utterance by the user is input to the speech input/output unit  15 . The utterance input to the speech input/output unit  15  is supplied to the control unit  10  as utterance information. In addition, in accordance with control by the control unit  10 , the speech input/output unit  15  reproduces predetermined speech with respect to the user. When the agent  1  is portable, carrying around the agent  1  enables speech to be input and output at any location. 
     The display  16  is a component that displays still images and moving images. Examples of the display  16  include an LCD (Liquid Crystal Display), organic EL (Electro Luminescence), and a projector. The display  16  according to the embodiment is configured as a touch screen and enables operation input by coming into contact with (or coming close to) the display  16 . 
     The database  17  is a storage unit that stores various types of information. Examples of the database  17  include a magnetic storage device such as an HDD (Hard Disk Drive), a semiconductor storage device, an optical storage device, and a magneto-optical storage device. Predetermined information among information stored in the database  17  is searched by the control unit  10  and a search result thereof is presented to the user. 
     The agent  1  may be configured to be driven based on power supplied from a commercial power supply or may be configured to be driven based on power supplied from a chargeable and dischargeable lithium-ion secondary battery or the like. 
     While a configuration example of the agent  1  has been described above, the configuration of the agent  1  can be modified as deemed appropriate. In other words, a configuration of the agent  1  may not include a part of the illustrated components or may differ from the illustrated configuration. 
     Functions of Agent 
     Next, functions of the agent  1  and, more specifically, an example of functions of the control unit  10  will be described with reference to  FIG. 2 . As functions thereof, for example, the control unit  10  has a score calculation data storage unit  10   a,  a score calculation unit  10   b,  and a search result output unit  10   c.    
     Score Calculation Data Storage Unit 
     The score calculation data storage unit  10   a  stores information in the database  17 . As shown in  FIG. 2 , the score calculation data storage unit  10   a  detects emotion based on a sensing result of biological information obtained via the sensor unit  11 , a result of image analysis with respect to image data of a photograph or the like that is input from the image input unit  12 , a result of speech recognition, and the like. In addition, the score calculation data storage unit  10   a  performs speech recognition and morphological analysis with respect to utterance information that is input via the speech input/output unit  15 , associates a result thereof and a result of emotion detection and the like with each other, and stores the associated result in the database  17  as history. 
     According to the result of speech recognition and morphological analysis performed by the score calculation data storage unit  10   a,  for example, the following is obtained: a predetermined term (for example, a noun); related terminology that is related to the term (for example, a noun in apposition to the term, an adjective that modifies the term, and a verb with respect to the term); time-of-day information included in an utterance (which may be a time of day itself or information equivalent to a time of day); positional information included in an utterance (for example, a geographical name, an address, and latitude and longitude); and an identification score (a score value according to a recognition likelihood of speech recognition). 
       FIG. 3  shows an example of information stored in the database  17  by the score calculation data storage unit  10   a.  The database  17  stores predetermined terms associated with a plurality of pieces of attribute information. In  FIG. 3 , “ID”, “time of day”, “location”, “part-of-speech in apposition”, “emotion”, “related term”, and “recognition accuracy” are shown as examples of attribute information. 
     For example, an utterance of 
     “The food at Japanese restaurant A that we went to last week (24 Aug. 2017) was delicious” 
     is input to the speech input/output unit  15 . 
     In this case, the score calculation data storage unit  10   a  sets “Japanese restaurant A” as a term corresponding to ID: 1 and stores attribute information obtained based on utterance information in association with “Japanese restaurant A”. For example, with respect to “Japanese restaurant A”, the score calculation data storage unit  10   a  associates and stores “24 Aug. 2017” as the time of day, “in Tokyo” as the location, “delicious” as the emotion, and “80” as recognition accuracy. When a location is not included in the utterance information, for example, the agent  1  acquires a log (for example, a log stored in a smart phone or the like) of positional information on “24 Aug. 2017” and registers the acquired positional information as the location. The recognition accuracy is a value that is set in accordance with a magnitude of noise or the like at the time of speech recognition. 
     For example, an utterance of 
     “I&#39;ve heard a new model has arrived at Bicycle shop B which I told you about last month (July, 2017)” 
     is input to the speech input/output unit  15 . 
     In this case, the score calculation data storage unit  10   a  extracts “Bicycle shop B” and “new model” that are included in utterance information, sets attribute information corresponding to each term, and stores the terms and the set attribute information in the database  17 . In  FIG. 3 , ID: 2 represents an example of a term “Bicycle shop B” and attribute information that corresponds to the term, and ID: 3 represents an example of a term “new model” and attribute information that corresponds to the term. For example, the agent  1  controls the communication unit  14  and accesses Bicycle shop B&#39;s website, acquires detailed location thereof (in the example shown in  FIG. 3 , “Shinjuku”), and registers the acquired location information as a location corresponding to “Bicycle shop B”. 
     ID: 4 represents 
     an example of a term and attribute information corresponding to the term that are stored in the database  17  based on utterance information of 
     “I met A at Seafood restaurant C that we went to last month (May, 2017)”. 
     ID: 5 represents 
     an example of a term and attribute information corresponding to the term that are stored in the database  17  based on utterance information of 
     “Motsunabe restaurant D in Osaki which we visited in summer has reopened”. 
     As in the present example, there are also cases where a “location” that is positional information is acquired based on utterance information. 
     ID: 6 represents 
     an example of a term and attribute information corresponding to the term that are stored in the database  17  based on utterance information of 
     “I want to find that wonderful, wonderful shochu that we had during our trip to Kyushu”. 
     As an emotion, the fact that “wonderful” is repeated is also stored. 
     ID: 7 represents 
     an example of a term and attribute information corresponding to the term that are stored in the database  17  based on utterance information of 
     “I want to revisit Japanese restaurant E where we went to in early August and the food was truly delicious”. 
     As an emotion, the fact that the term “truly” is added to emphasize “delicious” is also stored. 
     It is needless to say that the contents of the database  17  shown in  FIG. 3  are simply an example and the database  17  is not limited thereto. Other pieces of information may also be used as attribute information. 
