DETAILED ACTION
This office action is in response to the communication filed on April 29, 2026. Claims 1-18 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
In response to the terminal disclaimer filed on 04/07/26 the pending double patenting rejection of claims 1-18 has been withdrawn.
In response to the claim amendments/remarks filed on 04/29/26 the pending 101 rejection of claim 2 has been withdrawn, however, the 101 rejection of claims 5-7 is being maintained in this office action.
In response to the claim amendments/remarks filed on 04/29/26 the pending 112 rejection of claims 3, 17, and 18 has been withdrawn.
Response to Arguments
Applicant's arguments filed on April 29, 2026 have been fully considered but they are not persuasive for the following reasons:
Applicant in Pages 10-11 of the Remarks argues that amended claims 5-7 fully
satisfy the requirements of 35 U.S.C. 101 and integrates any alleged abstract idea into
a practical application when the claims are considered as a whole.
Examiner respectfully disagrees.
Dependent claims 5 and 7 cover several steps, such as the estimated and calculated steps in claims 5 and 7, that recite an abstract idea within the “Mental Processes” grouping of abstract ideas, because a person can mentally or using a pen and paper perform the limitations recited in said steps, which are discussed in detail in the current 101 rejection below.
The remaining steps in the claims, such as the receive and search steps in claims 5 and 7, the receive, store, search, and transmit steps in independent claim 1 upon which claims 5 and 7 depend on, and the first determination model and second determination model steps described in dependent claim 6 which depends on claim 5, are identified as reciting additional elements, are only adding insignificant extra-solution activity to the judicial exception, are recognized as a well understood, routine, and conventional activity within the field of computer functions, and are applying the exception using generic computer components, which is not sufficient to amount to significantly more than the judicial exception and are not directed to any specific improvement in computer technology.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Applicant in Page 12 of the Remarks argues that Kayser, Matsunaga, De Bayser, and Tang do not teach or even suggest the amended features "the feature value includes a scene of the first image and at least one of a model name or a lens name of a camera used for capturing of the first images included in header information of the first image", as recited in amended independent claim 1 and similarly recited in amended independent claim 17.
Applicant’s arguments with respect to claim(s) 1 and 17 have been considered but are moot in view of new grounds of rejection, which is discussed in detail in the 103 rejection below.
For the above reasons, Examiner states that rejection of the current Office action is proper.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 5-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1:
Dependent claims 5-7 recite a server, which is directed to a statutory category such as a process, machine, or an article of manufacture.
At step 2A, prong one:
Dependent claim 5 recites the limitations:
“the feature value of the first image and the feature value of each second image received…are estimated by using a first determination model”;
A person can mentally or using a pen and paper estimate a feature value of a first image and a feature value of each received second image by using a first determination model.
“degrees of similarity between the feature value of the first image and the feature values of the second images received…are estimated for each first image by using a second determination model”;
A person can mentally or using a pen and paper estimate degrees of similarity between feature value of a first image and feature values of received second images for each first image by using a second determination model.
“a total degree of similarity between the feature value of the first image and the feature values of the plurality of second images received…is calculated based on the degrees of similarity”;
A person can mentally or using a pen and paper calculate a total degree of similarity between feature value of a first image and feature values of plurality of received second images based on estimated degrees of similarity.
The limitations, as recited above, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Dependent claim 7 recites the limitations:
“the feature value of the first image and the feature value of each second image received…are estimated by using a first determination model”;
A person can mentally or using a pen and paper estimate a feature value of a first image and a feature value of each received second image by using a first determination model.
“distances between the feature value of the first image and the feature values of the second images received…are calculated for each first image”;
A person can mentally or using a pen and paper calculate distances between a feature value of a first image and feature values of received second images for each first image.
“a total degree of similarity between the feature value of the first image and the feature values of the plurality of second images received…is calculated based on the distances”;
A person can mentally or using a pen and paper calculate a total degree of similarity between a feature value of a first image and feature values of received plurality of second images based on calculated distances.
The limitations, as recited above, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
At step 2A, prong two:
This judicial exception is not integrated into a practical application.
Dependent claims 5 and 7 respectively depend on independent claim 1 and independent claim 1 recites the limitations:
“receive first images and set values regarding capturing of the first images in a case where the first images are captured from one or more first communication terminals”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receive a feature value of the first image determined based on an analysis of the first image for each first image”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“store a table that includes the first image, the set value, and the feature value in association with each other for each first image in a memory”, which is a step of storing data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“search for the first image associated with a feature value coincident with or similar to a feature value of a second image received from a second communication terminal by the first processor from among the first images included in the table stored in the memory”, which is a step of searching for data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receive a set value determined to be associated with the first image searched for by the first processor from among the set values included in the table stored in the memory”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“transmit the set value acquired by the first processor to the second communication terminal”, which is a step of transmitting data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“wherein the feature value includes a scene of the first image and at least one of a model name or a lens name of a camera used for capturing of the first images included in header information of the first image”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “a server accessed by a plurality of communication terminals of a plurality of users, the server comprising: a first processor, wherein the first processor is configured to:”, “from one or more first communication terminals”, “from a second communication terminal by the first processor”, “stored in the memory”, “by the first processor”, and “by the first processor to the second communication terminal”, in the steps in claim 1 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 5 recites the limitations:
“wherein the first processor is configured to receive a plurality of the second images from the second communication terminal”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“the first processor is configured to search for a predetermined number of the first images from a side of which the total degree of similarity is the highest from among the first images stored in the memory”, which is a step of searching for data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “the server according to claim 1”, “wherein the first processor is configured to”, “from the second communication terminal”, “by the first processor”, “by using a first determination model”, “by using a second determination model”, “the first processor is configured to”, and “stored in the memory” in the steps in claim 5 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 depends on dependent claim 5 and recites the limitations:
“the server according to claim 5,
wherein the first determination model is a trained model in which a first learning image and a feature value of the first learning image are used as pieces of first training data and a relationship between the first learning image and the feature value of the first learning image is trained for a plurality of the pieces of first training data, and
the second determination model is a trained model in which a feature value of a second learning image, a feature value of a third learning image, and a degree of similarity between the feature value of the second learning image and the feature value of the third learning image are used as pieces of second training data and a relationship between the feature value of the second learning image, the feature value of the third learning image, and the degree of similarity between the feature value of the second learning image and the feature value of the third learning image is trained for a plurality of the pieces of second training data”.
These limitations recite additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Dependent claims 7 recites the limitations:
“wherein the first processor is configured to receive a plurality of the second images from the second communication terminal”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“the first processor is configured to search for a predetermined number of the first images from a side on which the total degree of similarity is the highest from among the first images stored in the memory”, which is a step of searching for data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “the server according to claim 1”, “wherein the first processor is configured to”, “from the second communication terminal”, “by the first processor”, “by using a first determination model”, “by using a second determination model”, “the first processor is configured to”, and “stored in the memory” in the steps in claim 7 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
At step 2B:
Dependent claims 5-7 recite the same additional elements as identified in step 2A prong two above. These additional elements are not sufficient to amount to significantly more than the judicial exception.
