DETAILED ACTION
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 .
Response to Amendment
Applicant’s Amendments filed on July 20, 2026, has been entered and made of record.
Currently pending Claim(s) 1-4 and 6-9
Independent Claim(s) 1 and 8-9
Amended Claim(s) 1, 3, and 6-9
Canceled Claim(s) 5
Response to Arguments
This office action is responsive to Applicant’s Arguments/Remarks Made in an Amendment received on July 20, 2026.
In view of amendments filed on July 20, 2026, to the claims, the Applicant has amended the title in response to the Examiner’s previous objection, and the objection is now overcome. The Applicant has amended claim 6 in response to the previous objection for minor informalities, and the objection is now overcome. The Applicant has amended the independent claims 1 and 8-9 to include the subject matter from claim 5, which is now canceled. Additionally, the Applicant has amended claims 3 and 7 for changes to punctuation.
In view of Applicant Arguments/Remarks filed July 20, 2026, with respect to the claims, the Applicant first argued (Remarks page 7) that the independent claims 1 and 8-9, as amended, overcome the previous rejections under 35 U.S.C. § 101, because the subject matter of claim 5, which was not previously rejected under 35 U.S.C. § 101, has been incorporated into the independent claims. However, upon review of the amended independent claims, the Examiner argues that the claims still do not fall within at least one of the four categories of patent eligible subject matter. See the new 35 U.S.C. § 101 rejection for all claims presented in the body of rejection below.
Next, the Applicant argued (Remarks page 8) that the independent claims 1 and 8-9, as amended, are now allowable over Shihadah (US 2013/0282532 A1). The Examiner finds this argument to be persuasive. In the previous office action (Non-Final Rejection dated April 20, 2026), the Examiner indicated that claim 5 contained allowable subject matter, and this subject matter has now been incorporated into the independent claims. However, upon further search and consideration, the Examiner has found additional prior art—Chand (US 2015/0254711 A1)—which directly anticipates claims 1- 4 and 6-9, so new rejections under 35 U.S.C. § 102(a)(2) are presented in the body of rejection below. Accordingly, this action is made non-final.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 and 6-9 are rejected under 35 U.S.C. § 101, because these claims do not fall within at least one of the four categories of patent eligible subject matter. In the independent claims 1 and 8-9, several steps are recited:
acquiring an image that is a captured image of an image capture subject and includes a product in a part of an area
processing the image and thereby generating subject inference data indicating an inference result of a type of the image capture subject
performing output based on the subject inference data
acquiring a plurality of images generated by a plurality of terminals
generating corresponding subject inference data for each of the plurality of terminals
outputting, as the output, first relation data indicating a relation between attribute information of a user of each of the plurality of terminals and the corresponding subject inference data.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. Regarding the independent claims, claim 1 is directed to an image processing apparatus with at least one memory and processor, so claim 1 falls within the statutory category of machine. Claim 8 is directed to an image processing method performed by a computer, so claim 8 falls within the statutory category of a process. Claim 9 is directed to a non-transitory computer-readable medium and falls within the category of machine. Thus, the independent claims 1 and 8-9 all fall within a statutory category.
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Regarding the independent claims 1 and 8-9, the broadest reasonable interpretation of steps (a)-(f) listed above is that those steps fall within the mental-process grouping of abstract ideas, because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
More specifically, step (a) involves acquiring an image of a product, which is a step of data gathering. Step (b) involves determining the image capture subject (whether the image contains an email, commercial, social media post, etc.). A person presented with an image can infer the image capture subject from observable characteristics in the image without the need for a specific computer system. Step (c) involves “performing output” without providing any additional details about what information is being output, and no specific process for obtaining the output is mentioned. Step (d) involves acquiring a plurality of images, which is a step of data gathering. Step (e) involves generating corresponding subject inference data for each of the plurality of images. A person can easily observe multiple images and infer the image capture subject in each image from observable characteristics without the need for a specific computer system. Step (f) involves outputting relational data indicating a relation between attribute information of users and corresponding subject inference data without providing any steps or algorithms regarding how the relational data is determined.
