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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claim limitation “a user interface module ; an investigation module” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a linking word “ configured to” coupled with functional language respectively recited after each of the aforementioned claim limitations, without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 1-4; 6-8;11-12 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: see figure 1 and corresponding text. If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f).
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rogers et al (US 2021/0067563) in view of Korenwaitz et al (US 2025/0103645)
As to claim 1, Rogers et al teaches the computer system for detecting first generation illicit material on a target device, the system comprising : a processor (202, figure 2) configured to execute:
a user interface module (206, figure 2) configured to generate a user interface for interaction with a user (The client devices 120 are configured to interact with the cloud storage backend 140 using a cloud storage application or equivalent web interface, via which the client devices 120 may upload an image file or video file for storage or processing by the cloud storage backend 140., paragraph [0016]);
and an investigation module ( forensic server 146) configured to:
generate an investigation interface including input fields for prioritized folder data (The extracted feature vector is provided to the SVM classifier(s) 224 in order to determine whether the input image relates to one of a plurality of classes relating to an illicit, illegal, or malicious activity. Such classes may, for example, include child exploitation, illegal drug trafficking, and firearms trafficking. However, the SVM classifier(s) 224 may be configured to detect images corresponding to any number of additional classes relating to an illicit, illegal, or malicious activity, paragraph [0032]), wherein the prioritized folder data represents an ordered list of prioritized folders to scan for image files (the values that define the decision surface or hyperplane are derived using to an optimization process that maximizes the margin around the decision surface or hyperplane, paragraph [0033-0036]);
search the prioritized folders to locate image files (a compelling legal order is received, the forensic servers 146 may execute a search of the digital forensic evidence stored on the forensic storage devices 148 according to the parameters of the compelling legal order, paragraph [0018]); filter the image files using a plurality of filters each having filter criteria and reject image files which do not meet the filter criteria ( The at least one parameter of the search to be performed may, for example, specify a particular user account, a particular time period, a particular IP address, or the like, which can be used to filter or narrow down the digital forensic evidence that is relevant to the search. After the search is performed, the processor 212 operates the network communications module 218 to transmit a message including some or all of the identified digital forensic evidence relating to at least one parameter of the search, including the associated images or videos, paragraph [0048]) ;
While Rogers teaches the limitation above, Rogers fails to teach “scan image files which were not rejected by the plurality of filters using an Al model, wherein the Al model is trained to identify illicit material; and flag possible first-generation illicit material by the Al model and display the flagged possible first-generation illicit material on the investigation interface.”
However, Korenwaitz et al teaches the scanning 102 may identify material based on content and/or other heuristic means. For example, this may find illicit material that is not on a database on known materials (e.g., a hash table). For example, rather than finding illicit material based on a data base, data on pedophilia may be used to train a machine learning system and/or contents filtering system to recognize material. Using machine learning, if there is a “first-generation” material, it may be recognized even if it is not on a database of known materials ( paragraph [0115]). Korenwaitz et al teaches Presence of this data may be a sign of first-generation materials. When multiple files with similar metadata are found and/or files with metadata similar to illicit content are found, the suspect may be reported as likely a source and/or possessor of first-generation materials. In some embodiments, when illicit material is found with metadata, a further search may be made for other material (even if it is not in itself illicit) having similar metadata that may be additional evidence of the suspect's connection to the illicit material. ( paragraph [0127]). It would have been obvious to one skilled in the art before filing of the claimed invention to use metadata to identify the illicit material in order to help law enforcement agencies to target the source of the problem, rather than just the collectors. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
scan image files which were not rejected by the plurality of filters using an Al model, wherein the Al model is trained to identify illicit material;
and flag possible first-generation illicit material by the Al model and display the flagged possible first-generation illicit material on the investigation interface.