Dependent claims 5 and 7 respectively depend on independent claim 1 and independent claim 1 recites the limitations:
“receive first images and set values regarding capturing of the first images in a case where the first images are captured from one or more first communication terminals”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“receive a feature value of the first image determined based on an analysis of the first image for each first image”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“store a table that includes the first image, the set value, and the feature value in association with each other for each first image in a memory”, which is a step of storing data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
“search for the first image associated with a feature value coincident with or similar to a feature value of a second image received from a second communication terminal by the first processor from among the first images included in the table stored in the memory”, which is a step of searching for data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
“receive a set value determined to be associated with the first image searched for by the first processor from among the set values included in the table stored in the memory”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“transmit the set value acquired by the first processor to the second communication terminal”, which is a step of transmitting data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“wherein the feature value includes a scene of the first image and at least one of a model name or a lens name of a camera used for capturing of the first images included in header information of the first image”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Dependent claims 5 recites the limitations:
“wherein the first processor is configured to receive a plurality of the second images from the second communication terminal”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“the first processor is configured to search for a predetermined number of the first images from a side of which the total degree of similarity is the highest from among the first images stored in the memory”, which is a step of searching for data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Dependent claim 6 depends on dependent claim 5 and recites the limitations:
“the server according to claim 5,
wherein the first determination model is a trained model in which a first learning image and a feature value of the first learning image are used as pieces of first training data and a relationship between the first learning image and the feature value of the first learning image is trained for a plurality of the pieces of first training data, and
the second determination model is a trained model in which a feature value of a second learning image, a feature value of a third learning image, and a degree of similarity between the feature value of the second learning image and the feature value of the third learning image are used as pieces of second training data and a relationship between the feature value of the second learning image, the feature value of the third learning image, and the degree of similarity between the feature value of the second learning image and the feature value of the third learning image is trained for a plurality of the pieces of second training data”.
These limitations recite additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Dependent claims 7 recites the limitations:
“wherein the first processor is configured to receive a plurality of the second images from the second communication terminal”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“the first processor is configured to search for a predetermined number of the first images from a side on which the total degree of similarity is the highest from among the first images stored in the memory”, which is a step of searching for data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. iso
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 5-7, and 10-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kayser (US Pub 2022/0108447, provisional application 63/087,605 filed on 10/05/20) in view of Matsunaga (US Pub 2018/0061051) in view of De Bayser (US Pub 2017/0339340) and in further view of Sundaresan (US Pub 2016/0026866).
With respect to claim 1, Kayser discloses a server accessed by a plurality of communication terminals of a plurality of users, the server (Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data) comprising:
a first processor, wherein the first processor is configured (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data) to:
receive first images and set values regarding capturing of the first images in a case where the first images are captured from one or more first communication terminals (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part),
receive a feature value of the first image determined based on an analysis of the first image for each first image (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part),
store…the first image, the set value, and the feature value in association with each other for each first image in a memory (Kayser in [0025], [0052]-[0054], and [0078] discloses database residing on server storing different types of information such as image data, metadata associated with image, images of wound associated with settings used to capture the image, wound state etc., receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part; Kayser in [0100] and [0103] and in Figure 11 discloses server storing in memory; here Kayser does not explicitly disclose storing a table that includes image, set value, feature value in association with each other, but the De Bayser reference discloses the feature, as discussed below),
…the first image associated with a feature value coincident with or similar to a feature value of a second image received from a second communication terminal by the first processor from among the first images…stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Kayser in [0100] and [0103] and in Figure 11 discloses server storing in memory; here Kayser does not explicitly disclose searching for the first image associated with a feature value coincident with or similar to a feature value of a second image, but the Matsunaga reference discloses the feature, as discussed below),
receive a set value determined to be associated with the first image searched for by the first processor from among the set values…stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Kayser in [0100] and [0103] and in Figure 11 discloses server storing in memory; here Kayser does not explicitly disclose included in a table, but the De Bayser reference discloses the feature, as discussed below), and
transmit the set value acquired by the first processor to the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image);
wherein the feature value includes…at least one of…used for capturing of the first images included in…the first image (Kayser in [0007], [0044], and [0054] discloses image depicting a body part or a user or person, image depicting a wound, camera setting includes settings for one or more of aperture, shutter speed, ISO, which are stored as metadata associated with an image; here Kayser does not explicitly disclose feature value includes a scene of the first image and at least one or a model name or a lens name of a camera used for capturing of the first images included in header information of the first image, but the Sundaresan references disclose the features, as discussed below).
Kayser discloses identifying a first image associated with a feature value coincident with or similar to a feature value of a second image received from a second communication terminal by the first processor from among the first images stored in the memory, however, Kayser does not explicitly disclose:
searches for the first image associated with a feature value coincident with or similar to a feature value of a second image;
The Matsunaga reference discloses searching for a first image associated with a feature value coincident with or similar to a feature value of a second image (Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser and Matsunaga, to have combined Kayser and Matsunaga. The motivation to combine Kayser and Matsunaga would be to assist in diagnosing data in a query image by searching for a candidate image that is similar to the query image for comparison (Matsunaga: [0005] and [0035]).
Kayser discloses storing image data, features, and settings in a database residing on a server comprising memory for storage, however, Kayser and Matsunaga do not explicitly disclose:
store a table that includes the first image, the set value, and the feature value in association with each other for each first image;
The De Bayser reference discloses storing a table that includes a first image, a set value, and a feature value in association with each other for each first image (De Bayser [0025], [0094], and [0099] and in Figure 8 discloses database stored in memory, photos including associated features and settings stored in database, database tabulates and stores pictures and associated data in one or more tables which includes camera settings associated with one or more features, the features associated with one or more captured images).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, and De Bayser, to have combined Kayser, Matsunaga, and De Bayser. The motivation to combine Kayser, Matsunaga, and De Bayser would be to automatically adjust camera settings by learning a user’s preferences from stored settings associated with ideal or near ideal pictures (De Bayser: [0069], [0097], and [0099]).
Kayser discloses image including feature values and setting values used for capturing the image, however, Kayser, Matsunaga, and De Bayser do not explicitly disclose:
…the feature value includes a scene of the first image and at least one of a model name or a lens name of a camera used for capturing of the first images included in header information of the first image;
The Sundaresan reference discloses a feature value including a scene of a first image and at least one of a model name or a lens name of a camera used for capturing of first images included in header information of the first image (Sundaresan in [0019] and [0062] discloses providing feedback of recommendations to a user based on camera metadata obtained from an image, camera metadata covers a broad spectrum of metadata tags, context data related to environment or scene conditions or other metadata used to analyze image file, feedback data includes distance between a scene and a camera, Exif file data configuration includes a header, a thumbnail, and primary image data, camera metadata referred to as Exif data, header stores camera metadata recording camera information in image files, header includes data fields for storing the camera metadata, such as camera make and model, date and time of an image capture, shutter speed, lens used, distance to a subject, and other technical details, Exif file data includes thumbnail along with technical and primary image data, represented by header and primary image data, stored in a single image file; Sundaresan in [0033] and [0036] discloses analyzing context of an image file to determine scene conditions, feedback generated using one or a combination of information types, such as camera metadata, other metadata, context information related to environment or scene conditions, or user’s purpose for taking the pictures).