     Score Calculation Unit 
     The score calculation unit  10   b  calculates a score that is an index with respect to information stored in the database  17 . A score according to the present embodiment includes a subscore that is calculated for each piece of attribute information and an integrated score that integrates subscores. An integrated score is, for example, a simple addition or a weighted addition of subscores. In the following description, an integrated score will be referred to as an accuracy score when appropriate. 
     As shown in  FIG. 2 , for example, when utterance information is input via the speech input/output unit  15 , the control unit  10  always performs speech recognition and morphological analysis with respect to the utterance information. In addition, when utterance information including a term with ambiguity is input, the control unit  10  calculates an accuracy score and a subscore that correspond to the utterance information for each term that is stored in the database  17 . A term with ambiguity is a term which refers to something but it is impossible to uniquely identify exactly what the term refers to. Specific examples of a term with ambiguity include demonstratives such as that and it, terms including temporal ambiguity such as recently, and terms including locational ambiguity such as near or around P station. A term with ambiguity is extracted using, for example, meta-information related to context. 
     For example, let us consider a case where a request from a user of 
     “Make a reservation at that restaurant where I recently visited and where the food was delicious” 
     was input to the agent  1  by speech at Osaki Station on 10 Sep. 2017. 
     Since the utterance information include a term with ambiguity (in the present example, the term “recently”), the score calculation unit  10   b  calculates an accuracy score and a subscore. It should be noted that an upper limit value, a lower limit value, and the like of the accuracy score and the subscore can be appropriately set. 
       FIG. 4  is a diagram showing an example of accuracy scores and subscores. Since contents of the utterance information are “a restaurant where the food was delicious”, pieces of information on places other than restaurants (in the example shown in  FIG. 4 , pieces of information corresponding to ID: 2 and ID: 3) are excluded. In this case, accuracy scores with respect to ID: 2 and ID: 3 may not be calculated or may be set to 0. 
     For example, the subscore for each piece of attribute information is calculated as follows.
         In the case of “time of day”, attribute information that is closer to the “time of day” and of which a range is narrower (attribute information with a smaller deviation from the time of day specified in the utterance information) is given a higher score.   Similarly, in the case of “location”, attribute information that is closer to the location and of which a range is narrower (attribute information with a smaller deviation from the location specified in the utterance information) is given a higher score.   In the case of “emotion”, when there is a term indicating information on positivity/ negativity of an emotion, a basic score value is given, and when there is a term that further emphasizes the emotion (for example, “truly”) or when the emotion is repeated, a score is calculated so as to increase an absolute value of the basic score.   “Recognition accuracy” is calculated based on recognition accuracy when stored in the database  17 .   Even when attribute information is not registered, a constant value is assigned without exempting the attribute information. For example, even though a time of day corresponding to ID: 6 is not registered, since it is unclear as to whether the time of day corresponding to ID: 6 is near to or far from the time of day specified in the utterance information, a certain value (for example, 20) is given.       

     For example, the score calculation unit  10   b  calculates the accuracy score by simply adding up the subscores. A specific description will be given using information corresponding to ID: 1. Since the term corresponding to ID: 1 is “Japanese restaurant A”, the term becomes a candidate of a search result. With respect to the attribute information “time of day”, since the attribute information “time of day” is near the time of day (10 Sep. 2017) that is included in the utterance information, a high score (for example, 90) is given. With respect to the attribute information “location”, although Osaki Station that is included in the utterance information is in Tokyo, since a case where the deviation is large is also assumed, an intermediate value (for example, 50) is assigned. With respect to the attribute information “emotion”, since the attribute information “emotion” has a high degree of coincidence with the emotional expression “delicious” that is included in the utterance information, a high score (for example, 100) is given. With respect to recognition accuracy, a value thereof is used as a subscore. A value obtained by a simple addition of the respective subscores, 320, is the accuracy score corresponding to the term “Japanese restaurant A”. An accuracy score and subscores are similarly calculated with respect to pieces of information corresponding to the other IDs. 
     It should be noted that, in the present embodiment, with respect to pieces of attribute information (part-of-speech in apposition, related term, and the like) that are often not assigned, a subscore is not calculated. Accordingly, processing can be simplified. It is needless to say that, alternatively, subscores may be calculated with respect to all of the pieces of attribute information. 
     Search Result Output Unit 
     The search result output unit  10   c  outputs a search result in accordance with a score calculation result by the score calculation unit  10   b.  When utterance information including a term with ambiguity is input, the search result output unit  10   c  notifies the user of a search result. The search result output unit  10   c  outputs a search result in four patterns (patterns P 1 , P 2 , P 3 , and P 4 ). The four patterns will be described using the example shown in  FIG. 4 . 
     While conditions corresponding to the respective patterns may overlap with each other in order to facilitate understanding of each pattern in the description below, in reality, the conditions are appropriately set so as not to overlap with each other. 
     Output Examples of Search Result 
     Pattern P 1   
     The pattern P 1  is an output pattern of a search result that is performed in a case where it is clearly determined that there is only one piece of information (option) that corresponds to utterance information. A case where it is clearly determined that there is only one option is, for example, a case where an accuracy score of information corresponding to a given ID exceeds a threshold and there is one piece of information of which an accuracy score exceeds the threshold. 
       FIG. 5  is a diagram showing an example of communication that takes place between a user U and the agent  1  in the case of the pattern P 1 . As in the example described above, the user U makes an utterance of “Make a reservation at that restaurant where I recently visited and where the food was delicious” to the agent  1 . As a result of calculating an accuracy score and subscores, since an accuracy score of “Japanese restaurant E” exceeds a threshold (for example, 330) and “Japanese restaurant E” is the only term that exceeds the threshold, the agent  1  outputs “Japanese restaurant E” that is a search result in the pattern P 1 . 
     In the case of the pattern P 1 , while the agent  1  notifies the user U of the one and only candidate, the agent  1  performs processing based on the utterance without questioning whether the candidate is correct or not. The control unit  10  of the agent  1  performs control of generating speech data saying “You&#39;re referring to Japanese restaurant E. I will now make a reservation.” and reproducing the speech from the speech input/output unit  15 . In addition, by controlling the communication unit  14 , the control unit  10  of the agent  1  accesses a website or the like of “Japanese restaurant E” to perform appropriate reservation processing. 
