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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 102
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.
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.
Claim(s) 2-8, 12, 14-19, and 21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Carceroni, US 2018/0146133.
In regard to claim 2, Carceroni, US 2018/0146133, discloses a computer-implemented method comprising:
receiving input comprising an instruction for an image sensor to capture at least one image of a subject (image capturing command with natural language user input), wherein the instruction comprises a first criterion (person or basketball) and a second criterion (basketball hoop) for the at least one image of the subject (see figure 5 and para 85-99: capturing a plurality of a human shooting a basketball into a hoop);
based at least in part on the instruction, causing the image sensor to capture a plurality of images of the subject (see figure 5 and para 85-99: capturing a plurality of a human shooting a basketball into a hoop);
determining a correspondence between a first captured image of the plurality of captured images and the first criterion (see para 88 and 92-94);
determining a correspondence between a second captured image of the plurality of captured images and the second criterion (see para 89-91 and 99-100); and
providing the first captured image and the second captured image (see para 93-94: storing images determined to have the human and the basketball hoop).
In regard to claim 3, Carceroni, US 2018/0146133, discloses the method of claim 2, wherein the received input is a voice input (see para 85).
In regard to claim 4, Carceroni, US 2018/0146133, discloses the method of claim 2, further comprising:
determining a first instruction vector (current visual token) based at least in part on the first criterion (see para 75-76: determining visual tokens for a plurality of images); and
determining a second instruction vector (future visual token) based at least in part on the second criterion (see para 89: determining visual tokens for a plurality of images);
wherein determining the correspondence between the first captured image and the first criterion comprises comparing the first instruction vector to a first captured image vector for the first captured image (see para 95-96), and
wherein determining the correspondence between the second captured image and the second criterion comprises comparing the second instruction vector to a second captured image vector for the second captured image (see para 99-100).
In regard to claim 5, Carceroni, US 2018/0146133, discloses the method of claim 4, wherein:
determining the first instruction vector comprises:
determining first text that corresponds to the first criterion (see para 85); and
providing the first text to one or more trained machine learning models (visual token model 29) to determine the first instruction vector based at least in part on the first text (see para 52 and 72);
determining the second instruction vector comprises:
determining second text that corresponds to the second criterion (see para 85); and
providing the second text to the one or more trained machine learning models (future visual token model 26) to determine the second instruction vector based at least in part on the second text (see para 49-50; and
for each respective captured image of the plurality of captured images, providing the respective captured image to the one or more trained machine learning model to determine a respective captured image vector for the provided respective captured image (see para 75).
In regard to claim 6, Carceroni, US 2018/0146133, discloses the method of claim 2, further comprising:
based at least in part on the instruction:
causing the image sensor to observe a scene, wherein the scene comprises the subject (see para 85-86); and
analyzing the observed scene, wherein causing the image sensor to capture the plurality of images of the subject is performed based at least in part on (see para 85):
determining, based at least in part on the analyzing, that the observed scene corresponds to at least one of the first criterion or the second criterion (see para 89).
In regard to claim 7, Carceroni, US 2018/0146133, discloses the method of claim 6, further comprising:
based at least in part on the analyzing, identifying contextual information related to the scene (see para 87-88); and
determining a first instruction vector based at least in part on the first criterion and the contextual information (see para 88).
In regard to claim 8, Carceroni, US 2018/0146133, discloses the method of claim 6, further comprising: displaying, at a display of a device comprising the image sensor, the observed scene in a preview mode (see para 85-87).
In regard to claim 12, Carceroni, US 2018/0146133, discloses the method of claim 2, further comprising: storing the plurality of images of the subject in a buffer memory (see figure 2, element 48) associated with the image sensor, and wherein, based at least in part on determining the correspondence between the first captured image and the first criterion, providing the first captured image is performed by transferring the first captured image from the buffer memory to persistent memory (see para 45-46, 74, and 94).
In regard to claim 14, Carceroni, US 2018/0146133, discloses the method of claim 2, wherein: providing the first image and the second image comprises providing a user interface (see figure 2, element 12) to enable navigation through the plurality of captured images; and the method further comprises receiving, at the user interface, selection of the first image and the second image for at least one of storage or transmission (see para 94).