Therefore, it would have been obvious to a person ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, De Bayser, and Sundaresan, to have combined Kayser, Matsunaga, De Bayser, and Sundaresan. The motivation to combine Kayser, Matsunaga, De Bayser, and Sundaresan would be to provide feedback or recommendations to a user based on camera metadata obtained from an image (Sundaresan: [0019]).
With respect to claim 2, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the server according to claim 1, wherein the first processor is configured to:
store the first image, the set value, and the feature value in association with each other for each user in the memory (Kayser in [0025], [0052]-[0054], and [0078] discloses database residing on server storing different types of information such as image data, metadata associated with image, images of wound associated with settings used to capture the image, wound state etc., receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part),
receive an image preference of a user of the second communication terminal based on the feature value of the first image of the user of the second communication terminal read from the memory by the first processor (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times), and
search for a first image associated with a feature value coincident with or similar to the image preference of the user of the second communication terminal from among the first images stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors).
With respect to claim 5, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the server according to claim 1,
wherein the first processor is configured to receive a plurality of the second images from the second communication terminal (Kayser in [0005], [0031], [0049], and [0068] discloses machine-learned models trained to classify features in images, inputting images into the models to classify, receive classification based on training data, train models by identifying relationships between images and at least a portion of the training data, input image and body part type into one of the models trained to determine whether an image depicts a body part of the body part types inputted, machine learning models comprises supervised, such as classification or similarity model, unsupervised, and/or semi-supervised models configured to determine a classification, models receive multiple instances of image data captured at different times, image data comprising plurality of images; Kaiser in [0069], [0086], and [0089] and in Figure 9 determine from the image data characteristics of the images and classifying based on the characteristics, machine-learned model trained using training data generated based on historical images, models transmitted to patient device and on clinician device to predict and classify image characteristics based on image features),
the feature value of the first image and the feature value of each second image received by the first processor are estimated by using a first determination model (Kayser in [0005], [0031], [0049], and [0068] discloses machine-learned models trained to classify features in images, inputting images into the models to classify, receive classification based on training data, train models by identifying relationships between images and at least a portion of the training data, input image and body part type into one of the models trained to determine whether an image depicts a body part of the body part types inputted, machine learning models comprises supervised, such as classification or similarity model, unsupervised, and/or semi-supervised models configured to determine a classification, models receive multiple instances of image data captured at different times, image data comprising plurality of images; Kaiser in [0069], [0086], and [0089] and in Figure 9 determine from the image data characteristics of the images and classifying based on the characteristics, machine-learned model trained using training data generated based on historical images, models transmitted to patient device and on clinician device to predict and classify image characteristics based on image features),
degrees of similarity between the feature value of the first image and the feature values of the second images received by the first processor are estimated for each first image by using a second determination model (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors),
a total degree of similarity between the feature value of the first image and the feature values of the plurality of second images received by the first processor is calculated based on the degrees of similarity (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors), and
the first processor is configured to search for a predetermined number of the first images from a side of which the total degree of similarity is the highest from among the first images stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors).
With respect to claim 6, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the server according to claim 5,
wherein the first determination model is a trained model in which a first learning image and a feature value of the first learning image are used as pieces of first training data and a relationship between the first learning image and the feature value of the first learning image is trained for a plurality of the pieces of first training data (Kayser in [0005], [0031], [0049], and [0068] discloses machine-learned models trained to classify features in images, inputting images into the models to classify, receive classification based on training data, train models by identifying relationships between images and at least a portion of the training data, input image and body part type into one of the models trained to determine whether an image depicts a body part of the body part types inputted, machine learning models comprises supervised, such as classification or similarity model, unsupervised, and/or semi-supervised models configured to determine a classification, models receive multiple instances of image data captured at different times, image data comprising plurality of images; Kaiser in [0069], [0086], and [0089] and in Figure 9 determine from the image data characteristics of the images and classifying based on the characteristics, machine-learned model trained using training data generated based on historical images, models transmitted to patient device and on clinician device to predict and classify image characteristics based on image features), and
the second determination model is a trained model in which a feature value of a second learning image, a feature value of a third learning image, and a degree of similarity between the feature value of the second learning image and the feature value of the third learning image are used as pieces of second training data and a relationship between the feature value of the second learning image, the feature value of the third learning image, and the degree of similarity between the feature value of the second learning image and the feature value of the third learning image is trained for a plurality of the pieces of second training data (Kayser in [0005], [0031], [0049], and [0068] discloses machine-learned models trained to classify features in images, inputting images into the models to classify, receive classification based on training data, train models by identifying relationships between images and at least a portion of the training data, input image and body part type into one of the models trained to determine whether an image depicts a body part of the body part types inputted, machine learning models comprises supervised, such as classification or similarity model, unsupervised, and/or semi-supervised models configured to determine a classification, models receive multiple instances of image data captured at different times, image data comprising plurality of images; Kaiser in [0069], [0086], and [0089] and in Figure 9 determine from the image data characteristics of the images and classifying based on the characteristics, machine-learned model trained using training data generated based on historical images, models transmitted to patient device and on clinician device to predict and classify image characteristics based on image features).
