Detailed Action
This communication is in response to the Arguments and Amendments filed on 6/11/2026.
Claims 1-20 are pending and have been examined.
Claims 1-20 are rejected. Hence, this action has been made Final.
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 .
Response to Amendment
The Applicant has amended the claims to include “wherein marking the entity phrase comprises inserting a first tag before the entity phrase and a second tag after the entity phrase, and wherein the revised prompt includes the first tag, the entity phrase, and the second tag;” “and wherein the replacement phrase is generated based on a context of the revised prompt;” “in place of the entity phrase.” And “by performing autoregressive token generation”
Regarding the 35 U.S.C. § 101 rejections, Applicant notes
Step 1 Applicant respectfully submits that each of claims 1-20 are directed to a method or a
system and are therefore directed to a statutory category of invention, and that the Office Action does not contend otherwise. See Office Action at page 2. Step 2A, Prong One
The Office Action does not explicitly identify the grouping of abstract ideas (e.g.,
mathematical concepts, certain methods of organizing human activity, or mental processes) that claims 1-20 are alleged to fall within. See Office Action at pages 2-5. Based on the language used when addressing the limitations of claims 1, 12, and 16 (e.g., "[t]his relates to a human receiving a text prompt using vision or auditory processes", "this relates to a human identifying and marking the phrase using pen and paper", etc., see Office Action at pages 2-5), it appears that the Office Action contends that claims 1-20 are directed to abstract ideas because they recite mental processes.
Examiner notes the claims are directed to mental processes a human can perform.
Applicant notes claims 1, 12, and 16 do not recite mental processes. Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. MPEP 2106.04(a)(2)(III)(A). See SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims"); CyberSource Corp. v. Retail Decisions, Inc, 654 F.3d 1366, 1376, 99 USPQ2d at 1699 (Fed. Cir. 2011) (distinguishing Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 97 USPQ2d 1274 (Fed. Cir. 2010), and SiRF Tech., Inc. v. Int'l Trade Comm'n, 601 F.3d 1319, 94 USPQ2d 1607 (Fed. Cir. 2010), as directed to inventions that "could not, as a practical matter, be performed entirely in a human's mind"). The Federal Circuit has applied this principle to hold that claims reciting computer-implemented data analysis that operates at a scale or through means beyond what a human can practically perform are not directed to mental processes. See 930 F.3d at 1304 (claims not directed to an abstract idea where "the human mind is not equipped" to perform the recited computer-implemented steps).
Examiner notes the statutory exception analysis asks whether the claim is “directed to” an abstract idea. Courts and the USPTO have recognized that claims which at a high level recite the performance of cognitive or mental tasks (e.g., categorizing, comparing, organizing information, calculating statistical measures, aligning sequences) may be placed in the mental process grouping even if implemented by machines (see examples in the August 2025 Memo and cases like SAP, Electric Power Group).
Applicant notes Claim 1 does not recite a process that could be performed mentally. Obtaining training data and training a language generation model cannot practically be performed in the human mind or with pen and paper, and especially when operating at the scale that is required for determining information that is used for training a neural network.
Examiner notes the claim recites high level, cognitive data processing, mental-like steps. The limitations of “receiving, marking, generating” can all be performed in the human mind.
Applicant notes paragraphs [0157]-[0159] and [0161]-[0162] of the Specification provide:
can be used to make predictions on new, unseen data (i.e., during inference) Applicant further notes that obtaining training data at the scale that would be required for training the language generation model, and then training the language generation model, could not be practically performed in the human mind or using a paper and pencil.
Examiner notes the present claim language recites limitations that are high level and functional. If applicant includes cited portions of the specification in the claim language the analysis may change.
Applicant notes Under Step 2A, Prong Two, the Specification sets forth an improvement to a technology in detail. Specifically, the paragraphs [0021]-[0023] of the Specification describe specific techniques and details that are improvements over conventional machine learning technology:
[0021] A conventional large language model employs an autoregressive token generation
technique. For example, when a large language model predicts a next token to be
generated, the large language model attends to (i.e., uses as context) past tokens that have
either been passed in as an instruction or have been previously generated by the large
language model. Therefore, for a scenario in which a phrase is to be replaced in an input sentence, conventional large language models are only able to attend to words preceding
the phrase, and not to words following the phrase, and therefore cannot generate a
replacement for the phrase based on a context of the sentence as a whole.
