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 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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7-14 and 16-22 are rejected under 35 U.S.C. 103 as being unpatentable over King et al (2011/0043652) in views of Miller et al (2020/0162236) and Stojancic et al (2012/0207402).
For claim 1, King teaches that A method (abstract) comprising: receiving, from a first sensor of a first information handling system, content to be associated with a fingerprint (King teaches of capturing or scanning is the process of systematic examination to obtain information from a rendered document, capturing information and presenting content associated with the captured information or other supplemental information (speech patterns, fingerprints, etc.) as King teaches in par.19, 43 and 95); determining, by the first information handling system, the fingerprint of the content (King teaches that system uploads the text to an index of content associated with the newspaper, and identifies and retrieves an electronic counterpart for the article. The capture device then displays the electronic counterpart via an associated touch screen along with one or more actions to perform as King teaches in par.54 and 55); receiving, by the first information handling system, a system context of the first information handling system (King teaches that n order from capture device 216. As an example, an order can include an identifier (such as a serial number of the capture device 216 or an identifier that partially or uniquely identifies the user of the capture device), capture context information (e.g., time of capture, location of capture, etc.) and/or captured information (such as a text string) that is used to uniquely identify the source from which data is being captured as King teaches in par.64); and sending, by the first information handling system to a second information handling system (King teaches that system may use resources external to a rendered document to recognize text within the rendered document, such as knowledge pertaining to the approximate number of glyphs within a word, dictionaries (e.g., word frequency dictionaries), grammar and punctuation rules, probabilities of finding particular word-grams and character-grams within a corpus, regular expressions for matching various strings, such as email addresses, URL, and so on. Furthermore, the system may use resources such as DNS servers, address books, and phone books to verify recognized text, such as URLS, emails addresses, and telephone numbers as King teaches in par.280).
King fails to teach that semantic cryptographic fingerprint, according to a fingerprinting algorithm, wherein determining the semantic cryptographic fingerprint according to the fingerprinting algorithm comprises: applying one or more convolutional filter kernels to the content; measuring, for each convolutional filter kernel of the one or more convolutional filter kernels, activation strengths and corresponding regions of activation; and determining the semantic cryptographic fingerprint based on one or more of the activation strengths and the corresponding regions of activation,
determining, by the first information handling system, a hash of the semantic cryptographic fingerprint and the system context; and the hash with an indicator associating the content from which the hash was determined from.
Miller teaches that semantic cryptographic fingerprint and wherein determining the semantic cryptographic fingerprint (Miller teaches the fingerprinting function may be a cryptographic fingerprinting function which may employ pixel semantic data as Miller teaches in par.27 and 78), determining, by the first information handling system, a hash of the semantic cryptographic fingerprint and the system context (Miller teaches that he fingerprint of the document may be generated via a tamper resistant image hashing function or algorithm, such as but not limited to a perceptual hashing function, or “p-hash” of the document's contents. A tamper resistant image hashing function, such as a perceptual hash, may include a hashing algorithm or hash function that is relatively insensitive to certain types of edits or updates to particular features of a document, while being significantly sensitive to other types of edits or alterations to the features of the document. For example, in embodiments where the document is a digital image, a p-hash value of at least a portion of the image (e.g., a portion that includes a visualization of a subject, such as a model) may be generated as Miller teaches in par.29 and 72); and the hash with an indicator associating the content from which the hash was determined from (Miller teaches that each time a document is accessed, modified, and/or saved, among other things that could change a document or metadata associated therewith, a fingerprint indicating the state of the document (e.g., a cryptographic hash of at least a portion of the document's contents) and/or an edit history of the document can be generated and stored within a block (or record) written to a distributed ledger, such as a blockchain as Miller teaches in par.3, 22, 30 and 39). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include semantic cryptographic fingerprint as taught and suggested by Miller for the purpose of providing assurances that any particular copy and/or version of the document has either maintained its original state, or has been modified within acceptable boundaries, among other things (Miller, par.4).