With respect to claim 7, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the server according to claim 1,
wherein the first processor is configured to receive a plurality of the second images from the second communication terminal (Kayser in [0005], [0031], [0049], and [0068] discloses machine-learned models trained to classify features in images, inputting images into the models to classify, receive classification based on training data, train models by identifying relationships between images and at least a portion of the training data, input image and body part type into one of the models trained to determine whether an image depicts a body part of the body part types inputted, machine learning models comprises supervised, such as classification or similarity model, unsupervised, and/or semi-supervised models configured to determine a classification, models receive multiple instances of image data captured at different times, image data comprising plurality of images; Kaiser in [0069], [0086], and [0089] and in Figure 9 determine from the image data characteristics of the images and classifying based on the characteristics, machine-learned model trained using training data generated based on historical images, models transmitted to patient device and on clinician device to predict and classify image characteristics based on image features),
the feature value of the first image and the feature value of each second image received by the first processor are estimated by using a first determination model (Kayser in [0005], [0031], [0049], and [0068] discloses machine-learned models trained to classify features in images, inputting images into the models to classify, receive classification based on training data, train models by identifying relationships between images and at least a portion of the training data, input image and body part type into one of the models trained to determine whether an image depicts a body part of the body part types inputted, machine learning models comprises supervised, such as classification or similarity model, unsupervised, and/or semi-supervised models configured to determine a classification, models receive multiple instances of image data captured at different times, image data comprising plurality of images; Kaiser in [0069], [0086], and [0089] and in Figure 9 discloses determine from the image data characteristics of the images and classifying based on the characteristics, machine-learned model trained using training data generated based on historical images, models transmitted to patient device and on clinician device to predict and classify image characteristics based on image features),
distances between the feature value of the first image and the feature values of the second images received by the first processor are calculated for each first image (Matsunaga in [0035] and [0057] discloses compare feature vector of a first image with feature vector of second image, searching at least one candidate that is similar to the query image among reference images, calculating a distance between feature vector of first image with feature vectors of stored second images),
a total degree of similarity between the feature value of the first image and the feature values of the plurality of second images received by the first processor is calculated based on the distances (Matsunaga in [0035] and [0057] discloses compare feature vector of a first image with feature vector of second image, searching at least one candidate that is similar to the query image among reference images, calculating a distance between feature vector of first image with feature vectors of stored second images), and
the first processor is configured to search for a predetermined number of the first images from a side on which the total degree of similarity is the highest from among the first images stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors; searching at least one candidate that is similar to the query image among reference images, calculating a distance between feature vector of first image with feature vectors of stored second images).
With respect to claim 10, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the server according to claim 1, wherein the first processor is configured to:
receive information on a first image selected by a user of the second communication terminal from among a plurality of the first images searched for by the first processor (Kayser in [0024] and [0044] discloses server communicates with user devices to responds to queries and receive data), determine one or more responses to queries and respond back to the user devices, user selecting a body part at which a wound is located, capturing an image of the wound after the body part is selected; Matsunaga in [0028], [0035], and [0057] select information in an image, compare reference image with query image, search at least a candidate that is similar to the query image, output the searched candidate), and
acquire a set value associated with a first image corresponding to the information on the first image from among the set values stored in the memory (Kayser in [0024] and [0044] discloses server communicates with user devices to responds to queries and receive data), determine one or more responses to queries and respond back to the user devices, user selecting a body part at which a wound is located, capturing an image of the wound after the body part is selected; Matsunaga in [0028], [0035], and [0057] discloses select information in an image, compare reference image with query image, search at least a candidate that is similar to the query image, output the searched candidate).
With respect to claim 11, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the server according to claim 1, wherein the set value includes at least one value of a stop, a shutter speed, a focal length, or an ISO sensitivity in a case where the first image is captured (Kayser in [0054] discloses camera setting includes settings for one or more of aperture, shutter speed, ISO, which are stored as metadata associated with an image).
With respect to claim 12, Kayser discloses an imaging system (Kayser in [0007] discloses a system) comprising:
the server according to claim 1 (see rejection of claim 1 above); and
the plurality of communication terminals (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data),
wherein the first communication terminal includes a second processor (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data),
the second communication terminal includes a third processor (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data),
the second processor is configured to transmit the first image and the set value to the server (Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data), and
the third processor is configured (Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data) to:
receive the set value acquired by the first processor from the server (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image),
set the set value received by the third processor for the camera of the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image), and
capture an image based on the set value set by the third processor (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times).
Kayser discloses the server according to claim 1, however, Kayser in claim 1 does not explicitly disclose:
search for the first image associated with a feature value coincident with or similar to a feature value of a second image;
The Matsunaga reference discloses searching for a first image associated with a feature value coincident with or similar to a feature value of a second image (Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser and Matsunaga, to have combined Kayser and Matsunaga. The motivation to combine Kayser and Matsunaga would be to assist in diagnosing data in a query image by searching for a candidate image that is similar to the query image for comparison (Matsunaga: [0005] and [0035]).
Kayser discloses the server according to claim 1 and further discloses storing image data, features, and settings in a database residing on the server comprising memory for storage, however, Kayser and Matsunaga do not explicitly disclose:
store a table that includes the first image, the set value, and the feature value in association with each other for each first image;
The De Bayser reference discloses storing a table that includes a first image, a set value, and a feature value in association with each other for each first image (De Bayser [0025], [0094], and [0099] and in Figure 8 discloses database stored in memory, photos including associated features and settings stored in database, database tabulates and stores pictures and associated data in one or more tables which includes camera settings associated with one or more features, the features associated with one or more captured images).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, and De Bayser, to have combined Kayser, Matsunaga, and De Bayser. The motivation to combine Kayser, Matsunaga, and De Bayser would be to automatically adjust camera settings by learning a user’s preferences from stored settings associated with ideal or near ideal pictures (De Bayser: [0069], [0097], and [0099]).
Kayser discloses image including feature values and setting values used for capturing the image, however, Kayser, Matsunaga, and De Bayser do not explicitly disclose:
…the feature value includes a scene of the first image and at least one of a model name or a lens name of a camera used for capturing of the first images included in header information of the first image;
The Sundaresan reference discloses a feature value including a scene of a first image and at least one of a model name or a lens name of a camera used for capturing of first images included in header information of the first image (Sundaresan in [0019] and [0062] discloses providing feedback of recommendations to a user based on camera metadata obtained from an image, camera metadata covers a broad spectrum of metadata tags, context data related to environment or scene conditions or other metadata used to analyze image file, feedback data includes distance between a scene and a camera, Exif file data configuration includes a header, a thumbnail, and primary image data, camera metadata referred to as Exif data, header stores camera metadata recording camera information in image files, header includes data fields for storing the camera metadata, such as camera make and model, date and time of an image capture, shutter speed, lens used, distance to a subject, and other technical details, Exif file data includes thumbnail along with technical and primary image data, represented by header and primary image data, stored in a single image file; Sundaresan in [0033] and [0036] discloses analyzing context of an image file to determine scene conditions, feedback generated using one or a combination of information types, such as camera metadata, other metadata, context information related to environment or scene conditions, or user’s purpose for taking the pictures).
Therefore, it would have been obvious to a person ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, De Bayser, and Sundaresan, to have combined Kayser, Matsunaga, De Bayser, and Sundaresan. The motivation to combine Kayser, Matsunaga, De Bayser, and Sundaresan would be to provide feedback or recommendations to a user based on camera metadata obtained from an image (Sundaresan: [0019]).
With respect to claim 13, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the imaging system according to claim 12,
wherein the communication terminal includes a camera (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data),
a camera of the first communication terminal includes the second processor (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data),
a camera of the second communication terminal includes the third processor (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data),
the second processor is configured to transmit the first image and the set value to the server (Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data), and
the third processor is configured (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data) to:
transmit the feature value of the second image to the server (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part),
receive the set value acquired by the first processor from the server (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image),
set the set value received by the third processor for the camera of the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times), and
capture an image based on the set value set by the third processor (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times).