[0022] According to some aspects, the language generation model generates the
replacement phrase by performing autoregressive token generation based on a sequence
of tokens from a revised prompt. ... For example, by using a first tag and a second tag
surrounding an entity phrase as proxies for a marked entity phrase in an input sequence, the language generation model is able to look backwards (attend to previous tokens) but
also consume a full context of the input sequence before generating the replacement
phrase.
[0023] Accordingly, the language generation model is able to generate replacement
phrases for an entity phrase that use an entire sentence as context ... Therefore, aspects of
the present disclosure provide a media processing system that improves on conventional language generation technology by using a language generation model that is trained to generate a replacement phrase based on a revised prompt, which increases a contextual accuracy of the replacement phrase. By contrast, conventional large language models cannot generate replacement phrases for entity phrases using words that follow the entity phrases as context.
In other words, the Specification sets forth an improvement to the accuracy of machine learning technology over conventional language generation models. Whereas a conventional model that performs autoregressive token generation attends only to tokens preceding a phrase to be replaced and therefore cannot generate a replacement based on the context of the sentence as a whole, the claimed first tag and second tag surrounding the entity phrase act as proxies that enable the autoregressive language generation model to look backward and also to consume the full context of the revised prompt, including tokens following the second tag, before generating the replacement phrase, thereby increasing the contextual accuracy of the replacement phrase.
Examiner notes Applicant argues the claim integrates the exception into a practical application by solving the generation of replacement phrases using context information. The specification provides useful problem/solution narrative, but the eligibility inquiry is primarily directed to the claim language as a whole and whether the claim recites particular technical means that show an improvement in computer functionality or other technology (cf. USPTO Memo, examples).
On the present claim wording, the limitations are largely functional and outcome oriented (receiving, marking, generating”) without concrete computational detail or a recitation of how the arrangements materially improve the functioning of the computer system itself (e.g., speed/latency reductions, memory or computational efficiency, novel data representations that reduce error by a measurable metric, or specific unconventional network architectures constrained in a way that produces the improvement).
Applicant notes A next step in the Prong Two analysis may include evaluating the claim to ensure that the claim reflects the disclosed improvement. "Second, if the specification sets for an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification...". 2019 PEG Update at 12. Moreover, "the specificity of the claim limitation is relevant to the evaluation of several considerations including the use of a particular machine, particular transformation and whether the limitations are mere instructions apply an exception." Id. at 11.
Furthermore, "the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application." Id. at 12. See Charles Kim, Deputy Commissioner for Patents, "Advance notice of change to the MPEP in light of Ex Parte Desjardins", Memo to the Patent Examining Corps (Dec. 5, 2025) at 2 (the "December Memo"), citing e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), "in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of 'catastrophic forgetting,' and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation."Page 14 of 27 Finally, per the December Memo, the second paragraph of MPEP § 2106.05(a),
subsection I, is revised to add new examples xiii and xiv to the list of examples that may show an improvement in computer functionality:
xiii. An improved way of training a machine learning model that protected the model's
knowledge about previous tasks while allowing it to effectively learn new tasks; Ex Parte
Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review
Panel Decision) (precedential); and
xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or
workstreams; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26,
2025, Appeals Review Panel Decision) (precedential).
December Memo at 4.
Applicant notes It is respectfully submitted that amended claim 1 recites at least the additional elements of "the revised prompt includes the first tag, the entity phrase, and the second tag" and "a language generation model", "autoregressive token generation based on a sequence of tokens from the revised prompt", "the replacement phrase comprises a variant of the entity phrase" and "the replacement phrase is generated based on a context of the revised prompt" which, when evaluated together with the other elements of claim 1, reflect the disclosed improvement to machine learning technology.
Examiner notes the 2025 Memo instructs patent examiners to consult the specification to determine whether the claimed invention reflects an improvement to technology. The specification does state the improvement). But per MPEP/Case law, showing an improvement may require either (a) claim language that itself conveys the particular technical means producing the improvement, or (b) evidence that the recited combination is unconventional and produces a technical effect (a factual inquiry). On the present record the claim does not persuasively present either.
Furthermore, it is respectfully submitted that claim 16 recites at least the additional elements of "an entity marking model comprising entity marking parameters stored in the at least one memory, the entity marking model trained to mark the entity phrase within a text prompt to obtain a revised prompt, wherein marking the entity phrase comprises inserting a first tag before the entity phrase and a second tag after the entity phrase, and wherein the revised prompt includes the first tag, the entity phrase, and the second tag" and "a language generation model comprising text generation parameters stored in the at least one memory, the language generation model trained to generate a replacement phrase based on the revised prompt, wherein the replacement phrase comprises a variant of the entity phrase and wherein the replacement phrase is generated based on a context of the revised prompt" which, when evaluated together with the other elements of claim 16, reflect the disclosed improvement to machine learning technology Page 15 of 27 and reflects an improvement to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams.
