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 .
Application Status
This office action is responsive to the amendments filed on 06/17/2026.
This action has been made FINAL.
Response to Arguments
Applicant's arguments filed 06/17/2026 have been fully considered but they are not persuasive.
The Applicant alleges the following on page 7 of the remarks: “Applicant concedes that Richardson discloses a transcript of an emergency call. However, the OA has not provided a reasonable basis for a combination of Bakunov and Richardson. Bakunov is directed to taking a textual description of a scene and converting it to an image. See Bakunov Fig. 7, 702 "Forest at different times of year." See also "AI Image generation service" Bakunov P[0002], P[0023], P[0094-0096]. Although a transcript of an emergency call is text, it is not necessarily a description of a scene. For example, an emergency call transcript may state, "I need an ambulance at 123 Main street." This is not a description of a scene, but rather may be used to identify which responders to dispatch. As such, there would be no motivation for a person skilled in the art to combine the references as proposed, aside from hindsight bias using applicant's own disclosure as a roadmap for the combination.” The examiner is not persuaded. In this case, we find such a modification of an old process (i.e. transcripts from an emergency call) using a new source (i.e. artificial intelligence) to be obvious. In KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417 (2007), the Supreme Court held that “if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill.” Id. at 417. “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.” Id. at 416; see also id. at 417 (“If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.”); In re Schreiber, 128 F.3d 1473, 1477 (Fed. Cir. 1997) (“It is well settled that the recitation of a new intended use for an old product does not make a claim to that old product patentable.” (citations omitted)). We do not find that the evidence shows providing transcripts from an emergency call using artificial intelligence “uniquely challenging or difficult for one of ordinary skill in the art.” Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1162 (Fed. Cir. 2007) (citing KSR, 550 U.S. at 418). Accordingly, we do not consider Applicant’s argument to sufficiently demonstrate the Examiner’s rejection is in error.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a description of a scene) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The examiner asserts the combination of Bakunov and Richardson discloses the Applicant’s claim language. More specifically, Bakunov’s teachings of “AI Image generation service” in Paragraphs 0002; 0023; 0094-0096 discloses the Applicant’s claim language of “using an artificial intelligence processing tool.” Moreover, the combination of Bakunov and Richardson discloses “the initial summary of the incident based in part on a transcript of an emergency call associated with the incident” in Paragraphs 0069; 0072 of Richardson. Additionally, both of the references teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, artificial intelligence.
Because "applicants may amend claims to narrow their scope, a broad construction during prosecution creates no unfairness to the applicant or patentee." In re ICON Health and Fitness, Inc., 496 F.3d 1374, 1379 (Fed. Cir. 2007) (citing In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004)). Accordingly, the examiner maintains the rejection.
The Applicant alleges the following on page 8 of the remarks: “In other words, the OA has alleged that the "AI Image generation service" provided in Bakunov discloses both text to image generation as well as image to text generation. Bakunov has been reviewed, and it cannot be determined where image to text generation is taught or suggested.” The examiner is not persuaded. The examiner asserts the combination of Bakunov and Richardson discloses the Applicant’s claim language. More specifically, Bakunov discloses image to text generation tool in Paragraphs 0002; 0023; 0094-0096. More specifically, Bakunov specifically recites “image-to-text model” in paragraphs 0094; 0114. MPEP § 2106 states Office personnel are to give claims their broadest reasonable interpretation in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed Cir. 1997). Accordingly, the examiner maintains the rejection.
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.
Claim(s) 1, 3-9, 11-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bakunov, US20240296535 in view of Richardson, US 20200367040.
Claim 1:
Bakunov discloses a computer-implemented method (See Bakunov Abstract) but fails to disclose “the initial summary of the incident based in part on a transcript of an emergency call associated with the incident.” This feature is disclosed in paragraphs 0069; 0072 of Richardson. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have further modify Bakunov by the teachings of Richardson to enable improved analyzation of an emergency call by using natural language processing, more effectively (See Richardson Summary of Invention). Additionally, both of the references teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, artificial intelligence. This close relation between both of the references highly suggests an expectation of success.