Stojancic teaches, similar system, according to the fingerprinting algorithm comprises: applying one or more convolutional filter kernels to the content (Stojancic teaches that Video sequence processing is described with various filtering rules applied to extract dominant features for content based video sequence identification for both video fingerprinting and identification, and video content based management using two dimensional anisotropic Gaussian kernels as Stojancic teaches in abstract and par.65); measuring, for each convolutional filter kernel of the one or more convolutional filter kernels (Stojancic teaches that measure is derived for each of the preliminary boundary regions, based on a gradient vector intensity computed for each pixel and a normalized sum of the gradient vector intensities computed for each region. The activity measure for each identified boundary region is compared against an activity threshold and based on this comparison it is decided whether to go into the third step of active region determination, or accept the preliminary active region boundaries determined in the first step Stojancic teaches that in par.7 and 43), activation strengths and corresponding regions of activation (Stojancic teaches that applying an array of anisotropic filters for image scale-space analysis. An array of elliptic-shaped, sampled anisotropic second order partial derivative Gaussian filters with (s.sub.x, s.sub.y) scale parameters is formed. An array of rectangular-shaped, sampled anisotropic second order partial derivative Gaussian filters with finite spatial support with (s.sub.x, s.sub.y) scale parameters is formed to approximate the elliptic-shaped sampled anisotropic second order partial derivative Gaussian filters. A scale space for a selected frame is analyzed by the array of rectangular sampled anisotropic second order partial derivative Gaussian filters of finite spatial support corresponding to different (s.sub.x, s.sub.y) scales Stojancic teaches that in par.8); and determining the fingerprint based on one or more of the activation strengths and the corresponding regions of activation (Stojancic teaches each vector contains a set (s.sub.x, s.sub.y, x, y, peak polarity). A rectangular box is drawn encompassing Ns.sub.x.times.Ms.sub.y pixels and centered at an interest point (x, y) location associated with a set (s.sub.x, s.sub.y, x, y, peak polarity). A multi-dimensional descriptor and a multi-dimensional signature are determined based on pixels in the rectangular box Stojancic teaches that in par.7-8 and 65). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include filter kernels as taught and suggested by Stojancic for the purpose of providing the effective area of images that are refined by complex filters that provide accurate region characterizations resistant to image distortion for identification of the video frames in the video sequence (Stojancic, par.6).
For claims 2, and 11, King, as modified by Miller and Stojancic, further teaches wherein the first sensor may comprise a camera, a microphone, or another measuring device (King teaches in par.43).
For claims 3, and 12, King, as modified by Miller and Stojancic, fails to teach wherein the content is signed with a private key of the first sensor.
Millter further teaches that wherein the content is signed with a private key of the first sensor (Miller teaches transaction may be signed via a private key of the computing device as Miller teaches in par.110). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include a private key of the first sensor as taught and suggested by Miller for the purpose of providing assurances that any particular copy and/or version of the document has either maintained its original state, or has been modified within acceptable boundaries, among other things (Miller, par.4).
For claims 4, and 13, King, as modified by Miller and Stojancic, fails to teach wherein the semantic cryptographic fingerprint is nonreversible.
Millter further teaches that wherein the semantic cryptographic fingerprint is nonreversible (Miller teaches cryptographic fingerprint is , a cryptographic hash and/or hash value) and cryptographic fingerprint hashes (such as SHA-256 or SHA-3) are designed to be one-way functions and are not reversible as Miller teaches in par.22). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include cryptographic fingerprint is nonreversible as taught and suggested by Miller for the purpose of providing assurances that any particular copy and/or version of the document has either maintained its original state, or has been modified within acceptable boundaries, among other things (Miller, par.4).
For claims 5, 14 and 20, King, as modified by Miller and Stojancic, further teaches wherein the first information handling system create the fingerprint before storing the fingerprint with the content in a user-accessible region of memory (King, par.55).
King fails to teach that semantic cryptographic fingerprint.
Miller teaches that semantic cryptographic fingerprint (Miller teaches the fingerprinting function may be a cryptographic fingerprinting function which may employ pixel semantic data as Miller teaches in par.27 and 78). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include semantic cryptographic fingerprint as taught and suggested by Miller for the purpose of providing assurances that any particular copy and/or version of the document has either maintained its original state, or has been modified within acceptable boundaries, among other things (Miller, par.4).
For claims 7, and 16, King, as modified by Miller and Stojancic, further teaches wherein the system context comprises a sensor measurement date, timing, a geolocation coordinate, metadata (King, par.33 and 93). However, King fails to teaches private key of the first sensor.
Miller further teaches that system context comprises a sensor measurement date, timing, a geolocation coordinate, metadata (Miller par.42 and 72). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include private key as taught and suggested by Miller for the purpose of providing assurances that any particular copy and/or version of the document has either maintained its original state, or has been modified within acceptable boundaries, among other things (Miller, par.4).