Thus, these steps recite no specific machine-learning architecture, mathematical image-processing algorithm, or other processing that must be performed by a specific computer system to obtain the subject capture data from images and determine the relational data. Rather, the claims recite only the result of a processing algorithm, which is inferring the image capture subject and outputting relational data. Furthermore, the process of determining relation data recites the abstract mental process of organizing, comparing, and evaluating information to identify a relationship between categories of data, such as user attributes and inferred image capture subjects. This correlation and evaluation of information can be performed by a person with only a generic computer system. Since no specific process or algorithm is provided by the claims for performing correlation and evaluation, there is no technological improvement provided by the claims which would require more than a generic computer system to perform the claimed method.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Regarding the independent claims 1 and 8-9, these claims recite the additional elements of generic computer components. Claim 1 includes an apparatus comprising memory and a processor, claim 8 includes a computer-implemented method, and claim 9 includes a non-transitory storage medium for storing a computer program. The specification likewise describes the computers and/or terminals generically. In [0012] for example, the terminals are described as a smartphone or tablet operated by a person. Thus, the claims amount to no more than instructions to apply the claimed method using a generic computer. See MPEP 2106.05(f).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Overall, generic computer components are used to receive information, evaluate and correlate the information, and output the resulting information.
The dependent claims 2-4 and 6-7 fail to make up for the deficiencies of the independent claims, and these claims are also rejected under 35 U.S.C. § 101. Claim 2 explains that the subject is inferred from viewing the area around a product in the image without providing details about the process. Claim 3 explains that the image may be a video, and the subject may be inferred from each frame of the video. Claim 4 explains the different types of subjects. Claim 6 describes inferring a product from an image, which is a process that could be performed in the human mind. Claim 7 describes recording purchase information from users and outputting relation data between user attribute information and purchases. This claim describes ordinary computer functions of record keeping and data gathering, and similarly to claim 1, claim 7 describes outputting information without requiring any specific process for obtaining the information.
Overall, the Examiner recommends providing more technical details about the apparatus and the image processing method beyond the claimed steps of acquiring an image, inferring the subject (email advertisement, commercial, social media post, etc.) of the image, and simply outputting data. To provide a practical application for the independent claims, technical details about the image processing method and apparatus should be provided so that concrete, technological improvements which require the claimed apparatus are present.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4 and 6-9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chand et al. (US 2015/0254711 A1), hereafter Chand.
Regarding claim 1, Chand teaches an image processing apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to perform operations (Fig. 1 shows the server and user devices for performing the claimed method.), the operations comprising:
acquiring an image that is a captured image of an image capture subject and includes a product in a part of an area; processing the image and thereby generating subject inference data indicating an inference result of a type of the image capture subject; performing output based on the subject inference data (Figs. 3-4 and [0089-0097] show the process of inferring the image capture subject and collecting user demographics. [0089] “The processing unit 201 receives 302 images from a user via the communication unit 241. In one embodiment, the images are from print media such as newspapers, magazines, articles, etc. In another embodiment, the images are from a video (e.g., a situation comedy, a fashion show). The processing unit 201 identifies 304 a source for each image, for example, from a newspaper or from a video. In one embodiment, the processing unit 201 identifies that a source of the image is a video and determines a timestamp associated with the image in the video.”);
acquiring a plurality of images generated by a plurality of terminals (The system has many users creating profiles and pictures are received from the user devices. [0039] “In another embodiment, the recognition application 103 generates user profiles for each user… The recognition application 103 receives captured images from user devices.”);
generating corresponding subject inference data for each of the plurality of terminals (As shown above, [0089] discusses generating subject inference data. The subject and the product inferred from an image are both saved as metadata associated with that image. [0093] “The processing unit 201 identifies 404 metadata associated with the captured image… the processing unit 201 also identifies a source for the captured image.”); and
