DETAILED ACTION
Claims 7 and 17 have been cancelled.
Claims 21 and 22 have been added.
Claims 1-6, 8-16 and 18-22 are currently pending.
The previous rejections to claims 1-20 under 35 U.S.C. 101 are withdrawn due to Applicant’s amendment.
Response to Arguments
Applicant's arguments filed 7/14/26 have been fully considered but they are not persuasive.
The Applicant argues on page 18 of the response in essence that: Second, Watanabe does not teach or suggest at least "wherein the detecting the one or more emotions includes: generating, by a neural network model, a plurality of spatial features corresponding to the at least one dilated pet outline, capturing, by a transformer-based model, a temporal relationship between the generated plurality of spatial features, and translating, by the neural network model, the generated plurality of spatial features and the captured temporal relationship into at least one of the one or more emotions," as now recited in amended independent claim 1.
Watanabe discloses wherein the detecting the one or more emotions includes: generating, by a neural network model (paragraph 60, The behavior information generation unit 11 and the emotion determination unit 12 of the present embodiment may include, for example, a deep neural network or a convolutional neural network), a plurality of spatial features corresponding to the at least one pet outline (paragraph 35-36, The behavior information generation unit preferably generates behavior information by extracting a feature value from an image of an animal and determining the behavior of the animal based on the feature value), capturing, by a transformer-based model, a temporal relationship between the generated plurality of spatial features (paragraph 43, When a plurality of types of behaviors are included in an image, it is preferable for the behavior information generation unit to generate behavior information for each of the behaviors. For example, if a dog wags its tail for 30 seconds and then raises one paw for 30 seconds in a one-minute video, “wagging tail” and “raising one paw” are generated as behavior information), and translating, by the neural network model, the generated plurality of spatial features and the captured temporal relationship into at least one of the one or more emotions (paragraph 71, the emotion of the animal is determined from the behavior information (step S25)). Newly cited reference Chung discloses dilating the at least one outline (page 5, Dilation processing is performed on the broken and discontinuous pixels in the object image, in other words, the broken and discontinuous pixels in the object image are filled with the surrounding pixels, so that the contours of the object image are connected to form an expansion image).
Claim Rejections - 35 USC § 103
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.
Claims 1-6, 11, 13-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. US Publication 2025/0111698 and Chung et al. Taiwan Publication 1777689B (hereafter “Chung”).
Referring to claims 1, 13 and 19, Watanabe discloses a computer-implemented method for detecting one or more emotions of one or more pets, the method comprising:
receiving, by one or more processors, image data from at least one user device, wherein the image data includes one or more frames (paragraph 71, A user inputs basic information such as the type of a target animal to the acquisition unit and uploads an image of the animal (step S21));
detecting, by the one or more processors, at least one pet outline that includes at least one pet in the one or more frames (paragraph 35, The behavior information generation unit preferably generates behavior information by extracting a feature value from an image of an animal and determining the behavior of the animal based on the feature value);
detecting, by the one or more processors, one or more emotions of the at least one pet based on the at least one pet outline, wherein the detecting the one or more emotions includes:
generating, by a neural network model (paragraph 60, The behavior information generation unit 11 and the emotion determination unit 12 of the present embodiment may include, for example, a deep neural network or a convolutional neural network), a plurality of spatial features corresponding to the at least one pet outline (paragraph 35-36, The behavior information generation unit preferably generates behavior information by extracting a feature value from an image of an animal and determining the behavior of the animal based on the feature value),
capturing, by a transformer-based model, a temporal relationship between the generated plurality of spatial features (paragraph 43, When a plurality of types of behaviors are included in an image, it is preferable for the behavior information generation unit to generate behavior information for each of the behaviors. For example, if a dog wags its tail for 30 seconds and then raises one paw for 30 seconds in a one-minute video, “wagging tail” and “raising one paw” are generated as behavior information), and
translating, by the neural network model, the generated plurality of spatial features and the captured temporal relationship into at least one of the one or more emotions (paragraph 71, the emotion of the animal is determined from the behavior information (step S25)); and
displaying, by the one or more processors, the one or more emotions on at least one user interface of the at least one user device (paragraph 71, The output unit outputs and presents the derived determination result to the user by displaying it on a screen or by other means (step S26)).