For claims 8, and 17, King, as modified by Miller and Stojancic, fails to teach wherein the hash, associated with a private key, is uploaded to a public database with an associated public key.
Miller further teaches that wherein the hash, associated with a private key, is uploaded to a public database with an associated public key (Miller, par.41). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include private key as taught and suggested by Miller for the purpose of providing assurances that any particular copy and/or version of the document has either maintained its original state, or has been modified within acceptable boundaries, among other things (Miller, par.4).
For claims 9, and 18, King, as modified by Miller and Stojancic, fails to teach wherein the first or a third information handling system, sends an instruction to an application present on the first or third information handling system that limits access to the content to a set of users based on the hash of the content.
Miller further teaches that wherein the first or a third information handling system, sends an instruction to an application present on the first or third information handling system that limits access to the content to a set of users based on the hash of the content (Miller, par.41). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include the hash of the content as taught and suggested by Miller for the purpose of providing assurances that any particular copy and/or version of the document has either maintained its original state, or has been modified within acceptable boundaries, among other things (Miller, par.4).
For claim 10, King teaches that An information handling system (abstract) comprising: a processor; a memory; wherein the processor is configured to perform (King, par.77) steps comprising: receiving, from a first sensor of a first information handling system, content to be associated with a fingerprint (King teaches of capturing or scanning is the process of systematic examination to obtain information from a rendered document, capturing information and presenting content associated with the captured information or other supplemental information (speech patterns, fingerprints, etc.) as King teaches in par.19, 43 and 95); determining, by the first information handling system, the fingerprint of the content (King teaches that system uploads the text to an index of content associated with the newspaper, and identifies and retrieves an electronic counterpart for the article. The capture device then displays the electronic counterpart via an associated touch screen along with one or more actions to perform as King teaches in par.54 and 55); receiving, by the first information handling system, a system context of the first information handling system (King teaches that n order from capture device 216. As an example, an order can include an identifier (such as a serial number of the capture device 216 or an identifier that partially or uniquely identifies the user of the capture device), capture context information (e.g., time of capture, location of capture, etc.) and/or captured information (such as a text string) that is used to uniquely identify the source from which data is being captured as King teaches in par.64); and sending, by the first information handling system to a second information handling system (King teaches that system may use resources external to a rendered document to recognize text within the rendered document, such as knowledge pertaining to the approximate number of glyphs within a word, dictionaries (e.g., word frequency dictionaries), grammar and punctuation rules, probabilities of finding particular word-grams and character-grams within a corpus, regular expressions for matching various strings, such as email addresses, URL, and so on. Furthermore, the system may use resources such as DNS servers, address books, and phone books to verify recognized text, such as URLS, emails addresses, and telephone numbers as King teaches in par.280).
King fails to teach that semantic cryptographic fingerprint, wherein determining the semantic cryptographic fingerprint according to the fingerprinting algorithm comprises: applying one or more convolutional filter kernels to the content; measuring, for each convolutional filter kernel of the one or more convolutional filter kernels, activation strengths and corresponding regions of activation; and determining the semantic cryptographic fingerprint based on one or more of the activation strengths and the corresponding regions of activation, determining, by the first information handling system, a hash of the semantic cryptographic fingerprint and the system context; and the hash with an indicator associating the content from which the hash was determined from.
Miller teaches that semantic cryptographic fingerprint, wherein determining the semantic cryptographic fingerprint according to the fingerprinting algorithm (Miller teaches the fingerprinting function may be a cryptographic fingerprinting function which may employ pixel semantic data as Miller teaches in par.27 and 78), determining, by the first information handling system, a hash of the semantic cryptographic fingerprint and the system context (Miller teaches that he fingerprint of the document may be generated via a tamper resistant image hashing function or algorithm, such as but not limited to a perceptual hashing function, or “p-hash” of the document's contents. A tamper resistant image hashing function, such as a perceptual hash, may include a hashing algorithm or hash function that is relatively insensitive to certain types of edits or updates to particular features of a document, while being significantly sensitive to other types of edits or alterations to the features of the document. For example, in embodiments where the document is a digital image, a p-hash value of at least a portion of the image (e.g., a portion that includes a visualization of a subject, such as a model) may be generated as Miller teaches in par.29 and 72); and the hash with an indicator associating the content from which the hash was determined from (Miller teaches that each time a document is accessed, modified, and/or saved, among other things that could change a document or metadata associated therewith, a fingerprint indicating the state of the document (e.g., a cryptographic hash of at least a portion of the document's contents) and/or an edit history of the document can be generated and stored within a block (or record) written to a distributed ledger, such as a blockchain as Miller teaches in par.3, 22, 30 and 39). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include semantic cryptographic fingerprint as taught and suggested by Miller for the purpose of providing assurances that any particular copy and/or version of the document has either maintained its original state, or has been modified within acceptable boundaries, among other things (Miller, par.4).