With respect to claim 14, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the imaging system according to claim 13,
wherein the following operations are repeated in which the third processor is configured to recognize a subject appearing in a live preview image captured by the third processor, and outputs information on the subject (Kayser in [0047] discloses camera feed may be a live feed of a body part having a wound, capturing an image of the feed), and
whenever the subject appearing in the live preview image is recognized by the third processor and different information on the subject is output (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part),
the third processor is configured to transmit the information on the subject to the server (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times),
the first processor is configured to:
receive the information on the subject from the camera of the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times),
search for a first image associated with a feature value of a subject coincident with or similar to the information on the subject from among the first images stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors),
acquire a set value associated with the first image searched for by the first processor from among the set values stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times), and
transmit the set value acquired by the first processor to the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times), and
the third processor is configured to:
receive the set value acquired by the first processor from the server (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times), and
set the set value received by the third processor for the camera of the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times).
With respect to claim 15, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the imaging system according to claim 12,
wherein the communication terminal includes a camera having a communication function (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data) and
an information terminal having a communication function, an information terminal of the first communication terminal includes the second processor (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data),
an information terminal of the second communication terminal includes a fourth processor (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data),
a camera of the second communication terminal includes a fifth processor (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data),
the second processor is configured to transmit the first image and the set value received from a camera of the first communication terminal by the communication function to the server (Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data),
the fourth processor is configured to transmit the feature value of the second image to the server, and receive the set value acquired by the first processor from the server (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times), and
the fifth processor is configured to set the set value received from the information terminal of the second communication terminal by the communication function for the camera of the second communication terminal, and capture an image based on the set value set by the fifth processor (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times).
With respect to claim 16, Kayser discloses a communication terminal (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data) comprising:
a second processor, wherein the second processor is configured (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data) to:
transmit at least one of the first image, the set value, or the feature value of the second image to the server according to claim 1 (Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data; see rejection of claim 1 above),
receive the set value acquired by the first processor from the server (Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data),
capture an image based on the set value set by the second processor (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times).
Kayser discloses the server according to claim 1, however, Kayser in claim 1 does not explicitly disclose:
search for the first image associated with a feature value coincident with or similar to a feature value of a second image;
The Matsunaga reference discloses searching for a first image associated with a feature value coincident with or similar to a feature value of a second image (Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser and Matsunaga, to have combined Kayser and Matsunaga. The motivation to combine Kayser and Matsunaga would be to assist in diagnosing data in a query image by searching for a candidate image that is similar to the query image for comparison (Matsunaga: [0005] and [0035]).
Kayser discloses the server according to claim 1 and further discloses storing image data, features, and settings in a database residing on a server comprising memory for storage, however, Kayser and Matsunaga do not explicitly disclose:
store a table that includes the first image, the set value, and the feature value in association with each other for each first image;
The De Bayser reference discloses storing a table that includes a first image, a set value, and a feature value in association with each other for each first image (De Bayser [0025], [0094], and [0099] and in Figure 8 discloses database stored in memory, photos including associated features and settings stored in database, database tabulates and stores pictures and associated data in one or more tables which includes camera settings associated with one or more features, the features associated with one or more captured images).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, and De Bayser, to have combined Kayser, Matsunaga, and De Bayser. The motivation to combine Kayser, Matsunaga, and De Bayser would be to automatically adjust camera settings by learning a user’s preferences from stored settings associated with ideal or near ideal pictures (De Bayser: [0069], [0097], and [0099]).
Kayser discloses image including feature values and setting values used for capturing the image, however, Kayser, Matsunaga, and De Bayser do not explicitly disclose:
…the feature value includes a scene of the first image and at least one of a model name or a lens name of a camera used for capturing of the first images included in header information of the first image;
The Sundaresan reference discloses a feature value including a scene of a first image and at least one of a model name or a lens name of a camera used for capturing of first images included in header information of the first image (Sundaresan in [0019] and [0062] discloses providing feedback of recommendations to a user based on camera metadata obtained from an image, camera metadata covers a broad spectrum of metadata tags, context data related to environment or scene conditions or other metadata used to analyze image file, feedback data includes distance between a scene and a camera, Exif file data configuration includes a header, a thumbnail, and primary image data, camera metadata referred to as Exif data, header stores camera metadata recording camera information in image files, header includes data fields for storing the camera metadata, such as camera make and model, date and time of an image capture, shutter speed, lens used, distance to a subject, and other technical details, Exif file data includes thumbnail along with technical and primary image data, represented by header and primary image data, stored in a single image file; Sundaresan in [0033] and [0036] discloses analyzing context of an image file to determine scene conditions, feedback generated using one or a combination of information types, such as camera metadata, other metadata, context information related to environment or scene conditions, or user’s purpose for taking the pictures).
Therefore, it would have been obvious to a person ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, De Bayser, and Sundaresan, to have combined Kayser, Matsunaga, De Bayser, and Sundaresan. The motivation to combine Kayser, Matsunaga, De Bayser, and Sundaresan would be to provide feedback or recommendations to a user based on camera metadata obtained from an image (Sundaresan: [0019]).
With respect to claim 17, Kayser discloses an imaging method in an imaging system that includes a plurality of communication terminals of a plurality of users and a server accessed by the plurality of communication terminals (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data; Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data), the method comprising:
configuring a second processor of one or more first communication terminals to transmit first images captured by the first communication terminals and set values regarding capturing of the first communication terminals in a case where the first images are captured to the server (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data; Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data);
configuring a first processor of the server (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data; Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data) to:
receive the first image and the set value of the first image from the first communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times);
receive a feature value of the first image determined based on an analysis of the first image for each first image (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times);
store…the first image, and the set value and the feature value of the first image in association with each other for each first image in a memory of the server (Kayser in [0025], [0052]-[0054], and [0078] discloses database residing on server storing different types of information such as image data, metadata associated with image, images of wound associated with settings used to capture the image, wound state etc., receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part; Kayser in [0100] and [0103] and in Figure 11 server storing in memory);
configuring a third processor of a second communication terminal to transmit a feature value of a second image to the server (Kayser in [0052]-[0054] discloses– receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times);
configuring the first processor to receive the feature value of the second image from the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times);
configuring the first processor (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data; Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data) to:
…a first image associated with a feature value coincident with or similar to the feature value of the second image from among the first images…stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times; Kayser in [0100] and [0103] and in Figure 11 server storing in memory);
receive a set value determined to be associated with the first image…from among the set values…stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times; Kayser in [0100] and [0103] and in Figure 11 server storing in memory); and
transmit the set value acquired by a set value acquisition unit to the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times);
configuring the third processor (Kayser in [0007] and [0023] discloses devices include a camera and one or more processors to perform operations such as receiving a feed and/or capturing an image of the feed, devices configured to communicate image data; Kayser in [0024] and [0025] discloses one or more server computing devices communicate with devices, receives data, and responds to queries, database residing on server storing different type of information such as image data) to:
receive the set value acquired by the set value acquisition unit from the server (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times);
capture an image based on the set value that is set (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times),
wherein the feature value includes…at least one of…used for capturing of the first images included in…the first image (Kayser in [0007], [0044], and [0054] discloses image depicting a body part or a user or person, image depicting a wound, camera setting includes settings for one or more of aperture, shutter speed, ISO, which are stored as metadata associated with an image; here Kayser does not explicitly disclose feature value includes a scene of the first image and at least one or a model name or a lens name of a camera used for capturing of the first images included in header information of the first image, but the Sundaresan reference discloses the feature, as discussed below).