     Pattern P 2   
     The pattern P 2  is an output pattern of a search result that is performed in a case where it is determined that there is only one piece of information (option) that corresponds to utterance information and it is determined that correctness of the piece of information (option) is around a certain degree (for example, around 90%). For example, when an accuracy score of information corresponding to a given ID exceeds a threshold (for example, 300), there is one piece of information of which an accuracy score exceeds the threshold, and a difference between the accuracy score and the threshold is within a predetermined range, a correctness of 90% is determined. 
       FIG. 6  is a diagram showing an example of communication that takes place between the user U and the agent  1  in the case of the pattern P 2 . As in the example described above, the user U makes an utterance of “make a reservation at that restaurant where I recently visited and where the food was delicious” to the agent  1 . As a result of calculating an accuracy score and subscores, since an accuracy score of “Japanese restaurant E” exceeds a threshold (for example, 330) and, although “Japanese restaurant E” is the only term that exceeds the threshold, since a difference between the accuracy score and the threshold is within a predetermined range (for example, 40 or lower), the agent  1  outputs “Japanese restaurant E” that is a search result in the pattern P 2 . 
     In the case of the pattern P 2 , as the agent  1  notifies the user U of the one and only candidate, the agent  1  performs an interaction for confirming whether the candidate is correct or not. With respect to the utterance by the user U, the control unit  10  of the agent  1  performs control of generating speech data saying “Are you referring to Japanese restaurant E?” and reproducing the speech from the speech input/output unit  15 . At this point, when confirmation by the user U is obtained in the form of a response saying “That&#39;s right” or the like, the control unit  10  of the agent  1  accesses the website or the like of “Japanese restaurant E” by controlling the communication unit  14  to perform appropriate reservation processing. When the intention of the user U is not “Japanese restaurant E”, information corresponding to a next highest accuracy score may be notified. 
     Pattern P 3   
     The pattern P 3  is an output pattern of a search result that is performed in a case where, while the accuracy score of a piece of information (option) that corresponds to utterance information is sufficient, it is determined that the score is near an accuracy score of a next-highest or subsequent candidate, there are a plurality of pieces of information (options) of which the accuracy score exceeds a threshold, or the like. In the case of the pattern P 3 , a plurality of candidates are output as search results. Conceivable methods of outputting the search results include a method using video and a method using speech. First, the method using video will be described. 
     Pattern P 3 : Output Example of Plurality of Search Results by Video 
       FIG. 7  is a diagram showing an example of communication that takes place between the user U and the agent  1  in the case of the pattern P 3 . In accordance with an utterance by the user U, the score calculation unit  10   b  of the control unit  10  calculates an accuracy score and subscores. Referring to the example shown in  FIG. 4 , while the highest accuracy score is 354 (piece of information corresponding to ID: 7), there are two pieces of information (pieces of information corresponding to ID: 1 and ID: 4) of which a difference in accuracy scores is within a threshold (for example, 150). In this case, the control unit  10  outputs pieces of information corresponding to IDs: 1, 4 and 7 as an output of search results. For example, as shown in  FIG. 7 , search results are output together with speech saying “There are several candidate. Which one is correct?” In the present example, still images corresponding to the plurality of candidates are displayed on the display  16 . The still images corresponding to the plurality of candidates may be acquired via the communication unit  14  or may be input by the user U via the image input unit  12 . 
     As shown in  FIG. 7 , an image IM 1  showing “Japanese restaurant A”, an image IM 2  showing “Seafood restaurant C”, and an image IM 3  showing “Japanese restaurant E” are displayed on the display  16 . In this case, the images IM 1  to IM 3  are examples of pieces of information corresponding to predetermined terms. Furthermore, each image is displayed in association with an accuracy score and subscores corresponding to each image or, more specifically, an accuracy score and subscores corresponding to each term with the ID: 1, 4, or 7. In other words, the images IM 1  to IM 3  are notified in such a manner that the accuracy scores and subscores having been calculated with respect to the terms corresponding to the images IM 1  to IM 3  are recognizable. 
     Specifically, an accuracy score “320” having been calculated with respect to “Japanese restaurant A” is displayed under the image IM 1  showing “Japanese restaurant A”. In addition, a subscore “90” related to the attribute information “time of day” and a subscore “50” related to the attribute information “location” are displayed in parallel to the accuracy score. In other words, a score SC 1  reading “320/90/50” is displayed below the image IM 1 . 
     An accuracy score “215” having been calculated with respect to “Seafood restaurant C” is displayed under the image IM 2  showing “Seafood restaurant C”. In addition, a subscore “50” related to the attribute information “time of day” and a subscore “100” related to the attribute information “location” are displayed in parallel to the accuracy score. In other words, a score SC 2  reading “215/50/100” is displayed below the image IM 2 . 
     An accuracy score “354” having been calculated with respect to “Japanese restaurant E” is displayed under the image IM 3  showing “Japanese restaurant E”. In addition, a subscore “70” related to the attribute information “time of day” and a subscore “85” related to the attribute information “location” are displayed in parallel to the accuracy score. In other words, a score SC3 reading “354/70/85” is displayed below the image IM 3 . 
     In this manner, by at least displaying an accuracy score, when there are a plurality of candidates of search results, the user can recognize which candidate was determined to have a high accuracy. In addition, providing numerical values instead of texts enables a display space to be downsized and even a small display  16  can be accommodated. 
     It should be noted that the designation with respect to the plurality of candidates may be performed by a pointing cursor as shown in  FIG. 7 , by designating an object name such as “Japanese restaurant A” by speech, or by designating a display position by speech. In addition, when designating “Japanese restaurant A”, a selection of a candidate may be performed by designating an accuracy score by speech such as “a restaurant with the score 320”. A selection of a candidate may be performed by designating a subscore by speech. 
     Display may be modified in accordance with an accuracy score. For example, display size may be increased in an ascending order of accuracy scores. In the example shown in  FIG. 7 , the image IM 3  is displayed in a largest size, the image IM 1  is displayed in a next-largest size, and the image IM 2  is displayed in a smallest size. An order, a grayscale, a frame color, or the like of display of each of the images IM 1  to IM 3  may be modified in accordance with a magnitude of the accuracy score. For example, an order of display or the like is appropriately set so that an image with a high accuracy score becomes prominent. The images IM 1  to IM 3  may be displayed by combining these methods of modifying display. In addition, an upper limit value or a lower limit value of accuracy scores to be displayed, the number of subscores to be displayed, and the like may be set in accordance with the display space. 