Stojancic teaches, similar system, according to the fingerprinting algorithm comprises: applying one or more convolutional filter kernels to the content (Stojancic teaches that Video sequence processing is described with various filtering rules applied to extract dominant features for content based video sequence identification for both video fingerprinting and identification, and video content based management using two dimensional anisotropic Gaussian kernels as Stojancic teaches in abstract and par.65); measuring, for each convolutional filter kernel of the one or more convolutional filter kernels (Stojancic teaches that measure is derived for each of the preliminary boundary regions, based on a gradient vector intensity computed for each pixel and a normalized sum of the gradient vector intensities computed for each region. The activity measure for each identified boundary region is compared against an activity threshold and based on this comparison it is decided whether to go into the third step of active region determination, or accept the preliminary active region boundaries determined in the first step Stojancic teaches that in par.7 and 43), activation strengths and corresponding regions of activation (Stojancic teaches that applying an array of anisotropic filters for image scale-space analysis. An array of elliptic-shaped, sampled anisotropic second order partial derivative Gaussian filters with (s.sub.x, s.sub.y) scale parameters is formed. An array of rectangular-shaped, sampled anisotropic second order partial derivative Gaussian filters with finite spatial support with (s.sub.x, s.sub.y) scale parameters is formed to approximate the elliptic-shaped sampled anisotropic second order partial derivative Gaussian filters. A scale space for a selected frame is analyzed by the array of rectangular sampled anisotropic second order partial derivative Gaussian filters of finite spatial support corresponding to different (s.sub.x, s.sub.y) scales Stojancic teaches that in par.8); and determining the fingerprint based on one or more of the activation strengths and the corresponding regions of activation (Stojancic teaches each vector contains a set (s.sub.x, s.sub.y, x, y, peak polarity). A rectangular box is drawn encompassing Ns.sub.x.times.Ms.sub.y pixels and centered at an interest point (x, y) location associated with a set (s.sub.x, s.sub.y, x, y, peak polarity). A multi-dimensional descriptor and a multi-dimensional signature are determined based on pixels in the rectangular box Stojancic teaches that in par.7-8 and 65). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include filter kernels as taught and suggested by Stojancic for the purpose of providing the effective area of images that are refined by complex filters that provide accurate region characterizations resistant to image distortion for identification of the video frames in the video sequence (Stojancic, par.6).
For claim 19, King teaches that computer program product (par.62) comprising: a non-transitory computer readable medium comprising instructions for causing an information handling system to perform steps (par.62) comprising: receiving, from a first sensor of a first information handling system, content to be associated with a fingerprint (King teaches of capturing or scanning is the process of systematic examination to obtain information from a rendered document, capturing information and presenting content associated with the captured information or other supplemental information (speech patterns, fingerprints, etc.) as King teaches in par.19, 43 and 95); determining, by the first information handling system, the fingerprint of the content (King teaches that system uploads the text to an index of content associated with the newspaper, and identifies and retrieves an electronic counterpart for the article. The capture device then displays the electronic counterpart via an associated touch screen along with one or more actions to perform as King teaches in par.54 and 55); receiving, by the first information handling system, a system context of the first information handling system (King teaches that n order from capture device 216. As an example, an order can include an identifier (such as a serial number of the capture device 216 or an identifier that partially or uniquely identifies the user of the capture device), capture context information (e.g., time of capture, location of capture, etc.) and/or captured information (such as a text string) that is used to uniquely identify the source from which data is being captured as King teaches in par.64); and sending, by the first information handling system to a second information handling system (King teaches that system may use resources external to a rendered document to recognize text within the rendered document, such as knowledge pertaining to the approximate number of glyphs within a word, dictionaries (e.g., word frequency dictionaries), grammar and punctuation rules, probabilities of finding particular word-grams and character-grams within a corpus, regular expressions for matching various strings, such as email addresses, URL, and so on. Furthermore, the system may use resources such as DNS servers, address books, and phone books to verify recognized text, such as URLS, emails addresses, and telephone numbers as King teaches in par.280).