While Watanabe discloses detecting a pet outline, Watanabe does not disclose expressly dilating the pet outline.
Chung discloses dilating, by the one or more processors, the at least one outline by expanding the at least one outline by one or more pixels of the one or more frames (page 5, Dilation processing is performed on the broken and discontinuous pixels in the object image, in other words, the broken and discontinuous pixels in the object image are filled with the surrounding pixels, so that the contours of the object image are connected to form an expansion image).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to dilate the outline. The motivation for doing so would have been to improve image recognition by removing broken and discontinuous pixels. Therefore, it would have been obvious to combine Chung with Watanabe to obtain the invention as specified in claims 1, 13 and 19.
Referring to claims 2 and 14, Watanabe discloses classifying, by the one or more processors, the at least one pet included in the at least one pet outline as corresponding to at least one pet breed (paragraph 71, the breed determination unit determines the breed from the image of the animal (step S23)); and
associating, by the one or more processors, the at least one pet breed with at least one breed cluster, wherein each of the at least one breed cluster includes a plurality of emotion detectors (paragraph 71, Then, a database corresponding to the breed is selected (step S24), and by referring to the selected database, the emotion of the animal is determined from the behavior information (step S25)).
Referring to claims 3 and 15, Watanabe discloses the classifying further comprising:
receiving, by a trained machine-learning model, the at least one pet outline (paragraph 71, A user inputs basic information such as the type of a target animal to the acquisition unit and uploads an image of the animal (step S21));
analyzing, by the trained machine-learning model, the at least one pet outline to determine at least one physical feature of the at least one pet (paragraph 45, breeds may be classified by height, length, genetic relationship, and/or the like) (paragraph 48, positions and shapes of key facial parts (for example, eyes, nose, and mouth) distinguish different breeds); and
determining, by the trained machine-learning model, based on the at least one physical feature, that the at least one pet corresponds to the at least one pet breed (paragraph 71, the breed determination unit determines the breed from the image of the animal (step S23)).
Referring to claims 4 and 16, Watanabe discloses the method further comprising:
analyzing, by the one or more processors, the at least one pet outline in each of the one or more frames (paragraph 87, The breed of the cat was American Shorthair, and the shooting time was 13.4 seconds); and
determining, by the one or more processors, that the at least one pet breed occurs a maximum amount of times in the one or more frames (paragraph 68, The determination of the breed may be determination of a specific breed name [the breed that is selected must be the breed that occurs the most times]).
Referring to claim 5, Watanabe discloses wherein the neural network model includes a convolutional neural network (CNN) model (paragraph 60, The behavior information generation unit 11 and the emotion determination unit 12 of the present embodiment may include, for example, a deep neural network or a convolutional neural network).
Referring to claim 6, Watanabe discloses wherein the image data includes video data (paragraph 34, The acquisition unit acquires an image of an animal. Unless otherwise specified, an “image” includes both a still image and a video).
Referring to claim 11, Watanabe discloses storing, by the one or more processors, the at least one pet and the one or more emotions in one or more data stores (paragraph 71, The output unit outputs and presents the derived determination result to the user by displaying it on a screen or by other means (step S26) [the displayed data must be stored]).
Claims 8, 18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. US Publication 2025/0111698 and Chung et al. Taiwan Publication 1777689B as applied to claims 1, 13 and 19 above, and further in view of Sinnott et al. “Run or Pat: Using Deep Learning to Classify the Species Type and Emotion of Pets,” 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Brisbane, Australia, 2021 (hereafter “Sinnott”).
Referring to claims 8, 18 and 21, Watanabe discloses normalizing the at least one pet outline (paragraph 34, The image may be normalized), but does not disclose expressly masking a background around the at least one pet outline.