     As shown in  FIG. 7 , in the present embodiment, at least one subscore is to be displayed in addition to an accuracy score. However, not all subscores are to be displayed, but only a portion thereof is to be displayed. According to the display, when a plurality of candidates are to be displayed, a decline in visibility due to a large number of subscores being displayed can be prevented. On the other hand, there may be cases where attribute information corresponding to a displayed subscore differs from attribute information intended by the user U. Therefore, in the present embodiment, switching of display of a subscore to another display is further enabled. 
     Switching of the display of a subscore to another display will be described with reference to  FIG. 8 . As described above, it is assumed that the images IM 1  to IM 3  are displayed on the display  16  of the agent  1 . In this case, it is assumed that the user U utters “Display subscores of “emotion””. The utterance information of the user U is supplied to the control unit  10  via the speech input/output unit  15  and speech recognition by the control unit  10  is performed. The control unit  10  searches the database  17  and reads subscores respectively corresponding to the images IM 1  to IM 3  or, in other words, the IDs: 1, 4, and 7. In addition, as shown in  FIG. 8 , the control unit  10  displays a subscore of “emotion” below each image. Specifically, a score SC 1   a  reading “320/90/50/100” to which a subscore of “emotion” has been added is displayed below the image IM 1 . A score SC 2   a  reading “215/50/100/0” to which a subscore of “emotion” has been added is displayed below the image IM 2 . A score SC 3   a  reading “354/70/85/120” to which a subscore of “emotion” has been added is displayed below the image IM 3 . 
     According to the display, the user U can find out subscores corresponding to desired attribute information. It should be noted that, as shown in  FIG. 8 , scores SC 1   b  to SC 3   b  that only include an accuracy score and a subscore corresponding to designated attribute information may be displayed. In addition, a subscore corresponding to designated attribute information may be highlighted and displayed so that the user U can better recognize the subscore. For example, a color of a subscore corresponding to the designated attribute information may be distinguished from a color of other subscores or the subscore corresponding to the designated attribute information may be caused to blink. Furthermore, when predetermined attribute information is designated by an utterance, when a subscore corresponding to the attribute information is already being displayed, the subscore may be highlighted and displayed in accordance with the utterance. 
     There may be cases where the user U is not satisfied or feels a sense of discomfort with respect to a displayed search result. For example, in the example shown in  FIG. 8 , there may be a case where the user U feels that a difference between the accuracy score of “Japanese restaurant E” and the accuracy score of “Japanese restaurant A” is not as large as expected despite the user U recalling that he/she had felt the food at “Japanese restaurant E” was delicious. In order to accommodate such cases, in the present embodiment, a weight for calculating an accuracy score can be changed by the user U by designating attribute information to be emphasized. More specifically, an accuracy score is recalculated by giving additional weight (increasing a weight) of a subscore that corresponds to attribute information that the user U desires to emphasize. 
     A specific example will be described with reference to  FIG. 9 . Let us assume that the user U having viewed the images IM 1  to IM 3  utters “Emphasize subscore of “emotion””. The utterance information of the user U is input to the control unit  10  via the speech input/output unit  15  and speech recognition by the control unit  10  is performed. The score calculation unit  10   b  of the control unit  10  recalculates an accuracy score by, for example, doubling a weight with respect to a subscore of “emotion” that is the designated attribute information. 
     In addition, as shown in  FIG. 9 , a recalculated accuracy score and subscores recalculated in accordance with the changed weight are displayed on the display  16  as scores SC 1   d  to SC 3   d.  Specifically, the subscore of “emotion” of “Japanese restaurant A” that was originally “100” is recalculated as “200”. The accuracy score of “Japanese restaurant A” becomes “420” that represents an increase by an amount of increase (100) of the subscore. “420/200” that represents the accuracy score and the subscore of “emotion” is displayed below the image IM 1  as the score SC 1   d.  The subscore of “emotion” of “Seafood restaurant C” that was originally “0” is also recalculated as “0”. Therefore, “215/0” that represents the accuracy score and the subscore of “emotion” of “Seafood restaurant C” which are unchanged is displayed below the image IM 2  as the score SC 2   d.  The subscore of “emotion” of “Japanese restaurant E” that was originally “120” is recalculated as “240”. The accuracy score of “Japanese restaurant E” becomes “474” that represents an increase by an amount of increase (120) of the subscore. “474/240” that represents the accuracy score and the subscore of “emotion” is displayed below the image IM 3  as the score SC 3   d.  The user U having viewed the accuracy scores and the subscores after the recalculations can recognize that the difference in accuracy scores between “Japanese restaurant A” and “Japanese restaurant E” has increased and can experience a sense of satisfaction in the fact that the user U had previously felt the food at “Japanese restaurant E” was delicious. 
     Pattern P 3 : Output Example of Plurality of Search Results by Speech 
     Next, an output example of a plurality of search results by speech will be described.  FIG. 10  is a diagram for explaining an output example of a plurality of search results by speech. An utterance including a term with ambiguity is made by the user U. For example, the user U utters “Make a reservation at that restaurant where I recently visited and where the food was delicious”. The control unit  10  to which utterance information is input generates, in correspondence to the utterance information, speech data of a plurality of candidates and reproduces the speech data from the speech input/output unit  15 . 
     For example, the plurality of candidates that are search results are sequentially reproduced as speech. In the example shown in  FIG. 10 , candidates are notified by speech in an order of “Japanese restaurant A”, “Seafood restaurant C”, and “Japanese restaurant E”. In this case, the speech corresponding to each restaurant name is an example of a piece of information corresponding to the predetermined term. In addition, “Japanese restaurant E” is selected by a response (for example, a designation by speech saying “That&#39;s the one”) by the user U upon being notified of “Japanese restaurant E”, and reservation processing of “Japanese restaurant E” by the agent  1  is performed. 