Examiner notes the processor and memory are noted as additional elements. The additional elements do not amount to significantly more than the judicial exception. Processor and memory are noted as a generic computer components.
Finally, it is respectfully submitted that claim 12 recites at least the additional elements of "a training set including a training text prompt and a training replacement phrase, wherein the training text prompt includes a training entity phrase surrounded by a first tag and a second tag, and the training replacement phrase comprises a ground-truth variant of the training entity phrase" and "training, using the training set, a language generation model to generate a replacement phrase by performing autoregressive token generation based on a text prompt, wherein the replacement phrase comprises a variant of an entity phrase in the text prompt", which, when evaluated together with the other elements of claim 12, reflect the disclosed improvement to machine learning technology and reflect an improvement to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams.
Examiner notes the claims recite limitations a human can perform using pen and paper.
Applicant notes Step 2B Page 5 of the Office Action contends that claims 1, 12, and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
If a claim has been determined to be directed to a judicial exception under Step 2A,
examiners should then evaluate the additional elements individually and in combination under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). 2019 PEG at 56. If the examiner determines that the element (or combination of elements) amounts to significantly more than the exception itself, the claim is eligible, thereby concluding the eligibility analysis. Id. In determining patent eligibility, examiners should consider whether the claim "purport(s) to improve the functioning of the computer itself' or "any other technology or technical field." MPEP 2106.05(a) (citing 574 U.S. at 225). In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. MPEP 2106.05(a)(I) (citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016)).
When evaluating a claim under the Step 2B analysis, the full scope of the claim under the broadest reasonable interpretation should be considered to determine if the claim reflects an improvement in technology (e.g., the improvement described in the specification). MPEP 2106.05(a). In making this determination, it is critical that examiners look at the claim "as a whole," in other words, the claim should be evaluated "as an ordered combination, without ignoring the requirements of the individual steps." Id. When performing this evaluation, examiners should be "careful to avoid oversimplifying the claims" by looking at them generally and failing to account for the specific requirements of the claims. Id., citing McRO, 837 F.3d at 1313, 120 USPQ2d at 1100.
It is respectfully submitted that claim 1 recites at least the additional elements of "the revised prompt includes the first tag, the entity phrase, and the second tag" and "a language generation model", "autoregressive token generation based on a sequence of tokens from the revised prompt", "the replacement phrase comprises a variant of the entity phrase" and "the replacement phrase is generated based on a context of the revised prompt" which, when taken together with the other elements of claim 1, enable claim 1 to recite an ordered combination of elements including marking the entity phrase within the text prompt to obtain a revised prompt, wherein marking the entity phrase comprises inserting a first tag before the entity phrase and a second tag after the entity phrase, and wherein the revised prompt includes the first tag, the entity phrase, and the second tag and generating, using a language generation model, a replacement phrase by performing autoregressive token generation based on a sequence of tokens from the revised prompt, wherein the replacement phrase comprises a variant of the entity phrase and wherein the replacement phrase is generated based on a context of the revised prompt that is not well-understood, routine, or conventional, and reflects an improvement to machine learning technology set forth in at least paragraphs [0021]-[0023] and [0060]-[0063] of the Specification. Claim 1 therefore provides an inventive concept.
Examiner notes the claim limitations recite an abstract idea. The additional elements must supply an “inventive concept.” The claim recites known functional components (language generation model) and high level data transformations. Without claim specificity tying those components to particular unconventional architectures, constrained parameterizations, training/regimen steps, or demonstrable improvements, the recited elements appear to be routine, conventional uses of language models and generic software components, and therefore fail to supply an inventive concept.
The applicants Arguments and Amendments do not overcome the rejections 35 U.S.C. § 101 Rejections.