Kayser discloses identifying a first image associated with a feature value coincident with or similar to a feature value of a second image received from a second communication terminal by the first processor from among the first images stored in the memory, however, Kayser does not explicitly disclose:
search a first image associated with a feature value coincident with or similar to the feature value of the second image;
The Matsunaga reference discloses searching a first image associated with a feature value coincident with or similar to a feature value of a second image (Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser and Matsunaga, to have combined Kayser and Matsunaga. The motivation to combine Kayser and Matsunaga would be to assist in diagnosing data in a query image by searching for a candidate image that is similar to the query image for comparison (Matsunaga: [0005] and [0035]).
Kayser discloses storing image data, features, and settings in a database residing on a server comprising memory for storage, however, Kayser and Matsunaga do not explicitly disclose:
store a table that includes the first image, the set value, and the feature value of the first image in association with each other for each first image;
The De Bayser reference discloses storing a table that includes a first image, a set value, and a feature value in association with each other for each first image (De Bayser [0025], [0094], and [0099] and in Figure 8 discloses database stored in memory, photos including associated features and settings stored in database, database tabulates and stores pictures and associated data in one or more tables which includes camera settings associated with one or more features, the features associated with one or more captured images).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, and De Bayser, to have combined Kayser, Matsunaga, and De Bayser. The motivation to combine Kayser, Matsunaga, and De Bayser would be to automatically adjust camera settings by learning a user’s preferences from stored settings associated with ideal or near ideal pictures (De Bayser: [0069], [0097], and [0099]).
Kayser discloses image including feature values and setting values used for capturing the image, however, Kayser, Matsunaga, and De Bayser do not explicitly disclose:
…the feature value includes a scene of the first image and at least one of a model name or a lens name of a camera used for capturing of the first images included in header information of the first image;
The Sundaresan reference discloses a feature value including a scene of a first image and at least one of a model name or a lens name of a camera used for capturing of first images included in header information of the first image (Sundaresan in [0019] and [0062] discloses providing feedback of recommendations to a user based on camera metadata obtained from an image, camera metadata covers a broad spectrum of metadata tags, context data related to environment or scene conditions or other metadata used to analyze image file, feedback data includes distance between a scene and a camera, Exif file data configuration includes a header, a thumbnail, and primary image data, camera metadata referred to as Exif data, header stores camera metadata recording camera information in image files, header includes data fields for storing the camera metadata, such as camera make and model, date and time of an image capture, shutter speed, lens used, distance to a subject, and other technical details, Exif file data includes thumbnail along with technical and primary image data, represented by header and primary image data, stored in a single image file; Sundaresan in [0033] and [0036] discloses analyzing context of an image file to determine scene conditions, feedback generated using one or a combination of information types, such as camera metadata, other metadata, context information related to environment or scene conditions, or user’s purpose for taking the pictures).
Therefore, it would have been obvious to a person ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, De Bayser, and Sundaresan, to have combined Kayser, Matsunaga, De Bayser, and Sundaresan. The motivation to combine Kayser, Matsunaga, De Bayser, and Sundaresan would be to provide feedback or recommendations to a user based on camera metadata obtained from an image (Sundaresan: [0019]).
With respect to claim 18, Kayser discloses a non-transitory computer-readable recording medium having a program causing a computer to execute the steps of the imaging method according to claim 17 recorded thereon (see rejection of claim 17 above).
Kayser discloses the imaging method according to claim 18, however, Kayser in claim 18 does not explicitly disclose:
search a first image associated with a feature value coincident with or similar to the feature value of the second image;
The Matsunaga reference discloses searching a first image associated with a feature value coincident with or similar to a feature value of a second image (Matsunaga in [0029] and [0057] discloses device including a search engine for searching at least a candidate image similar to a query image by comparing first image feature vectors with the second image feature vectors).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser and Matsunaga, to have combined Kayser and Matsunaga. The motivation to combine Kayser and Matsunaga would be to assist in diagnosing data in a query image by searching for a candidate image that is similar to the query image for comparison (Matsunaga: [0005] and [0035]).
Kayser discloses the method according to claim 18 and further discloses storing image data, features, and settings in a database residing on a server comprising memory for storage, however, Kayser and Matsunaga do not explicitly disclose:
store a table that includes the first image, the set value, and the feature value in association with each other for each first image;
The De Bayser reference discloses storing a table that includes a first image, a set value, and a feature value in association with each other for each first image (De Bayser [0025], [0094], and [0099] and in Figure 8 discloses database stored in memory, photos including associated features and settings stored in database, database tabulates and stores pictures and associated data in one or more tables which includes camera settings associated with one or more features, the features associated with one or more captured images).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, and De Bayser, to have combined Kayser, Matsunaga, and De Bayser. The motivation to combine Kayser, Matsunaga, and De Bayser would be to automatically adjust camera settings by learning a user’s preferences from stored settings associated with ideal or near ideal pictures (De Bayser: [0069], [0097], and [0099]).
Kayser discloses image including feature values and setting values used for capturing the image, however, Kayser, Matsunaga, and De Bayser do not explicitly disclose:
Kayser discloses image including feature values and setting values used for capturing the image, however, Kayser, Matsunaga, and De Bayser do not explicitly disclose:
…the feature value includes a scene of the first image and at least one of a model name or a lens name of a camera used for capturing of the first images included in header information of the first image;
The Sundaresan reference discloses a feature value including a scene of a first image and at least one of a model name or a lens name of a camera used for capturing of first images included in header information of the first image (Sundaresan in [0019] and [0062] discloses providing feedback of recommendations to a user based on camera metadata obtained from an image, camera metadata covers a broad spectrum of metadata tags, context data related to environment or scene conditions or other metadata used to analyze image file, feedback data includes distance between a scene and a camera, Exif file data configuration includes a header, a thumbnail, and primary image data, camera metadata referred to as Exif data, header stores camera metadata recording camera information in image files, header includes data fields for storing the camera metadata, such as camera make and model, date and time of an image capture, shutter speed, lens used, distance to a subject, and other technical details, Exif file data includes thumbnail along with technical and primary image data, represented by header and primary image data, stored in a single image file; Sundaresan in [0033] and [0036] discloses analyzing context of an image file to determine scene conditions, feedback generated using one or a combination of information types, such as camera metadata, other metadata, context information related to environment or scene conditions, or user’s purpose for taking the pictures).