     When notifying a plurality of candidates by speech, the candidates may be notified in a descending order of accuracy scores. In addition, when notifying a plurality of candidates by speech, accuracy scores and subscores may be successively notified together with candidate names. Since there is a risk that numerical values such as accuracy scores alone may be missed by the user U, when reading out accuracy scores and the like, a sound effect, BGM (Background Music), or the like may be added. While types of sound effects and the like can be set as appropriate, for example, when an accuracy score is high, a happy sound effect is reproduced when reproducing a candidate name corresponding to the accuracy score, and when an accuracy score is low, a gloomy sound effect is reproduced when reproducing a candidate name corresponding to the accuracy score. 
     Pattern P 4   
     The pattern P 4  is an output pattern of a search result that is performed when there are no accuracy scores that satisfy a criterion to begin with. In this case, the agent  1  makes a direct query to the user regarding contents.  FIG. 11  is a diagram showing an example of communication that takes place between the user U and the agent  1  in the case of the pattern P 4 . 
     The user U makes an utterance (for example, “Make a reservation at that restaurant where I recently visited and where the food was delicious”) that includes a term with ambiguity. When a search of the database  17  in accordance with the utterance information results in no appropriate candidates, for example, the agent  1  outputs speech saying “Which restaurant are you referring to?” to directly query the user U about a specific restaurant name. 
     Let us assume that the user U responds by saying “Japanese restaurant E” to the query by the agent  1 . In accordance with the response, the agent  1  executes processing for making a reservation at Japanese restaurant E. 
     As described above, search results are output from the agent  1  based on the exemplified patterns P 1  to P 4 . When outputting the search results, a method using video and a method using speech may be used in combination. In addition, when outputting search results according to the patterns P 1 , P 2 , and P 4 , video may be used or a method that concomitantly uses video and speech may be used. 
     Flow of Processing 
     A flow of processing performed by the agent  1  according to the first embodiment will be described. Control related to the processing described below is performed by the control unit  10  unless specifically stated to the contrary. 
       FIG. 12  is a flow chart showing a flow of processing mainly performed by the score calculation unit  10   b  of the control unit  10 . In step ST 11 , the user makes an utterance. In following step ST 12 , speech accompanying the utterance is input as utterance information to the control unit  10  via the speech input/output unit  15 . Subsequently, the processing advances to step ST 13 . 
     In step ST 13  and steps ST 14  and ST 15  subsequent thereto, the control unit  10  executes speech processing such as speech recognition, morphological analysis, and word decomposition with respect to the utterance information and detects a term (word) with ambiguity. Subsequently, the processing advances to step ST 16 . 
     In step ST 16 , as a result of processing of steps ST 13  to ST 15 , a determination is made as to whether or not the utterance information of the user includes a term with ambiguity. When the utterance information does not include a term with ambiguity, the processing returns to step ST 11 . When the utterance information includes a term with ambiguity, the processing advances to step ST 17 . 
     In step ST 17 , the score calculation unit  10   b  of the control unit  10  performs score calculation processing. Specifically, the score calculation unit  10   b  of the control unit  10  calculates subscores corresponding to the utterance information. In addition, the score calculation unit  10   b  of the control unit  10  calculates an accuracy score based on the calculated subscores. 
     Following the processing shown in the flow chart in  FIG. 12 , processing shown in the flow chart in  FIG. 13  is performed. It should be noted that a description of “AA” shown in the flow charts in  FIGS. 12 and 13  indicates continuity of processing and does not indicate a specific processing step. 
     The processing shown in the flow chart in  FIG. 13  is processing that is mainly performed by the search result output unit  10   c  of the control unit  10 . In step ST 18 , a determination is made as to whether or not there is only one candidate corresponding to the utterance information and that the candidate is at a level (hereinafter, referred to as an assertible level when appropriate) where it can be asserted that the candidate corresponds to the utterance by the user. When accuracy of the search result is at the assertible level (for example, an accuracy of around 99%), the processing advances to step ST 19 . 
     In step ST 19 , the candidate that is a search result is notified by the pattern P 1  described above. For example, the control unit  10  performs processing based on the utterance of the user made in step ST 11  while notifying a candidate name of the one and only candidate. 
     When accuracy of the search result is not at the assertible level, the processing advances to step ST 20 . In step ST 20 , a determination is made as to whether or not there is only one candidate corresponding to the utterance information and that the candidate is at a level (hereinafter, referred to as a near-assertible level when appropriate) where it can be nearly asserted that the candidate corresponds to the utterance by the user. When accuracy of the search result is at the near-assertible level (for example, an accuracy of around 90%), the processing advances to step ST 21 . 
     In step ST 21 , the candidate that is a search result is notified by the pattern P 2  described above. For example, the control unit  10  notifies a candidate name of the one and only candidate and, when it is confirmed that the candidate name is a candidate desired by the user, the control unit  10  performs processing based on the utterance of the user made in step ST 11 . 
     When accuracy of the search result is not at the near-assertible level, the processing advances to step ST 22 . In step ST 22 , a determination is made as to whether or not there are several candidates that are search results. When there are no candidates corresponding to the utterance information, the processing advances to step ST 23 . 
     In step ST 23 , processing corresponding to the pattern P 4  described above is executed. In other words, processing in which the agent  1  directly queries the user about a name of the candidate is performed. 
     In step ST 22 , when there are several candidates that are search results, the processing advances to step ST 24 . In step ST 24 , processing corresponding to the pattern P 3  described above is executed and the user is notified of a plurality of candidates that are search results. The plurality of candidates may be notified by speech, notified by video, or notified by a combination of speech and video. Subsequently, the processing advances to step ST 25 . 
     In step ST 25 , a determination is made as to whether or not any of the plurality of notified candidates has been selected. The selection of a candidate may be performed by speech, by an input using the operation input unit  13 , or the like. When any of the candidates has been selected, the processing advances to step ST 26 . 
     In step ST 26 , the control unit  10  executes processing of contents indicated in the utterance of the user with respect to the selected candidate. Subsequently, the processing is ended. 
     In step ST 25 , when any of the plurality of notified candidates has not been selected, the processing advances to step ST 27 . In step ST 27 , a determination is made as to whether or not there is an instruction to change contents. An instruction to change contents is, for example, an instruction to change a weight of each piece of attribute information or, more specifically, an instruction to place emphasis on a predetermined piece of attribute information. In step ST 27 , when there is no instruction to change contents, the processing advances to step ST 28 . 