Applicant’s arguments and amendments, with respect to the rejection(s) of claims 1, 12 and 16 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of BADJATIYA (US Patent Number US 20250335775 A1).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Independent Claim 1, Claim 1 recites,
“1. A method for media processing, comprising:
receiving a text prompt including an entity phrase; [This relates to a human receiving a text prompt using vision or auditory processes.]
marking the entity phrase within the text prompt to obtain a revised prompt; [This relates to a human identifying and marking the phrase using pen and paper.]
wherein marking the entity phrase comprises inserting a first tag before the entity phrase and a second tag after the entity phrase, and wherein the revised prompt includes the first tag, the entity phrase, and the second tag; [this relates to a human marking a phrase with a tag using pen and paper.]
generating, using a language generation model, a replacement phrase by performing autoregressive token generation based on a sequence of tokens from the revised prompt wherein the replacement phrase comprises a variant of the entity phrase;
[This relates to a human generating a replacement phrase in the human mind or using pen and paper.]
and generating an augmented prompt that includes the replacement phrase in place of the entity phrase. [This relates to a human generating an augmented prompt in the human mind or using pen and paper.]
The Dependent Claim does not include additional limitations that could incorporate the abstract idea into a practical application or cause the Claim as a whole to amount to significantly more than the underlying abstract idea.
Regarding Independent Claim 12, claim 12 recites,
“A method of training a machine learning model, the method comprising:
obtaining a training set including a training text prompt and a training replacement phrase, [This relates to a human obtaining a training set using visual and auditory processes.] wherein the training text prompt includes a training entity phrase surrounded by a first tag and a second tag, and the training replacement phrase comprises a ground-truth variant of the training entity phrase; [This relates to a human performing training using visual and auditory processes.]
and training, using the training set, a language generation model to generate a replacement phrase by performing autoregressive token generation based on a text prompt, wherein the replacement phrase comprises a variant of an entity phrase in the text prompt. [This relates to a human training using a training set using pen and paper or auditory processes.]
Regarding independent claim 16 claim 16 recites,
“A system for media processing, comprising: at least one memory; at least one processor executing instructions stored in the at least one memory; an entity marking model comprising entity marking parameters stored in the at least one memory,
the entity marking model trained to mark the entity phrase within a text prompt to obtain a revised prompt, wherein marking the entity phrase comprises inserting a first tag before the entity phrase and a second tag after the entity phrase, and wherein the revised prompt includes the first tag, the entity phrase, and the second tag; [This relates to a human marking a phrase using pen and paper.]
a language generation model comprising text generation parameters stored in the at least one memory, the language generation model trained to generate a replacement phrase based on the revised prompt, wherein the replacement phrase comprises a variant of the entity phrase and wherein the replacement phrase is generated based on a context of the revised prompt. [This relates to a human generating a replacement phrase using pen and paper.]
This judicial exception is not integrated into a practical application. In particular, claim 16 recites additional elements of “memory” and “processor” For example, in [0153] of the as filed specification, there is description of using memory is used to store computer-readable, computer-executable software including instructions that, when executed, cause at least one processor of processor unit 1305 to perform various functions described herein. And in [0152] In some cases, processor unit 1305 is configured to operate a memory array using a memory controller. In other cases, a memory controller is integrated into processor unit 1305. In some cases, processor unit 1305 is configured to execute computer-readable instructions stored in memory unit 1310 Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a processor and memory is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitation in the claims noted above are directed towards insignificant solution activity. The claims are not patent eligible.
Dependent claim 2 recites,
“2. The method of claim 1, further comprising:
identifying, using a natural language processing model, the entity phrase from the text prompt by identifying a part of speech tag. [This relates to a human identifying an entity phrase in the human mind.] No additional limitations present.
Dependent claim 3 recites,
“3. The method of claim 1, further comprising:
generating a plurality of replacement phrases including the replacement phrase; and receiving a user input selecting the replacement phrase from among the plurality of replacement phrases, wherein the augmented prompt is generated based on the user input. [This relates to a human generating a plurality of replacement phrases using pen and paper.] No additional limitations present.
Dependent claim 4 recites,
“4. The method of claim 1, further comprising:
identifying an additional entity phrase in the text prompt; and generating an additional replacement phrase for the additional entity phrase, wherein the augmented prompt includes the additional replacement phrase. [This relates to a human identifying an additional entity phrase in the human mind.
Dependent claim 5 recites,
“5. The method of claim 4, wherein: the additional replacement phrase is generated based on the replacement phrase. [This relates to a human generating a replacement phrase using pen and paper] No additional limitations present.
Dependent claim 6 recites,
“6. The method of claim 1, further comprising:
displaying the entity phrase; [This relates to a human displaying the entity phrase using pen and paper.]
receiving a selection of the entity phrase; [This relates to a human receiving a selection entity phrase using vision or auditory systems.]
and displaying the replacement phrase in response to the selection. [This relates to a human displaying the entity phrase using pen and paper.] No additional limitations present.