Therefore, it would have been obvious to a person ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, De Bayser, and Sundaresan, to have combined Kayser, Matsunaga, De Bayser, and Sundaresan. The motivation to combine Kayser, Matsunaga, De Bayser, and Sundaresan would be to provide feedback or recommendations to a user based on camera metadata obtained from an image (Sundaresan: [0019]).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kayser (US Pub 2022/0108447, provisional application 63/087,605 filed on 10/05/20) in view of Matsunaga (US Pub 2018/0061051) in view of in view of De Bayser (US Pub 2017/0339340) in view of Sundaresan (US Pub 2016/0026866) and in further view of Chang (US Pub 2015/0186425).
With respect to claim 3, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the server according to claim 1, wherein the first processor (Kayser: [0007] and [0023]) is configured to:
receive…for the first image by a user of the first communication terminal from the first communication terminal (Kayser in [0025], [0052]-[0054], and [0078] discloses database residing on server storing different types of information such as image data, metadata associated with image, images of wound associated with settings used to capture the image, wound state etc., receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part),
store the first image and…for the first image in association with each other in the memory (Kayser in [0025], [0052]-[0054], and [0078] discloses database residing on server storing different types of information such as image data, metadata associated with image, images of wound associated with settings used to capture the image, wound state etc., receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part),
receive a search instruction…from the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors), and
search for a predetermined number of the first images…from among the first images stored in the memory in response to the instruction (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors).
Kayser and Sundaresan discloses receiving various information for a first image by a user, however, Kayser, Matsunaga, De Bayser, and Sundaresan do not explicitly disclose:
receive at least one of a positive evaluation or a negative evaluation voted for the first image by a user;
store the number of all evaluations voted for the first image;
receive a search instruction based on the evaluation;
The Chang reference discloses receiving at least one of a positive evaluation or a negative evaluation voted for a first image by a user, storing the number of all evaluations voted for the first image, and receiving a search instruction based on the evaluation (Chang in [0022] and [0027] discloses actively learn query concept of a user based on content and context information such as time and date information of a plurality of selected images, active learning is an iterative process of relevance feedback, storing date and time; Chang in [0042] and [0046] discloses solicit user for feedback or relevance feedback, a selected image labeled as a positive instance and included in a learned query concept; Chang in [0038] and [0053] discloses searching based on time period).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, De Bayser, Sundaresan, and Chang, to have combined Kayser, Matsunaga, De Bayser, Sundaresan, and Chang. The motivation to combine Kayser, Matsunaga, De Bayser, Sundaresan, and Chang would be to quickly and accurately identify relevant images via actively learning query concept of a user (Chang: [0005] and [0022]).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kayser (US Pub 2022/0108447, provisional application 63/087,605 filed on 10/05/20) in view of Matsunaga (US Pub 2018/0061051) in view of in view of De Bayser (US Pub 2017/0339340) in view of Sundaresan (US Pub 2016/0026866) in view of Chang (US Pub 2015/0186425) and in further view of Kim (US Pub 2016/0132533).
With respect to claim 4, Kayser in view of Matsunaga in view of De Bayser in view of Sundaresan and in further view of Chang discloses the server according to claim 3, wherein the first processor (Kayser: [0007] and [0023]) is configured to:
receive a…date and time…from the first communication terminal (Kayser in [0025], [0052]-[0054], and [0078] discloses database residing on server storing different types of information such as image data, metadata associated with image, images of wound associated with settings used to capture the image, wound state etc., receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part; Chang in [0022] and [0027] discloses actively learn query concept of a user based on content and context information such as time and date information of a plurality of selected images, active learning is an iterative process of relevance feedback, storing date and time; Chang in [0042] and [0046] discloses solicit user for feedback or relevance feedback, a selected image labeled as a positive instance and included in a learned query concept; Chang in [0038] and [0053] discloses searching based on time period),
store the first image and the…date and time…in association with each other in the memory (Kayser in [0025], [0052]-[0054], and [0078] discloses database residing on server storing different types of information such as image data, metadata associated with image, images of wound associated with settings used to capture the image, wound state etc., receive image having characteristics or features captured in a particular setting, determine setting values on a received image; Kayser in [0044], [0066], and [0067] and in Figures 8-10 discloses receive image from a device, image included with image data including metadata associated with image such as camera settings used for capture, determining image data is depicting a wound of a body part; Chang in [0022] and [0027] discloses actively learn query concept of a user based on content and context information such as time and date information of a plurality of selected images, active learning is an iterative process of relevance feedback, storing date and time; Chang in [0042] and [0046] discloses solicit user for feedback or relevance feedback, a selected image labeled as a positive instance and included in a learned query concept; Chang in [0038] and [0053] discloses searching based on time period),
receive a…period…from the second communication terminal (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors; Chang in [0022] and [0027] discloses actively learn query concept of a user based on content and context information such as time and date information of a plurality of selected images, active learning is an iterative process of relevance feedback, storing date and time; Chang in [0042] and [0046] discloses solicit user for feedback or relevance feedback, a selected image labeled as a positive instance and included in a learned query concept; Chang in [0038] and [0053] discloses searching based on time period), and
search for a first image for which the evaluation is voted at the…date and time in the…period…form among the first images stored in the memory (Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image; Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors; Chang in [0022] and [0027] discloses actively learn query concept of a user based on content and context information such as time and date information of a plurality of selected images, active learning is an iterative process of relevance feedback, storing date and time; Chang in [0042] and [0046] discloses solicit user for feedback or relevance feedback, a selected image labeled as a positive instance and included in a learned query concept; Chang in [0038] and [0053] discloses searching based on time period; here Chang discloses a voting of an evaluation and searching for images at a date and time within a period, however, Kayser, Matsunaga, De Bayser, Sundaresan, and Chang do not explicitly disclose a voting date and time of the evaluation).
Kayser discloses receiving image data and associated information from a communication terminal, and storing image and information in associating with each other in memory, Matsunaga discloses receiving a request and searching for a first image from among first images stored in memory, and Chang discloses receiving date and time, storing image and the date and time in associating with each other, receiving a period and searching for a first image for which an evaluation is voted in a period from among first images stored in a memory, however, Kayser, Matsunaga, De Bayser, Sundaresan, and Chang do not explicitly disclose:
receive a voting date and time of the evaluation;
store the voting date and time of the evaluation;
voting period of the evaluation;
The Kim reference discloses receiving a voting date and time of an evaluation, storing the voting date and time of the evaluation, and a voting period of the evaluation (Kim in [0041], [0042], and [0044] discloses detecting statistics on information about a user in different time periods, storing information about a user in the form of a date, a time at which a user clicked a ‘like’ button for images is stored in connection with the images, which is storing a voting date and time of an evaluation by a user).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, De Bayser, Sundaresan, Chang, and Kim, to have combined Kayser, Matsunaga, De Bayser, Sundaresan, Chang, and Kim. The motivation to combine Kayser, Matsunaga, De Bayser, Sundaresan, Chang, and Kim would be to provide user with a related image by collecting information about the user (Kim: [0002]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kayser (US Pub 2022/0108447, provisional application 63/087,605 filed on 10/05/20) in view of Matsunaga (US Pub 2018/0061051) in view of De Bayser (US Pub 2017/0339340) in view of Sundaresan (US Pub 2016/0026866) and in further view of Takahashi (US Pub 2011/0200980).