Sinnott discloses identifying, by the one or more processors, at least one mask corresponding to a background around the at least one pet outline of the one or more frames;
updating, by the one or more processors, the one or more frames, the updating including utilizing the at least one mask to neutralize the background from the one or more frames; and
creating, by the one or more processors, new image data based on the one or more updated frames (page 3, As an example, Figure 1 shows an image of a Chiahuahua in the original image and the annotated bounding box associated with that image. The images are clipped according to the bounding box information).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to mask the background of an image. The motivation for doing so would have been to remove parts of the image that would decrease the accuracy of the model. Therefore, it would have been obvious to combine Sinnott with Watanabe to obtain the invention as specified in claims 8, 18 and 21.
Claims 9, 10, 12, 20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. US Publication 2025/0111698 and Chung et al. Taiwan Publication 1777689B as applied to claims 1 and 19 above, and further in view of Gibbs et al. US Publication 2021/0089945 (hereafter “Gibbs”).
Referring to claims 9 and 22, Watanabe discloses detecting one or more emotions, but does not disclose expressly creating a customized plan based on the one or more emotions.
Gibbs discloses creating, by the one or more processors, a customized plan for the at least one pet based on the one or more emotions; and
displaying, by the one or more processors, the customized plan on the at least one user interface of the user device (paragraph 108, As can be readily seen, the example just described is merely one illustration of the process of analyzing pet behavior data and correlating that data with known symptoms to establish a level of urgency, and messaging to the guardian that instructs on the data analytics and recommended course of action, and recommended products or services that should be purchased for the dog).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to create a customized plan based on a pet’s emotion. The motivation for doing so would have been to improve the pet’s health by offering relevant products to the owner and increase pet care sales. Therefore, it would have been obvious to combine Gibbs with Watanabe to obtain the invention as specified in claims 9 and 22.
Referring to claim 10, Watanabe discloses detecting one or more emotions, but does not disclose expressly determining a recommendation based on the one or more emotions.
Gibbs discloses determining, by the one or more processors, at least one recommendation based on the one or more emotions, wherein the at least one recommendation includes at least one physical activity (paragraph 101, the recommendation machine further provides for a deep learning correlation between the dog profile data and one or more expert systems, the expert systems including but not limited to at least local weather conditions, veterinary symptomology and/or pet care services including specifically recommended medical treatment and/or therapies intended to ameliorate the learned underlying symptomology); and
displaying, by the one or more processors, the at least one recommendation on the at least one user interface of the user device (paragraph 108, As can be readily seen, the example just described is merely one illustration of the process of analyzing pet behavior data and correlating that data with known symptoms to establish a level of urgency, and messaging to the guardian that instructs on the data analytics and recommended course of action, and recommended products or services that should be purchased for the dog).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to determine a recommendation based on a pet’s emotion. The motivation for doing so would have been to improve the pet’s health by offering relevant products and services to the owner and increase pet care sales. Therefore, it would have been obvious to combine Gibbs with Watanabe to obtain the invention as specified in claim 10.
Referring to claims 12 and 20, Watanabe discloses detecting one or more emotions, but does not disclose expressly comparing the one or more emotions to at least one previous emotion.
Gibbs discloses comparing, by the one or more processors, the one or more emotions to at least one previous emotion of the at least one pet (paragraph 132, The novel recommendation machine preferably incorporates “past behavior”, but importantly includes current dog behavior to alert guardians of a dog's immediate wants and needs based on the pet's current data, and predict future needs and wants based on continually analyzed dog behavior data, trend data, anomalous data, and comparative data from similar dogs in a large cohort of dogs); and
displaying, by the one or more processors, information corresponding to the comparing on the at least one user interface of the user device (paragraph 145, the recommendation machine not only provides for identifying the most preferred pet food for each pet, but may also further analyze the behavioral data to recommend the daily portions of that food responsive to real-time or near real-time daily and long-term changes in the pet's behavior).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to compare a pet’s emotion to a previous emotion. The motivation for doing so would have been to better predict a pet’s current state based on past and present data. Therefore, it would have been obvious to combine Gibbs with Watanabe to obtain the invention as specified in claims 12 and 20.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER K HUNTSINGER whose telephone number is (571)272-7435. The examiner can normally be reached Monday - Friday 8:30 - 5:00.
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, Benny Q Tieu can be reached at 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER K HUNTSINGER/Primary Examiner, Art Unit 2682
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