King fails to teach that semantic cryptographic fingerprint, the semantic cryptographic fingerprint according to the fingerprinting algorithm comprises: applying one or more convolutional filter kernels to the content; measuring, for each convolutional filter kernel of the one or more convolutional filter kernels, activation strengths and corresponding regions of activation; and determining the semantic cryptographic fingerprint based on one or more of the activation strengths and the corresponding regions of activation, determining, by the first information handling system, a hash of the semantic cryptographic fingerprint and the system context; and the hash with an indicator associating the content from which the hash was determined from.
Miller teaches that semantic cryptographic fingerprint, the semantic cryptographic fingerprint according to the fingerprinting algorithm (Miller teaches the fingerprinting function may be a cryptographic fingerprinting function which may employ pixel semantic data as Miller teaches in par.27 and 78), determining, by the first information handling system, a hash of the semantic cryptographic fingerprint and the system context (Miller teaches that he fingerprint of the document may be generated via a tamper resistant image hashing function or algorithm, such as but not limited to a perceptual hashing function, or “p-hash” of the document's contents. A tamper resistant image hashing function, such as a perceptual hash, may include a hashing algorithm or hash function that is relatively insensitive to certain types of edits or updates to particular features of a document, while being significantly sensitive to other types of edits or alterations to the features of the document. For example, in embodiments where the document is a digital image, a p-hash value of at least a portion of the image (e.g., a portion that includes a visualization of a subject, such as a model) may be generated as Miller teaches in par.29 and 72); and the hash with an indicator associating the content from which the hash was determined from (Miller teaches that each time a document is accessed, modified, and/or saved, among other things that could change a document or metadata associated therewith, a fingerprint indicating the state of the document (e.g., a cryptographic hash of at least a portion of the document's contents) and/or an edit history of the document can be generated and stored within a block (or record) written to a distributed ledger, such as a blockchain as Miller teaches in par.3, 22, 30 and 39). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include semantic cryptographic fingerprint as taught and suggested by Miller for the purpose of providing assurances that any particular copy and/or version of the document has either maintained its original state, or has been modified within acceptable boundaries, among other things (Miller, par.4).
Stojancic teaches, similar system, according to the fingerprinting algorithm comprises: applying one or more convolutional filter kernels to the content (Stojancic teaches that Video sequence processing is described with various filtering rules applied to extract dominant features for content based video sequence identification for both video fingerprinting and identification, and video content based management using two dimensional anisotropic Gaussian kernels as Stojancic teaches in abstract and par.65); measuring, for each convolutional filter kernel of the one or more convolutional filter kernels (Stojancic teaches that measure is derived for each of the preliminary boundary regions, based on a gradient vector intensity computed for each pixel and a normalized sum of the gradient vector intensities computed for each region. The activity measure for each identified boundary region is compared against an activity threshold and based on this comparison it is decided whether to go into the third step of active region determination, or accept the preliminary active region boundaries determined in the first step Stojancic teaches that in par.7 and 43), activation strengths and corresponding regions of activation (Stojancic teaches that applying an array of anisotropic filters for image scale-space analysis. An array of elliptic-shaped, sampled anisotropic second order partial derivative Gaussian filters with (s.sub.x, s.sub.y) scale parameters is formed. An array of rectangular-shaped, sampled anisotropic second order partial derivative Gaussian filters with finite spatial support with (s.sub.x, s.sub.y) scale parameters is formed to approximate the elliptic-shaped sampled anisotropic second order partial derivative Gaussian filters. A scale space for a selected frame is analyzed by the array of rectangular sampled anisotropic second order partial derivative Gaussian filters of finite spatial support corresponding to different (s.sub.x, s.sub.y) scales Stojancic teaches that in par.8); and determining the fingerprint based on one or more of the activation strengths and the corresponding regions of activation (Stojancic teaches each vector contains a set (s.sub.x, s.sub.y, x, y, peak polarity). A rectangular box is drawn encompassing Ns.sub.x.times.Ms.sub.y pixels and centered at an interest point (x, y) location associated with a set (s.sub.x, s.sub.y, x, y, peak polarity). A multi-dimensional descriptor and a multi-dimensional signature are determined based on pixels in the rectangular box Stojancic teaches that in par.7-8 and 65). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include filter kernels as taught and suggested by Stojancic for the purpose of providing the effective area of images that are refined by complex filters that provide accurate region characterizations resistant to image distortion for identification of the video frames in the video sequence (Stojancic, par.6).