outputting, as the output, first relation data indicating a relation between attribute information of a user of each of the plurality of terminals and the corresponding subject inference data (Demographic data is associated with each user account and saved on the server. [0094] “The user profile engine 203 generates 406 a user profile for a user of the user device 115 based on user demographics and the metadata. For example, the user profile includes user demographics such as age, sex, residence, language, etc. The user profile also includes metadata associated with images captured by the user.” Chand teaches associating the user demographics with products and the subject inference data of images for targeting advertising purposes. [0039] “The user profile also includes user demographics that can be used in conjunction with the history of captured images to recommend content.” [0041] “The recognition application 103 identifies products and product categories from images and indexes the images based on the products and product categories. In one embodiment, the recognition application 103 determines which channels are popular based on a number of captured images provided from user devices 115 over a period of time. In other embodiments, the recognition application 103 determines which channels are popular based on a number of captured images relating to a particular product, a particular brand, a group of products or product categories provided from user devices 115 over a period of time. In other embodiments, the recognition application 103 determines which channels are popular based on a number of captured images relating to a particular product, a particular brand, a group of products or product categories provided from user devices 115 at a particular time. In other embodiments, the recognition application 103 determines which channels are popular based on actions performed by users, such as a number of purchases resulting from capturing images for all products, a number of purchases resulting from capturing images for a particular product or a number of purchases resulting from capturing images for a product category” Here, Chand determines which media source (newspaper, television, etc.) a user interacts with most in relation to a product type, and that media source is used in future targeted ads.).
Regarding claim 2, Chand teaches the image processing apparatus according to claim 1, wherein the operations further comprise generating the subject inference data by processing an area around the product in the image (In each uploaded image, Chand’s invention identifies both the product in the image and the subject surrounding the product. [0089] “The processing unit 201 receives 302 images from a user via the communication unit 241. In one embodiment, the images are from print media such as newspapers, magazines, articles, etc. In another embodiment, the images are from a video (e.g., a situation comedy, a fashion show). The processing unit 201 identifies 304 a source for each image, for example, from a newspaper or from a video.”).
Regarding claim 3, Chand teaches the image processing apparatus according to claim 1, wherein the operations further comprise: acquiring, as the image, a moving image; and processing a plurality of frame images included in the moving image, and thereby generating the subject inference data ([0049] “The database indexes images. In one embodiment, the images are from a video played on the media player 107. The video includes a number of frames at a frame rate. Each frame represents an image of the video. For example, for a 10-minute conversation show that includes 900 frames/images and a 10-minute short movie that includes 1500 frames/images (e.g., the conversation show has a lower frame rate than the short movie), the database indexes the 900 images and 1500 images, respectively.”).
Regarding claim 4, Chand teaches the image processing apparatus according to claim 1, wherein a type of the image capture subject includes at least one of: a screen displayed on a display, based on broadcast; a screen displayed on a display, based on a social networking service (SNS); a screen displayed on a display, based on an email; a printed matter; and the product arranged in a physical space ([0089] “In one embodiment, the images are from print media such as newspapers, magazines, articles, etc. In another embodiment, the images are from a video (e.g., a situation comedy, a fashion show). The processing unit 201 identifies 304 a source for each image, for example, from a newspaper or from a video. In one embodiment, the processing unit 201 identifies that a source of the image is a video and determines a timestamp associated with the image in the video.”).
Regarding claim 6, Chand teaches the image processing apparatus according to claim 1, wherein the operations further comprise processing the image, and thereby inferring the product and/or a similar product (Fig. 3 and [0089-0091] show the process of recognizing products and the subject in an image. [0090] “The product engine 205 receives images from the processing unit 201 and identifies 306 products in each image.” [0096] “The product engine 205 identifies 410 a product in the indexed image and provides 412 the user with information about the product.”).