     In step ST 28 , a determination is made as to whether or not an instruction to stop (abort) the series of processing steps has been issued by the user. When an instruction to stop the series of processing steps has been issued, the processing is ended. When an instruction to stop the series of processing steps has not been issued, the processing returns to step ST 24  and notification of candidates is continued. 
     In step ST 27 , when there is an instruction to change contents, the processing advances to step ST 29 . In step ST 29 , an accuracy score and subscores are recalculated in accordance with the instruction issued in step ST 27 . The processing then advances to step ST 24  and a notification based on the accuracy score and the subscores after the recalculation is performed. 
     As described above, according to the present embodiment, based on an objective index (for example, an accuracy score), the user can understand how the agent had determined a term with ambiguity. In addition, the user can change contents of attribute information corresponding to an index (for example, a subscore). Furthermore, since the agent can make determinations based on storage of previous words, an accuracy of determinations by the agent is improved. In addition, also importing biological information, camera video, and the like instead of just importing words enables the agent to make determinations with higher accuracy. Furthermore, an improvement in the determination accuracy of the agent makes interactions between the agent and the user (a person) more natural and prevents the user from feeling a sense of discomfort. 
     Second Embodiment 
     Next, a second embodiment will be described. In the following description, components that are the same or homogeneous to those of the first embodiment are assigned same reference signs and redundant descriptions will be omitted. In addition, matters described in the first embodiment can also be applied to the second embodiment unless specifically stated to the contrary. 
     The second embodiment represents an example of applying an agent to a mobile body or, more specifically, to a vehicle-mounted apparatus. While the mobile body will be described as a vehicle in the present embodiment, the mobile body may be anything such as a train, a bicycle, or an aircraft. 
     An agent (hereinafter, referred to as an agent  1 A when appropriate) according to the second embodiment has a control unit  10 A that offers similar functionality to the control unit  10  of the agent  1 . As shown in  FIG. 14 , as functions thereof, for example, the control unit  10 A has a score calculation data storage unit  10 Aa, a score calculation unit  10 Ab, and a search result output unit  10 Ac. The control unit  10 A differs from the control unit  10  in terms of architecture in the score calculation data storage unit  10 Aa. The agent  1 A applied to a vehicle-mounted apparatus performs position sensing using a GPS, a gyroscope sensor, or the like and stores a result thereof in the database  17  as movement history. The movement history is stored as time-series data. In addition, terms (words) included in utterances made in the vehicle are also stored. 
       FIG. 15  is a diagram (a map) to be referred to for explaining a specific example of information stored in the database  17  according to the second embodiment. For example, a route R 1  traveled on 4 Nov. 2017 (Sat) is stored in the database  17  as movement history. “Japanese restaurant C 1 ” and “Furniture store F 1 ” exist at predetermined positions along the route R 1  and Sushi restaurant D 1  exists at a location that is slightly distant from the route R 1 . An utterance made near “Japanese restaurant C 1 ” (for example, an utterance saying that “the food here is excellent”) or an utterance made when traveling near “Furniture store F 1 ” (for example, an utterance saying that “they have great stuff here”) are also stored in the database  17 . 
     In addition, for example, a route R 2  traveled on 6 Nov. 2017 (Mon), 8 Nov. 2017 (Wed), and 10 Nov. 2017 (Fri) is stored in the database  17  as movement history. “Shop A 1 ”, “Japanese restaurant B 1 ”, and “Japanese restaurant E 1 ” exist at predetermined positions along the route R 2 . An utterance made when traveling near “Japanese restaurant B 1 ” (for example, an utterance saying that “this place is wonderful”) is also stored in the database  17 . In addition, names of stores or restaurants that exist along each route or exist within a predetermined range from each route are registered in the database  17  as terms. The terms in this case may be based on utterances or may be read from map data. 
     In a state where the exemplified information is stored in the database  17 , for example, an utterance saying “Please make a reservation at that Japanese restaurant near P Station which I pass on weekdays” is made by the user with respect to the agent  1 A. Since the utterance information includes the term “that” which has ambiguity, the control unit  10 A of the agent  1 A calculates a subscore for each piece of attribute information corresponding to the term and calculates an accuracy score based on the calculated subscores in a similar manner to the first embodiment. 
       FIG. 16  shows an example of calculated accuracy scores and subscores. As attribute information, for example, an “ID”, a “position accuracy”, a “date-time accuracy”, an “accuracy with respect to Japanese restaurant”, and an “individual appraisal” are associated with each term. 
     Hereinafter, settings related to the calculation of subscores will be described. 
     Position accuracy: Since the utterance information includes a term reading “near P Station”, a subscore is calculated so that the shorter the distance to P Station, the higher the subscore. 
     Date-time accuracy: Since the utterance information includes a word reading “weekdays”, a subscore is calculated so that a subscore of a restaurant that exists along the route R 2  which is frequently traveled on weekdays is high and a subscore of a restaurant that exists along the route R 1  which is traveled on weekends and holidays is low. 
     Accuracy with respect to “Japanese restaurant”: Since the utterance information includes a word reading “that Japanese restaurant”, a subscore is calculated so that a restaurant that fits the description of a Japanese restaurant is given a higher subscore. 
     Individual appraisal: This is an appraised value that is derived from previously-stored utterances made inside the vehicle. The more positive the utterances, the higher the subscore. 
     Subscores calculated based on the settings described above are shown in  FIG. 16 . In addition, a value representing a sum of the subscores is calculated as an accuracy score. It should be noted that the accuracy score may be calculated by a weighted addition of the respective subscores in a similar manner to the first embodiment. 
     Notification of a candidate with respect to the user is performed based on an accuracy score calculated as described above. The notification of a candidate is performed based on any of the patterns P 1  to P 4  in a similar manner to the first embodiment. For example, in the case of the pattern P 3  in which a plurality of candidates are notified as search results, notification is performed by making at least accuracy scores recognizable. Notification may be performed by making subscores recognizable or by making subscores instructed by the user recognizable as described in the first embodiment. 
     When the agent  1 A is applied as a vehicle-mounted apparatus, the following processing may be performed during a response from the agent  1 A with respect to the user. 