Dependent claim 7 recites,
“7. The method of claim 1, further comprising:
generating, using an image generation model, a synthetic image based on the augmented prompt, wherein the synthetic image depicts an entity described by the replacement phrase. [This relates to a human generating synthetic image based on the augmented prompt using pen and paper.] No additional limitations present.
Dependent claim 8 recites,
“8. The method of claim 1, further comprising:
retrieving a media item from a database based on the augmented prompt. [This relates to a human retrieving a media item from a database using logic and reasoning] No additional limitations present.
Dependent claim 9 recites,
9. The method of claim 1, further comprising: receiving a refresh command; [This relates to a human receiving a refresh command using visual or auditory processes]
and generating an additional replacement phrase based on the refresh command. [This relates to a human generating an additional replacement phrase using pen and paper] No additional limitations present.
Dependent claim 10 recites,
10. The method of claim 1, wherein marking the entity phrase comprises:
inserting a first tag before the entity phrase and a second tag after the entity phrase. [This relates to a human inserting a first tag before the entity phrase and a second tag after the entity phrase pen and paper] No additional limitations present.
Dependent claim 11 recites,
11. The method of claim 1, wherein: the language generation model is trained to generate the replacement phrase using a training set including a training text prompt and a training replacement phrase. [This relates to a human generating a replacement phrase using pen and paper] No additional limitations present.
Dependent claim 13 recites,
13. The method of claim 12, wherein obtaining the training set comprises:
identifying the training entity phrase in the training text prompt; [This relates to a human identifying a phrase using visual systems and the human mind]
and inserting the first tag before the training entity phrase and the second tag after the training entity phrase. [This relates to a human inserting a first tag before the entity phrase and a second tag after the entity phrase pen and paper] No additional limitations present.
Dependent claim 14 recites,
“14. The method of claim 12, wherein training the language generation model comprises:
generating, using the language generation model, a training output based on the training text prompt; [This relates to a human generating an output using pen and paper]
computing a loss function based on the training output and the training replacement phrase; [This relates to a human computing a loss function using pen and paper]
and updating parameters of the language generation model based on the loss function. [This relates to a human updating parameters using pen and paper] No additional limitations present.
Dependent claim 15 recites,
15. The method of claim 12, wherein obtaining the training set comprises:
obtaining an additional replacement phrase comprising an additional variant of the training entity phrase. [This relates to a human obtaining an additional replacement phrase using visual or auditory systems] No additional limitations present.
Dependent claim 17 recites,
17. The system of claim 16, the system further comprising: an augmentation component configured to generate an augmented prompt that includes the replacement phrase. [This relates to a human generate an augmented prompt using pen and paper] No additional limitations present.
Dependent claim 18 recites,
18. The system of claim 16, the system further comprising:
an image generation model comprising image generation parameters stored in the at least one memory, the image generation model configured to generate an image based on the replacement phrase. [This relates to a human generate an image based on the replacement phrase using pen and paper] No additional limitations present.
Dependent claim 19 recites,
19. The system of claim 16, the system further comprising: a retrieval component configured to retrieve a media item from a database based on the replacement phrase. [This relates to a human retrieve a media item from a database using logic and reasoning in the human mind] No additional limitations present.
Dependent claim 20 recites,
20. The system of claim 16, the system further comprising: a user interface configured to receive a selection of the entity phrase [This relates to a human receiving an entity phrase using visual or auditory systems]
and display the replacement phrase in response to the selection. [This relates to a human display the replacement phrase using pen and paper] No additional limitations present.
Claim Rejections - 35 USC § 102
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.
Claims 1, 12 and 16 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over BADJATIYA (U.S. Patent US 20250335775 A1).