Regarding claim 7, Chand teaches the image processing apparatus according to claim 6, wherein the operations further comprise: acquiring purchase result information indicating whether the product and/or the similar product has been purchased ([0090] “The product engine 205 receives images from the processing unit 201 and identifies 306 products in each image.” [0096] “The product engine 205 identifies 410 a product in the indexed image and provides 412 the user with information about the product.”); and
outputting second relation data indicating a relation between the subject inference data and the purchase result information ([0039] “ The user profile is used to associate users with images and to later track the user's reaction to information about products, such as when the user purchases a product that was in a captured image.” [0041] “In other embodiments, the recognition application 103 determines which channels are popular based on actions performed by users, such as a number of purchases resulting from capturing images for all products, a number of purchases resulting from capturing images for a particular product or a number of purchases resulting from capturing images for a product category. The recognition application 103 recommends at least one advertisement based on which channels are popular. For example, if images of a product are captured during certain peak times or purchases are made during peak times, the recognition application 103 recommends broadcasting an advertising campaign for the product at that time period. In some embodiments, the recognition application 103 generates a user profile based on user demographics and metadata associated with the captured image including the product, updates the user profile based on how the user interacts with the product and recommends an advertisement based on the user profile.”).
Regarding claim 8, Chand teaches an image processing method performing: by a computer (Fig. 1 shows the server and user devices for performing the claimed method.),
acquiring an image that is a captured image of an image capture subject and includes a product in a part of an area; processing the image and thereby generating subject inference data that indicate an inference result of a type of the image capture subject; performing output based on the subject inference data (Figs. 3-4 and [0089-0097] show the process of inferring the image capture subject and collecting user demographics. [0089] “The processing unit 201 receives 302 images from a user via the communication unit 241. In one embodiment, the images are from print media such as newspapers, magazines, articles, etc. In another embodiment, the images are from a video (e.g., a situation comedy, a fashion show). The processing unit 201 identifies 304 a source for each image, for example, from a newspaper or from a video. In one embodiment, the processing unit 201 identifies that a source of the image is a video and determines a timestamp associated with the image in the video.”);
acquiring a plurality of images generated by a plurality of terminals (The system has many users creating profiles and pictures are received from the user devices. [0039] “In another embodiment, the recognition application 103 generates user profiles for each user… The recognition application 103 receives captured images from user devices.”);
generating corresponding subject inference data for each of the plurality of terminals (As shown above, [0089] discusses generating subject inference data. The subject and the product inferred from an image are both saved as metadata associated with that image. [0093] “The processing unit 201 identifies 404 metadata associated with the captured image… the processing unit 201 also identifies a source for the captured image.”); and
outputting, as the output, first relation data indicating a relation between attribute information of a user of each of the plurality of terminals and the corresponding subject inference data (Demographic data is associated with each user account and saved on the server. [0094] “The user profile engine 203 generates 406 a user profile for a user of the user device 115 based on user demographics and the metadata. For example, the user profile includes user demographics such as age, sex, residence, language, etc. The user profile also includes metadata associated with images captured by the user.” Chand teaches associating the user demographics with products and the subject inference data of images for targeting advertising purposes. [0039] “The user profile also includes user demographics that can be used in conjunction with the history of captured images to recommend content.” [0041] “The recognition application 103 identifies products and product categories from images and indexes the images based on the products and product categories. In one embodiment, the recognition application 103 determines which channels are popular based on a number of captured images provided from user devices 115 over a period of time. In other embodiments, the recognition application 103 determines which channels are popular based on a number of captured images relating to a particular product, a particular brand, a group of products or product categories provided from user devices 115 over a period of time. In other embodiments, the recognition application 103 determines which channels are popular based on a number of captured images relating to a particular product, a particular brand, a group of products or product categories provided from user devices 115 at a particular time. In other embodiments, the recognition application 103 determines which channels are popular based on actions performed by users, such as a number of purchases resulting from capturing images for all products, a number of purchases resulting from capturing images for a particular product or a number of purchases resulting from capturing images for a product category” Here, Chand determines which media source (newspaper, television, etc.) a user interacts with most in relation to a product type, and that media source is used in future targeted ads.” Here, Chand determines which media source (newspaper, television, etc.) a user interacts with most in relation to a product type, and that media source is used in future targeted ads.).