As to claim 2, Korenwaitz et al teaches the system of claim 1, wherein the plurality of filters include at least one of:
an exchangeable image file (EXIF) filter wherein image files which match EXIF data associated with the target device are automatically scanned by the Al model, image files which have EXIF data which does not match the EXIF data associated with the target device are rejected, and image files which do not have EXIF data are not rejected; a size filter which filters the image files based on size threshold filter criteria, wherein image files with a size at or under the size threshold are rejected, and image files with a size over the size threshold are not rejected (metadata such as Exchangeable image file format (EXIF) data and file size, may be used to further classify first-generation content, paragraph [0094]); a color filter which filters the image files based on color depth filter criteria, wherein image files with a color depth at or under the color depth threshold are rejected, and image file with a color depth above the color depth threshold are not rejected; and a known illicit material filter which filters image files based on known illicit images, wherein image files which match known illicit material are rejected, and image files which do not match known illicit material are not rejected he data may be filtered 206 based on content and/or legal issues and/or classified as to its first-generation status. Optionally, the system may track individual actors over different media files. This can be done by associating different characters and their physical characteristics (such as, health, age, weight, height, musculature, skin tone, facial features, identifying markings (e.g., birth marks, tattoos, scarring), etc.) over multiple media objects. The system may track changes in these actors (e.g., physical condition, health, age, weight, height, musculature, skin tone, facial features, identifying markings (e.g., birth marks, tattoos, scarring), etc.) and correlate it with the date of the content and/or actions in the content (e.g., after a scene with violence did the actor appear injured in later scenes and/or apparently unrelated content). This information can be useful for identifying potential victims, suspects, and/or patterns of behavior. The system may use additional tests to rate the likelihood of media containing illegal content or being first-generation materials, paragraph [0132]).
As to claim 3, Korenwaitz et al teaches the system of claim 2, wherein the plurality of filters include the EXIF filter, the size filter, the color filter, and the known illicit material filter, wherein only images which do not have EXIF data are filtered by the size filter, wherein only images which do not have EXIF data and are over the size threshold are filtered by the color filter, wherein only which are filtered by the known illicit material filter, and wherein only images which do not have EXIF data, are over the size filter, are over the color filter or do not have color, and do not match known illicit material are scanned by the Al model(another factor may include whether the image features—EXIF—position. Thus, in some embodiments, if the image includes an EXIF position—it may get a point. For example, (e.g., for reasons of privacy) many systems that upload a pictures to the Internet delete the EXIF location. Thus, in some embodiments, if the image includes EXIF location information—it gets a point. In some embodiments, another factor may include whether the image features—EXIF—device type. Thus, in some embodiments, if the image includes an EXIF device type—it may get a point. For example, many systems that upload a pictures to the Internet delete the EXIF device type. Thus, in some embodiments, if the image includes EXIF device type—it gets a point (paragraph [0150-0151]).
As to claim 4, Korenwaitz et al teaches the system of claim 1, wherein the Al model is a child sexual abuse material (CSAM) model configured to receive a digital image as input and classify the digital image as CSAM or not CSA (In some embodiments, a system in accordance with the current invention detects and/or identifies illicit content (e.g., pedophilic material) using content sensitive automated and/or computer executed methodologies (e.g., Artificial Intelligence (AI), detection feature algorithms, General Classification Features (GCF) algorithms, boosted classifier algorithms, etc.). Optionally, such methods may identify and/or filter content (e.g., images and/or text) that has been altered or manipulated. Optionally, a content-based system may detect images that may have been modified in an attempt to evade detection by other methods, paragraph [0091]; the current system scans data (e.g., impounded media and/or intercepted data) for illicit material (e.g., child pornography and violence). In some embodiments, the current system filters the scanned material based on content and/or classifies the material based on legal issues and/or metadata properties, such as file size, camera data, identifier data, date data, location data, time data, and/or content, paragraph [0095]).
As to claim 5, Korenwaitz et al teaches the system of claim 1, wherein any folders or file location which were not included in the prioritized folders are scanned after the image files in the prioritized folders have been filtered and scanned (materials on a storage medium may be classified as illicit first generation media 442 (e.g., first generation materials that are identified by a content filter as having objectionable material e.g., pornography, violence, pedophilia), other first generation media 444 (e.g., first generation images and/or videos that on their own do not arouse objections of the content filter for objectionable content), other illicit media 446 (e.g., materials that don't appear to be first generation but are identified by a content filter as having objectionable material e.g., pornography, violence, pedophilia), other media 448 (media that does not appear to be first generation and is not identified by a content filter as having objectionable material e.g., pornography, violence, pedophilia), other illicit materials 450 (media that does not appear to be first generation but is identified by a content filter as having objectionable material e.g., pornography, violence, pedophilia) and other materials 452 (various materials on the storage media e.g., downloaded documents, local documents, personal communications etc.; paragraph [0140]).