     When a query is made by the user with respect to the agent  1 A while driving a vehicle, a response by the agent  1 A (including notification of a plurality of candidates) may be made after detecting that the vehicle has stopped. In the case of video, a video is displayed after the vehicle stops and, also in the case of speech, speech of the response is similarly provided after the vehicle stops. Accordingly, a decline in concentration of the user toward driving can be prevented. It should be noted that the agent  1 A can determine whether or not the vehicle has stopped based on sensor information obtained by a vehicle speed sensor. In this configuration, the sensor unit  11  includes the vehicle speed sensor. 
     In addition, when the agent  1 A detects that the vehicle has started moving during notification by video or speech, the agent  1 A suspends the notification by video or speech. Furthermore, based on sensor information of the vehicle speed sensor, when a vehicle speed of a certain level or higher continues for a certain period or longer, the agent  1 A determines that the vehicle is being driven on an expressway. When it is expected that the vehicle will not stop for a certain period or longer after a query is made from the user with respect to the agent  1 A such as when driving on an expressway as described above, the query may be canceled. The fact that the query has been canceled, an error message, or the like may be notified to the user by speech or the like. Responses may be provided to queries made by a user seated on a passenger seat with respect to the agent  1 A. Enabling the agent  1 A to accept only input from a user seated on a passenger seat can be realized by applying, for example, a technique referred to as beam-forming. 
     The second embodiment described above can also produce an effect similar to that of the first embodiment. 
     Third Embodiment 
     Next, a third embodiment will be described. In the following description, components that are the same or homogeneous to those of the first and second embodiments are assigned same reference signs and redundant descriptions will be omitted. In addition, matters described in the first and second embodiments can also be applied to the third embodiment unless specifically stated to the contrary. The third embodiment represents an example of applying an agent to a home electrical appliance or, more specifically, to a refrigerator. 
     An agent (hereinafter, referred to as an agent  1 B when appropriate) according to the third embodiment has a control unit  10 B that offers similar functionality to the control unit  10  of the agent  1 . As shown in  FIG. 17 , as functions thereof, for example, the control unit  10 B has a score calculation data storage unit  10 Ba, a score calculation unit  10 Bb, and a search result output unit  10 Bc. 
     The control unit  10 B differs from the control unit  10  in terms of architecture in the score calculation data storage unit  10 Ba. The agent  1 B includes, for example, two systems of sensors as the sensor unit  11 . One of the sensors is “a sensor for recognizing objects” of which examples include an imaging apparatus and an infrared sensor. The other sensor is “a sensor for measuring weight” of which examples include a gravity sensor. Using sensing results of the two systems, the score calculation data storage unit  10 Ba stores data regarding types and weights of objects inside the refrigerator. 
       FIG. 18  shows an example of information stored in the database  17  by the score calculation data storage unit  10 Ba. An “object” in  FIG. 18  corresponds to an “object” in the refrigerator that has been sensed by video sensing. A “change date/time” represents a date and time at which a change accompanying an object placed inside or taken out from the refrigerator had occurred. With respect to time information, a configuration in which a time measuring unit is included in the sensor unit  11  may be adopted, in which case time information may be obtained by the control unit  10 B from the time measuring unit, or the control unit  10 B may obtain time information from an RTC (Real Time Clock) included in the control unit  10 B itself. 
     “Change in number/number” represent the number of the object inside the refrigerator that had changed at the change date/time described above, and the number of the object after the change. The change in number is obtained based on, for example, a sensing result by an imaging apparatus or the like. “Change in weight/weight” represent a weight (an amount) that had changed at the change date/time described above, and the weight after the change. It should be noted that, in some cases, the weight changes even though the number does not. For example, there are cases where the weight changes even though the number does not such as the case of “apple juice” indicated by ID: 24 and ID: 31 in  FIG. 18 . This indicates that apple juice has been consumed. 
     Let us now consider a case where, for example, the user asks the agent  1 B, “What was the vegetable that&#39;s about to run out?” Such thinking for checking necessities often takes place during shopping outside of the home. Therefore, the user may talk to a smart phone during shopping outside of the home and utterance information may be transmitted from the smart phone to the agent  1 B via a network. A response to the user&#39;s query is transmitted from the agent  1 B via the network and the response is notified by display, speech, or the like from the user&#39;s smart phone. It is needless to say that, given the increasing popularity in recent years of shopping using the Internet or the like, cases where thinking for checking necessities takes place indoors (inside the home) are also expected. In such a case, a query by the user may be directly input to the agent  1 B. 
     The agent  1 B performs speech recognition with respect to the input utterance information of the user. Since the utterance information includes a term with ambiguity, “that vegetable”, the control unit  10 B calculates an accuracy score and subscores. 
     First, the score calculation unit  10 Bb of the control unit  10 B reads, from information in the database  17  shown in  FIG. 18 , a latest (newest) change date/time and a change in the number or the change in the weight that had occurred at the change date/time of each “object”. In addition, based on the read result, the score calculation unit  10 Bb calculates an accuracy score and subscores for each “object”. 
       FIG. 19  shows an example of calculated accuracy scores and subscores. In the present embodiment, an “object score” and a “weight score” are set as subscores. It is needless to say that scores in accordance with recognition accuracy of an object or the like may also be provided as described in the first embodiment. 
     Hereinafter, settings related to each subscore will be described. 
     Object score: Since the utterance information includes the term “that vegetable”, a high score is given in the case of a vegetable and a certain score is also given in the case of a fruit. In the example shown in  FIG. 19 , for example, carrots and onions which are vegetables are given high scores and kiwi fruit is also given a certain score. Conversely, scores given to non-vegetables (for example, eggs) are low. 
     Weight score: A score determined based on a most recent amount of change and a present weight is given. Since the utterance information includes the term (sentence) “about to run out”, a higher score is given when the amount of change is “negative (−)” and the weight after the change is smaller. For example, a high score is given to onions of which the amount of change is “negative (−)” and the weight after the change is small. 
     An accuracy score is calculated based on the calculated subscores. In the example shown in  FIG. 19 , an accuracy score is calculated by adding up the respective subscores. It is needless to say that the accuracy score may be calculated by a weighted addition of the respective subscores. 
     Notification of a candidate with respect to the user is performed based on an accuracy score calculated as described above. The notification of a candidate is performed based on any of the patterns P 1  to P 4  in a similar manner to the first embodiment. For example, in the case of the pattern P 3  in which a plurality of candidates are notified as search results, notification is performed by making at least accuracy scores recognizable. Notification may be performed by making subscores recognizable or by making subscores instructed by the user recognizable as described in the first embodiment. 