With respect to claim 8, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the server according to claim 1, wherein the first processor (Kayser: [0007] and [0023]) is configured to:
store the first image, the set value, the feature value, and a text-format… expressing the feature value of the first image in association with each other in the memory (Matsunaga in [0036] and [0070] discloses storing image and various pieces of data, storing feature vector, storing identification names for diseases and known images in association with each other in memory; here Kayser, Matsunaga, De Bayser, and Sundaresan do not explicitly disclose a tag, but the Takahashi reference discloses the feature, as discussed below),
receive a…search…from the second communication terminal (Matsunaga in [0029] and [0057] discloses device including a search engine for searching at least a candidate image similar to a query image by comparing first image feature vectors with the second image feature vectors; here Kayser, Matsunaga, De Bayser, and Sundaresan do not explicitly disclose a text-format search key, but the Takahashi reference discloses the feature, as discussed below), and
search for the first image…coincident with the search…from among the first images stored in the memory (Matsunaga in [0035] and [0057] discloses searching at least a candidate image similar to a query image comparing first image feature vectors with the second image feature vectors; here Kayser, Matsunaga, De Bayser, and Sundaresan do not explicitly disclose search for an image to which a tag coincident with a search key is assigned, but the Takahashi reference discloses the feature, as discussed below).
Kayser discloses storing images and various information expressing feature value of the images in association with each other in memory and receiving a request from a second communication terminal, Matsunaga discloses receiving a search and searching for a first image coincident with the search among the first images stored in a memory, and De Bayser discloses tagging, however, Kayser, Matsunaga, De Bayser, and Sundaresan do not explicitly disclose:
store a tag, receive a text-format search key, and search for the first image to which a tag coincident with the search key is assigned;
The Takahashi reference discloses storing a tag, receiving a text-format search key, and searching for a first image to which a tag coincident with the search key is assigned (Takahashi in [0055], [0056], [0088] discloses adding tag information by storing it directly in image data, image data is stored according to tag information added, browse related images by referring tag information as a keyword for searching for same and similar attribute information).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, De Bayser, Sundaresan, and Takahashi, to have combined Kayser, Matsunaga, De Bayser, Sundaresan, and Takahashi. The motivation to combine Kayser, Matsunaga, De Bayser, Sundaresan, and Takahashi would be to search for and display related images by using tag information as a keyword for searching (Takahashi: [0002] and [0056]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kayser (US Pub 2022/0108447, provisional application 63/087,605 filed on 10/05/20) in view of Matsunaga (US Pub 2018/0061051) in view of De Bayser (US Pub 2017/0339340) in view of Sundaresan (US Pub 2016/0026866) and in further view of Cao (US Pub 2015/0193863).
With respect to claim 9, Kayser in view of Matsunaga in view of De Bayser and in further view of Sundaresan discloses the server according to claim 1, wherein the first processor (Kayser: [0007] and [0023]) is configured to:
acquire, as the feature value, a feature value of a concept…and acquire feature values of concepts…(Kayser in [0052]-[0054] discloses receive image having characteristics or features captured in a particular setting, determine setting values on a received image, if less than a threshold then providing notification to alter camera setting, match camera setting for capturing a current image with camera settings used to capture previous images of a wound of feature, access settings of previously captured image and cause a setting of the camera from which the image is received to match the setting to capture a new image, minimize variability of images captured with different cameras in different environments at different times; here Kayser, Matsunaga, De Bayser, and Sundaresan do not explicitly disclose feature value of a concept of a first layer which is widest, and feature values of concepts of a second layer to an n-th layer sequentially narrower from the concept of the first layer in which n is an integer of 2 or more, but the Cao reference discloses the features, as discussed below), and
search for a predetermined number of the first images from among the first images stored in the memory by using at least one of the feature values of the concepts… (Matsunaga in [0029] and [0057] discloses device including a search engine for searching at least a candidate image similar to a query image by comparing first image feature vectors with the second image feature vectors; here Kayser, Matsunaga, De Bayser, and Sundaresan do not explicitly disclose searching feature values of concepts of the first layer to the nth layer, but the Cao reference discloses the feature, as discussed below).
Kayser discloses acquiring as a feature value a feature value of a concept, acquiring feature values of concepts, and first images stored in a memory, and Matsunaga discloses searching for a predetermined number of first images from among first images stored in a memory, by using at least one of a feature values of concepts, however, Kayser, Matsunaga, De Bayser, and Sundaresan do not explicitly disclose:
acquire a feature value of a concept of a first layer which is widest, and acquires feature values of concepts of a second layer to an n-th layer sequentially narrower from the concept of the first layer in which n is an integer of 2 or more, and
search for a predetermined number of the first images from among the first images stored in the memory by using at least one of the feature values of the concepts of the first layer to the n-th layer.
The Cao reference discloses acquiring a feature value of a concept of a first layer which is widest, and acquiring feature values of concepts of a second layer to an n-th layer sequentially narrower from the concept of the first layer in which n is an integer of 2 or more, and searches for a predetermined number of the first images from among the first images stored in the memory by using at least one of the feature values of the concepts of the first layer to the n-th layer (Cao in [0051], [0052], and [0055] discloses identifying uninteresting images in a corresponding layer, excluding images that were included in the uninteresting image set, selecting a first layer of images according to the images’ similarity to one or more characteristics of images in an initial set, select a second layer of images that are similar to one or more characteristics of one or more of the first layer of the images, search finds a second layer of images that are similar in one or more of a color, a style, a texture, and/or the like to one or more of the first layer of images, search layer quantity can be set to 2, nth layer product image set does not include images that were included in the (n-1)th layer product image set).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Kayser, Matsunaga, De Bayser, Sundaresan and Cao, to have combined Kayser, Matsunaga, De Bayser, Sundaresan and Cao. The motivation to combine Kayser, Matsunaga, De Bayser, Sundaresan and Cao would be to search for and display interesting images for a user by identifying and excluding images that are uninteresting to the user (Cao: [0002] and [0051]).
Remarks
The relevant prior art of record that are not used in claim rejections but are pertinent to the claims or disclosure are:
Matsumoto (US Pub 2003/0123696), which discloses image object including attribute information such as summary, thumbnail, header, original device, scene type, camera maker, camera model etc..
Ko (US Pub 2008/0033983), which discloses storing information regarding image file, such as camera make and model in header area, and adding description tag of a scene of an image as metadata to the image file.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.M/Examiner, Art Unit 2159 /ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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