In regard to claim 15, Carceroni, US 2018/0146133, discloses a system comprising:
an device (see figure 2, element 10; para 18-24), wherein the device comprises an image sensor (see figure 2, element 30; para 20-21); and
control circuitry (see figure 2, element 40) configured to:
receive input comprising an instruction for an image sensor to capture at least one image of a subject (image capturing command with natural language user input), wherein the instruction comprises a first criterion (person or basketball) and a second criterion (basketball hoop) for the at least one image of the subject (see figure 5 and para 85-99: capturing a plurality of a human shooting a basketball into a hoop);
based at least in part on the instruction, causing the image sensor to capture a plurality of images of the subject (see figure 5 and para 85-99: capturing a plurality of a human shooting a basketball into a hoop);
determine a correspondence between a first captured image of the plurality of captured images and the first criterion (see para 88 and 92-94);
determine a correspondence between a second captured image of the plurality of captured images and the second criterion (see para 89-91 and 99-100); and
provide the first captured image and the second captured image (see para 93-94: storing images determined to have the human and the basketball hoop).
In regard to claim 16, Carceroni, US 2018/0146133, discloses the system of claim 15, wherein the received input is a voice input (see para 85).
In regard to claim 17, Carceroni, US 2018/0146133, discloses the system of claim 15, wherein the control circuitry is further configured to:
determine a first instruction vector (current visual token) based at least in part on the first criterion (see para 75-76: determining visual tokens for a plurality of images); and
determine a second instruction vector (future visual token) based at least in part on the second criterion (see para 89: determining visual tokens for a plurality of images);
determine the correspondence between the first captured image and the first criterion comprises comparing the first instruction vector to a first captured image vector for the first captured image (see para 95-96), and
wherein determine the correspondence between the second captured image and the second criterion comprises comparing the second instruction vector to a second captured image vector for the second captured image (see para 99-100).
In regard to claim 18, Carceroni, US 2018/0146133, discloses the system of claim 17, wherein the control circuitry is further configured to:
determine the first instruction vector by:
determining first text that corresponds to the first criterion (see para 85); and
providing the first text to one or more trained machine learning models (visual token model 29) to determine the first instruction vector based at least in part on the first text (see para 52 and 72);
determining the second instruction vector comprises:
determining second text that corresponds to the second criterion (see para 85); and
providing the second text to the one or more trained machine learning models (future visual token model 26) to determine the second instruction vector based at least in part on the second text (see para 49-50; and
for each respective captured image of the plurality of captured images, providing the respective captured image to the one or more trained machine learning model to determine a respective captured image vector for the provided respective captured image (see para 75).
In regard to claim 19, Carceroni, US 2018/0146133, discloses the system of claim 15, wherein the control circuitry is further configured to:
based at least in part on the instruction:
causing the image sensor to observe a scene, wherein the scene comprises the subject (see para 85-86); and
analyze the observed scene, wherein causing the image sensor to capture the plurality of images of the subject is performed based at least in part on (see para 85):
determining, based at least in part on the analyzing, that the observed scene corresponds to at least one of the first criterion or the second criterion (see para 89).
In regard to claim 21, Carceroni, US 2018/0146133, discloses the system of claim 15, wherein the control circuitry is further configured to:
store the plurality of images of the subject in a buffer memory (see figure 2, element 48) associated with the image sensor, and wherein, based at least in part on determining the correspondence between the first captured image and the first criterion, provide the first captured image is performed by transferring the first captured image from the buffer memory to persistent memory (see para 45-46, 74, and 94).
Allowable Subject Matter
Claims 9-11, 13, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2023/0206417, discloses an imaging device that detects objects based on criterion.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEVELL V SELBY whose telephone number is (571)272-7369. The examiner can normally be reached Monday-Thursday 6 AM - 3:30 PM; Friday 6-10 AM.
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, Lin Ye can be reached at 571-272-7372. 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.
/GEVELL V SELBY/Primary Examiner, Art Unit 2638
gvs
Prosecution Insights