     The third embodiment described above can also produce an effect similar to that of the first embodiment. 
     Modifications 
     While a plurality of embodiments of the present disclosure have been described with specificity above, it is to be understood that the contents of the present disclosure are not limited to the embodiments described above and that various modifications can be made based on the technical ideas of the present disclosure. Hereinafter, modifications will be described. 
     A part of the processing by the agent according to the embodiments described above may be performed by a server apparatus. For example, as shown in  FIG. 20 , communication is performed between an agent  1  and a server apparatus  2 . The server apparatus  2  has, for example, a server control unit  21 , a server communication unit  22 , and a database  23 . 
     The server control unit  21  controls respective units of the server apparatus  2 . For example, the server control unit  21  has the score calculation data storage unit  10   a  and the score calculation unit  10   b  described earlier. The server communication unit  22  is a component for communicating with the agent  1  and has components such as a modulation/demodulation circuit and an antenna which correspond to a communication standard. The database  23  stores similar information to the database  17 . 
     Speech data and sensing data are transmitted from the agent  1  to the server apparatus  2 . The speech data and the like are supplied to the server control unit  21  via the server communication unit  22 . The server control unit  21  stores data for score calculation in the database  23  in a similar manner to the control unit  10 . In addition, when speech data supplied from the agent  1  includes a term with ambiguity, the server control unit  21  calculates an accuracy score and the like and transmits a search result corresponding to utterance information of the user to the agent  1 . The agent  1  notifies the user of the search result by any of the patterns P 1  to P 4  described earlier. Alternatively, a notification pattern may be designated by the server apparatus  2 . In this case, the designated notification pattern is described in data transmitted from the server apparatus  2  to the agent  1 . 
     Other modifications will now be described. In the embodiments described above, speech to be input to the agent is not limited to a conversation taking place around the agent but may also include a conversation recorded outside the home or the like, a conversion over the phone, and the like. 
     In the embodiments described above, a position where an accuracy score and the like are displayed is not limited to below an image and may be changed as appropriate such as to on top of an image. 
     In the embodiments described above, processing corresponding to utterance information is not limited to making a reservation at a restaurant and may be any kind of processing such as purchasing an item or reserving a ticket. 
     In the third embodiment described above, a sensor that reads a use-by date of an object (for example, a sensor that reads an RFID (Radio Frequency Identifier) attached to the object) may be applied as the sensor unit, in which case a weight may be set to 0 when the use-by date expires. In this manner, a configuration of the sensor unit may be changed as appropriate. 
     Configurations presented in the embodiments described above are merely examples and are not limited thereto. It is needless to say that components may be added, deleted, and the like without departing from the spirit and the scope of the present disclosure. The present disclosure can also be realized in any form such as an apparatus, a method, a program, and a system. The program may be stored in, for example, a memory included in the control unit or a suitable storage medium. 
     The present disclosure can also adopt the following configurations. 
     (1) 
     An information processing apparatus, including: 
     a control unit configured to perform, when there are a plurality of pieces of information corresponding to a predetermined term having been associated with a plurality of pieces of attribute information as candidates of a search result, control to notify each piece of information by making an index calculated with respect to each term recognizable. 
     (2) 
     The information processing apparatus according to (1), wherein the attribute information includes positional information acquired based on utterance information. 
     (3) 
     The information processing apparatus according to (1) or (2), wherein the control unit is configured to notify the search result when utterance information including a term with ambiguity is input. 
     (4) 
     The information processing apparatus according to any one of (1) to (3), wherein 
     the index includes a subscore calculated for each piece of attribute information and an integrated score that integrates a plurality of subscores, and 
     the control unit is configured to notify at least the integrated score so as to be recognizable. 
     (5) 
     The information processing apparatus according to (4), wherein the integrated score is a weighted addition of the subscores. 
     (6) 
     The information processing apparatus according to (5), wherein the control unit is configured to change a weight used in the weighted addition in accordance with utterance information. 
     (7) 
     The information processing apparatus according to any one of (4) to (6), wherein 
     the control unit is configured to notify at least one subscore so as to be recognizable. 
     (8) 
     The information processing apparatus according to any one of (1) to (7), wherein 
     the control unit is configured to display a plurality of pieces of the information in association with the index corresponding to each piece of information. 
     (9) 
     The information processing apparatus according to (8), wherein 
     the control unit is configured to differently display at least one of a size, a grayscale, and an arrangement order of display of each piece of information in accordance with an index corresponding to the piece of information. 
     (10) 
     The information processing apparatus according to (8), wherein 
     the index includes a subscore calculated for each piece of attribute information and an integrated score that integrates a plurality of subscores, and 
     the control unit is configured to display a subscore having been instructed by a predetermined input. 
     (11) 
     The information processing apparatus according to any one of (1) to (10), wherein 
     the control unit is configured to output a plurality of pieces of the information by speech in association with the index corresponding to each piece of information. 
     (12) 
     The information processing apparatus according to (11), wherein 
     the control unit is configured to consecutively output a predetermined piece of the information and the index corresponding to the piece of information. 
     (13) 
     The information processing apparatus according to (11), wherein 
     the control unit is configured to output a predetermined piece of the information by adding a sound effect based on the index corresponding to the piece of information. 
     (14) 
     The information processing apparatus according to any one of (1) to (13), wherein 
     the attribute information includes information related to an appraisal based on an utterance made during movement of a mobile body. 
     (15) 
     An information processing method, including: 
     a control unit performing, when there are a plurality of pieces of information corresponding to a predetermined term having been associated with a plurality of pieces of attribute information as candidates of a search result, control to notify each piece of information by making an index calculated with respect to each term recognizable. 
     (16) 
     A program that causes a computer to execute an information processing method including: 
     a control unit performing, when there are a plurality of pieces of information corresponding to a predetermined term having been associated with a plurality of pieces of attribute information as candidates of a search result, control to notify each piece of information by making an index calculated with respect to each term recognizable. 
     REFERENCE SIGNS LIST 
       1 ,  1 A,  1 B Agent 
       10 ,  10 A,  10 B Control unit 
       11  Sensor unit 
       15  Speech input unit 
       16  Display