For claims 21, King, as modified by Miller and Stojancic, fails to teach prior to determining the semantic cryptographic fingerprint of the content: selecting, by the first information handling system, the fingerprinting algorithm from among a plurality of fingerprinting algorithms based on a type of the content received from the first sensor.
Stojancic teaches prior to determining the semantic cryptographic fingerprint of the content: selecting, by the first information handling system, the fingerprinting algorithm from among a plurality of fingerprinting algorithms based on a type of the content received from the first sensor (Stojancic teaches array of elliptic-shaped, sampled anisotropic second order partial derivative Gaussian filters with (s.sub.x, s.sub.y) scale parameters is formed. An array of rectangular-shaped, sampled anisotropic second order partial derivative Gaussian filters with finite spatial support with (s.sub.x, s.sub.y) scale parameters is formed to approximate the elliptic-shaped sampled anisotropic second order partial derivative Gaussian filters. A scale space for a selected frame is analyzed by the array of rectangular sampled anisotropic second order partial derivative Gaussian filters of finite spatial support corresponding to different (s.sub.x, s.sub.y) scales. A region of pixels in a surrounding neighborhood of an initial interest point is convolved with the array of rectangular sampled anisotropic second order partial derivative Gaussian filters to produce a result that is resistant to image distortion as Stojancic teaches in par.8 and 65 and abstract). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include fingerprinting algorithms as taught and suggested by Stojancic for the purpose of providing the effective area of images that are refined by complex filters that provide accurate region characterizations resistant to image distortion for identification of the video frames in the video sequence (Stojancic, par.6).
For claims 22, King, as modified by Miller and Stojancic, fails to teach wherein determining the semantic cryptographic fingerprint according to the fingerprinting algorithm further comprises: randomly sampling the activation strengths and corresponding regions of activation for the one or more convolutional filter kernels to form a subset of activation strengths and corresponding regions of activations, wherein determining the semantic cryptographic fingerprint based on one or more of the activation strengths and the corresponding regions of activation includes determining the semantic cryptographic fingerprint based on the subset of activation strengths and corresponding regions of activation.
Stojancic teaches determining the semantic cryptographic fingerprint according to the fingerprinting algorithm further comprises: randomly sampling the activation strengths and corresponding regions of activation for the one or more convolutional filter kernels to form a subset of activation strengths and corresponding regions of activations, wherein determining the semantic cryptographic fingerprint based on one or more of the activation strengths and the corresponding regions of activation includes determining the semantic cryptographic fingerprint based on the subset of activation strengths and corresponding regions of activation (Stojancic teaches perform a method of applying an array of anisotropic filters for image scale-space analysis. An array of elliptic-shaped, sampled anisotropic second order partial derivative Gaussian filters with (s.sub.x, s.sub.y) scale parameters is formed. An array of rectangular-shaped, sampled anisotropic second order partial derivative Gaussian filters with finite spatial support with (s.sub.x, s.sub.y) scale parameters is formed to approximate the elliptic-shaped sampled anisotropic second order partial derivative Gaussian filters. A scale space for a selected frame is analyzed by the array of rectangular sampled anisotropic second order partial derivative Gaussian filters of finite spatial support corresponding to different (s.sub.x, s.sub.y) scales. A region of pixels in a surrounding neighborhood of an initial interest point is convolved with the array of rectangular sampled anisotropic second order partial derivative Gaussian filters to produce a result that is resistant to image distortion as Stojancic teaches in par.8-9 and 65). It would have been obvious to one ordinary skill in the art before effective filling date to modify King to include fingerprinting algorithms as taught and suggested by Stojancic for the purpose of providing the effective area of images that are refined by complex filters that provide accurate region characterizations resistant to image distortion for identification of the video frames in the video sequence (Stojancic, par.6).
Response to Amendments/Arguments
Applicant’s arguments with respect to claim(s) 1-5, 7-14 and 16-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The applicant’s arguments regarding new amendment limitations in claims 1, 10 and 19, has been considered but is moot, because the examiner applied new art, Stojancic et al (2012/0207402), that covers newly claimed limitation.
Regarding dependent claims arguments, said arguments are moot because the applied references are not considered to have alleged differences, and therefore are considered to properly show that for which they were cited.
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 AYUB A MAYE whose telephone number is (571)270-5037. The examiner can normally be reached Monday-Friday 9AM-5PM.
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/AYUB A MAYE/Examiner, Art Unit 2436
/MOEEN KHAN/Primary Examiner, Art Unit 2436
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