As to claim 6, Rogers et al teaches the system of claim 1, wherein the Al model is a deep learning neural network trained on known illicit material, the deep learning neural network comprising an input layer for receiving an input image, one or more hidden layers, and an output layer configured to assign a class label to the input image, wherein the class label identifies the input image as illicit material or not illicit material (It will be appreciated by those of ordinary skill in the art that a convolutional neural networks (CNNs) are a type of feed-forward neural network that contains a number of convolution layers or convolution operations. A convolution layer receives an input, and applies one or more convolutional filters to the input. A convolutional filter, also referred to as a kernel, is a matrix of weights, also referred to as parameters or filter values, which is applied to various chunks of an input matrix in a defined manner such that the matrix of weights is convolved over the input matrix to provide an output matrix. Values for the matrix of weights are learned in a training process prior to operation of the CNN. The dimensions of the output matrix is determined by the kernel size of the filter (i.e., the size of the matrix of weights) and by the “stride” of the filter, which indicates how much the chunks of the input matrix overlap with one another during convolution or are spaced apart from one another during convolution. The various layers and filters of a CNN are used to detect various “features” of the input., paragraph [0026]; note that CNN is a type of deep neural network specifically designed to process data with a grid-like topology, such as images or time-series signals. It falls under the deep learning category because it consists of multiple layers that learn hierarchical feature representations from raw input data ) .
As to claim 7, Korenwaitz et al teaches the system of claim 1, wherein the investigation module is further configured to scan image files which were not rejected by the plurality of filters using a skin tone detection model (This can be done by associating different characters and their physical characteristics (such as, health, age, weight, height, musculature, skin tone, facial features, identifying markings (e.g., birth marks, tattoos, scarring), etc.) over multiple media objects. The system may track changes in these actors (e.g., physical condition, health, age, weight, height, musculature, skin tone, facial features, identifying markings (e.g., birth marks, tattoos, scarring), etc.) and correlate it with the date of the content and/or actions in the content (e.g., after a scene with violence did the actor appear injured in later scenes and/or apparently unrelated content). This information can be useful for identifying potential victims, suspects, and/or patterns of behavior. The system may use additional tests to rate the likelihood of media containing illegal content or being first-generation materials, paragraph [0132-0133]).
As to claim 8, Korenwaitz et al teaches the system of claim 7, wherein the skin tone detection model flags image files as possible first generation illicit material based on a skin tone pixel threshold, wherein image files which contain skin tone pixels at or above the skin tone pixel threshold are flagged as possible first generation illicit material ( a factor in identifying first-generation material may include compression artifacts. Images that have been downloaded from the internet may have compression artifacts that are unlikely to be present in images captured by a camera. These artifacts can include pixelation, blurriness, distortion, filters, and/or various editing features (e.g., brightness, color adjustment, cropping, rotating, sharpening, etc.) or a combination thereof, paragraph [0130-0132]).
As to claim 9, Korenwaitz et al teaches the system of claim 8, wherein when an image file is flagged as possible first generation material by the Al model, image files in the same location as the flagged image file are scanned before other image files in the queue ( a method of identifying illegal content in accordance with an embodiment of the current invention. For example, method 200 may be used for identifying and/or filtering child pornography and/or violent pornography. Method 200 includes testing 202 for first-generation content before scanning 204, filtering 206, and reporting 208 the results there, paragraph [0123-0124]).
The limitation of claims 10-20 has been addressed above.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANCY BITAR whose telephone number is (571)270-1041. The examiner can normally be reached Mon-Friday from 8:00 am to 5:00 p.m..
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
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