Regarding claim 9, Chand teaches a non-transitory computer-readable medium storing a program for causing a computer to perform operations (Fig. 1 shows the server and user devices for performing the claimed method. [0048] “The data storage 243 is a non-transitory memory that stores data for providing the functionality described herein.”), the operations comprising:
acquiring an image that is a captured image of an image capture subject and includes a product in a part of an area; processing the image and thereby generating subject inference data that indicate an inference result of a type of the image capture subject; (Figs. 3-4 and [0089-0097] show the process of inferring the image capture subject and collecting user demographics. [0089] “The processing unit 201 receives 302 images from a user via the communication unit 241. In one embodiment, the images are from print media such as newspapers, magazines, articles, etc. In another embodiment, the images are from a video (e.g., a situation comedy, a fashion show). The processing unit 201 identifies 304 a source for each image, for example, from a newspaper or from a video. In one embodiment, the processing unit 201 identifies that a source of the image is a video and determines a timestamp associated with the image in the video.”);
acquiring a plurality of images generated by a plurality of terminals (The system has many users creating profiles and pictures are received from the user devices. [0039] “In another embodiment, the recognition application 103 generates user profiles for each user… The recognition application 103 receives captured images from user devices.”);
generating corresponding subject inference data for each of the plurality of terminals (As shown above, [0089] discusses generating subject inference data. The subject and the product inferred from an image are both saved as metadata associated with that image. [0093] “The processing unit 201 identifies 404 metadata associated with the captured image… the processing unit 201 also identifies a source for the captured image.”); and
outputting, as the output, first relation data indicating a relation between attribute information of a user of each of the plurality of terminals and the corresponding subject inference data (Demographic data is associated with each user account and saved on the server. [0094] “The user profile engine 203 generates 406 a user profile for a user of the user device 115 based on user demographics and the metadata. For example, the user profile includes user demographics such as age, sex, residence, language, etc. The user profile also includes metadata associated with images captured by the user.” Chand teaches associating the user demographics with products and the subject inference data of images for targeting advertising purposes. [0039] “The user profile also includes user demographics that can be used in conjunction with the history of captured images to recommend content.” [0041] “The recognition application 103 identifies products and product categories from images and indexes the images based on the products and product categories. In one embodiment, the recognition application 103 determines which channels are popular based on a number of captured images provided from user devices 115 over a period of time. In other embodiments, the recognition application 103 determines which channels are popular based on a number of captured images relating to a particular product, a particular brand, a group of products or product categories provided from user devices 115 over a period of time. In other embodiments, the recognition application 103 determines which channels are popular based on a number of captured images relating to a particular product, a particular brand, a group of products or product categories provided from user devices 115 at a particular time. In other embodiments, the recognition application 103 determines which channels are popular based on actions performed by users, such as a number of purchases resulting from capturing images for all products, a number of purchases resulting from capturing images for a particular product or a number of purchases resulting from capturing images for a product category” Here, Chand determines which media source (newspaper, television, etc.) a user interacts with most in relation to a product type, and that media source is used in future targeted ads.” Here, Chand determines which media source (newspaper, television, etc.) a user interacts with most in relation to a product type, and that media source is used in future targeted ads.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fordyce et al. (US 2011/0087546 A1) teaches systems and methods for targeted advertising and for recording user information. This includes recording purchase histories, point of interaction data (where a user receives advertisements, i.e. kiosk, email, phone, digital sign), and user demographics. Relational data can be determined using the recorded data.
Kim et al. (Understanding shopping routes of offline purchasers: selection of search-channels (online vs. offline) and search-platforms (mobile vs. PC) based on product types. Serv Bus 13, 305–338.) teaches a study where user shopping preferences were collected through a survey. Specifically, whether the user shopped online or offline was collected, and the search platform (mobile device or PC) was collected along with demographic information of the survey participants.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC JAMES SHOEMAKER whose telephone number is (571)272-6605. The examiner can normally be reached Monday through Friday from 8am to 5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner' s supervisor, JENNIFER MEHMOOD, can be reached at (571)272-2976. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Eric Shoemaker/
Patent Examiner
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