As modified:
The combination of Bakunov and Richardson discloses the following:
generating, by a processor, an initial summary (“text” See Bakunov Paragraphs 0020; 0023; 0062-0064; 0096) of an incident (“event” See Bakunov Paragraph 0052) using an artificial intelligence processing tool (“AI Image generation service” See Bakunov Paragraphs 0002; 0023; 0094-0096) the initial summary of the incident based in part on a transcript of an emergency call associated with the incident (See Richardson Paragraphs 0069; 0072);
generating, by a processor, at least two images based on the initial summary (“automated image generators may be text-to-image machine learning models” See Bakunov Paragraphs 0020; 0023; 0062-0064; 0096) using an artificial intelligence image generation tool (“AI Image generation service” See Bakunov Paragraphs 0002; 0023; 0094-0096);
generating, by a processor, for each of the at least two images, a subsequent summary for each image (“Automated image generators utilizing text-to-image technology, e.g., generators built on diffusion models or Generative Adversarial Networks (GANs), may be able to generate high-fidelity images in response to a user's prompts” See Bakunov Paragraphs 0020) using an artificial intelligence image to text generation tool (“AI Image generation service” See Bakunov Paragraphs 0002; 0023; 0094-0096);
comparing, by a processor, the initial summary to each of the subsequent summaries (“At block 620, the first image (the image selected from the first set of images generated by the first automated image generator) and the second image (the image selected from the second set of images generated by the second automated image generator) are automatically compared by the image quality evaluation system 236. Comparisons and rankings (as described below) may be automatically carried out, e.g., by one or more machine learning models or by other computing components” See Bakunov Figures 6A, Item 620; Figure 6B, Item 640; Paragraphs 0135; 0148) using a similarity determination (“The second machine learning model may apply one or more machine learning-based tasks and one or more other tasks, such as automatic rules-based calculations (e.g., a cosine similarity method), to generate the output.” See Bakunov Paragraph 0126) artificial intelligence (“AI Image generation service” See Bakunov Paragraphs 0002; 0023; 0094-0096);
and selecting, by a processor, (“a first image from the first set of images and a second image from the second set of images may be automatically selected” See Bakunov Paragraphs 0020-0025), based on the comparison (“At block 620, the first image (the image selected from the first set of images generated by the first automated image generator) and the second image (the image selected from the second set of images generated by the second automated image generator) are automatically compared by the image quality evaluation system 236. Comparisons and rankings (as described below) may be automatically carried out, e.g., by one or more machine learning models or by other computing components” See Bakunov Figures 6A, Item 620; Figure 6B, Item 640; Paragraphs 0135; 0148), the generated image associated with the subsequent summary that is most similar to the initial summary (See Bakunov Paragraphs 0020-0025) as representative of the incident (“event” See Bakunov Paragraph 0052);
and associating, by a processor, (See Bakunov Figures 6A, Item 620; Figure 6B, Item 640; Paragraphs 0135; 0148) the selected image with a database record (“The database 304 also stores augmentation data, such as overlays or filters, in an augmentation table 312. The augmentation data is associated with and applied to videos (for which data is stored in a video table 314) and images (for which data is stored in an image table 316).” See Bakunov Paragraph 0073) corresponding to the incident (“event” See Bakunov Paragraph 0052).
Claim 3:
Bakunov and Richardson discloses generating the at least two images based in part on audio input associated with the incident (See Bakunov Paragraphs 0045; 0052).
Claim 4:
Bakunov and Richardson discloses generating the at least two images based in part on visual input associated with the incident (See Bakunov Paragraphs 0099).
Claim 5:
Bakunov and Richardson discloses generating the at least two images based in part on metadata associated with the incident (See Bakunov Paragraphs 0082; 0096; 0113).
Claim 6:
Bakunov and Richardson further comprising:
generating a second initial summary (“text” See Bakunov Paragraphs 0020; 0023; 0062-0064; 0096) of a second incident (“event” See Bakunov Paragraph 0052) using the artificial intelligence processing tool (“AI Image generation service” See Bakunov Paragraphs 0002; 0023; 0094-0096);
generating at least two images based on the second initial summary (“automated image generators may be text-to-image machine learning models” See Bakunov Paragraphs 0020; 0023; 0062-0064; 0096) using the artificial intelligence image generation tool (“AI Image generation service” See Bakunov Paragraphs 0002; 0023; 0094-0096);
generating, for each of the at least two images (“automated image generators may be text-to-image machine learning models” See Bakunov Paragraphs 0020; 0023; 0062-0064; 0096) based on the second incident (“event” See Bakunov Paragraph 0052), a second subsequent summary for each image using the artificial intelligence image to text generation tool (“AI Image generation service” See Bakunov Paragraphs 0002; 0023; 0094-0096);
comparing the second initial summary to each of the second subsequent summaries (See Bakunov Figures 6A, Item 620; Figure 6B, Item 640; Paragraphs 0135; 0148);
selecting the generated image (“automated image generators may be text-to-image machine learning models” See Bakunov Paragraphs 0020; 0023; 0062-0064; 0096) associated with the second subsequent summary that is most similar to the second initial summary as representative of the second incident (“event” See Bakunov Paragraph 0052);
comparing the selected generated image representative (See Bakunov Figures 6A, Item 620; Figure 6B, Item 640; Paragraphs 0135; 0148) of the incident with the selected generated image representative of the second incident (“event” See Bakunov Paragraph 0052);
and determining the incident and the second (“event” See Bakunov Paragraph 0052) incident are related based on the comparing (See Bakunov Figures 6A, Item 620; Figure 6B, Item 640; Paragraphs 0135; 0148).
Claim 7:
Bakunov and Richardson discloses wherein the initial summary is based on correspondence of two descriptions of the incident (See Bakunov Paragraphs 0052; 0096).
Claim 8:
Bakunov and Richardson discloses wherein the correspondence is similarities between the two descriptions of the incident (See Bakunov Paragraphs 0052; 0096).
Claims 9 and 11-14:
Claims 9 and 11-14 are rejected on the same basis as claims 1 and 3-6.
Claims 15 and 17-20:
Claims 15 and 17-20 are rejected on the same basis as claims 1 and 3-6.
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publication No.: 20230081171 includes receiving, by a computing device, a particular textual description of a scene. The method also includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair. The method further includes predicting the output image rendition of the scene.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEREE N BROWN whose telephone number is (571)272-4229. The examiner can normally be reached M-F 5:30-2:00 PM EST.
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, SAID BROOME can be reached at (571) 272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHEREE N BROWN/Primary Examiner, Art Unit 2612 August 10, 2026
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