Regarding Claim 1, BADJATIYAteaches 1. (Currently amended) A method for media processing, comprising: receiving a text prompt including an entity phrase; (see BADJATIYA “[0030] FIG. 1 is a diagram illustrating an example of a computer-based system 100 that includes a structured prompt builder 116 for generating, in an interactive and structured manner, a prompt 130 for use as input to a language model system 128…”) marking the entity phrase within the text prompt to obtain a revised prompt, (see BADJATIYA [0089] “Continuing with the detailed description of the user interfaces illustrated in FIG. 9, the interactive nature of the prompt builder is further exemplified as the user engages with the user interface. When the user makes a subsequent selection of the intent indicator “With Family,” as shown in 808-B, the prompt builder interface dynamically updates to reflect this choice, as shown with reference number 808-B. The intent indicator “With Family” is now marked with a checkmark, visually confirming the user's selection and indicating a more specific search direction within the broader travel context.”) wherein marking the entity phrase comprises inserting a first tag before the entity phrase and a second tag after the entity phrase, and (see BADJATIYA Figure 9, element 808-A, Element 808-B) wherein the revised prompt includes the first tag, the entity phrase, and the second tag; (see BADJATIYA Figure 10, element 808-C) generating, using a language generation model, a replacement phrase by performing autoregressive token generation based on a sequence of tokens from the revised prompt, (see BADJATIYA [0036] Once the user 102 has crafted his or her prompt 130 and is content with its composition, the user 102 can initiate the search process by interacting with a designated button or similar control element within the user interface. This action triggers the prompt executor 126, which is responsible for submitting the refined prompt 130 to the language model system 128. The language model system 128, which may operate as a standalone LLM or as part of a RAG-based LLM solution, processes the prompt 130 accordingly. In the case of a RAG-based system, a search service (not shown) may preprocess the prompt 130 to retrieve relevant content that will inform the LLM's response. The output 132, which is the language model's synthesized and contextually informed response to the prompt, is then conveyed back to the user 102 through an appropriate user interface 130. With some embodiments, this interface 130 takes the form of a chat-based interaction, which not only presents the information in a conversational manner but also allows for easy follow-up prompting. This facilitates a dialogue-like experience where the user 102 can continue to refine their queries based on the information provided, ensuring a dynamic and responsive search session.”)(examiner notes BADJATIYA calls tokens words [0002]) (see BADJATIYA [0035] “The in-line word and phrase recommendation engine 124 further enhances the prompt refinement process by analyzing the generated prompt and identifying key words and phrases that could benefit from replacement by alternatives. For these identified elements, the engine 124 creates a list of alternative words and phrases that could potentially offer a more precise expression of the intent of the user 102. When a user 102 engages in manual editing of the prompt and selects or hovers over a word or phrase with their cursor, this list of alternatives is presented in an accessible format, such as a drop-down menu or an in-line suggestion box. This feature empowers the user 102 to efficiently substitute the selected word or phrase with one of the alternatives, thereby fine-tuning the prompt 130 to more accurately reflect their search intent. The in-line word and phrase recommendation engine 124 thus streamlines the process of prompt customization and personalization, making it easier for users to achieve a prompt that is closely aligned with their specific informational needs and preferences.”) wherein the replacement phrase comprises a variant of the entity phrase and wherein the replacement phrase is generated based on a context of the revised prompt; and (see BADJATIYA [0089] “Continuing with the detailed description of the user interfaces illustrated in FIG. 9, the interactive nature of the prompt builder is further exemplified as the user engages with the user interface. When the user makes a subsequent selection of the intent indicator “With Family,” as shown in 808-B, the prompt builder interface dynamically updates to reflect this choice, as shown with reference number 808-B. The intent indicator “With Family” is now marked with a checkmark, visually confirming the user's selection and indicating a more specific search direction within the broader travel context.”) generating an augmented prompt that includes the replacement phrase in place of the entity phrase. (see BADJATIYA [0083] “As a result of this determination, the prompt builder user interface component 808 is presented to the user, seamlessly embedded within the search results 806-A, 806-B, 806-C, and 806-D. This integration positions the prompt builder user interface 808 as a natural extension of the search process, inviting the user to engage with the system further. The prompt builder user interface 808 is strategically placed among the search results to capture the user's attention and to suggest that a more refined search experience is available.”)
As to independent Claim 12, BADJATIYA teaches
12. (Currently amended) A method of training a machine learning model, the method comprising: obtaining a training set including a training text prompt and a training replacement phrase, (set BADJATIYA [0062-0063] “FIG. 5 is a diagram illustrating a detailed view of the prompt writer 122 and a prompt writer model 504, which operate together as part of the structured prompt builder 116 to write a prompt for a user and for use as input to an LLM that is part of an AI-based search assistant, consistent with some embodiments. Consistent with some embodiments, the prompt writer model 504 is a specialized machine learning model that is trained to synthesize user inputs into a coherent prompt that can be effectively processed by an LLM. The training of the prompt writer model 504 involves providing it with a user query 502-B, a current prompt 502-A, and one or more selected intent indicators 502-C. The user query 502-B represents the initial input from the user, which sets the context for the subsequent search. The current prompt 502-A is the evolving search narrative that has been generated based on the user's interactions with the system thus far. The selected intent indicators 502-C are the specific elements chosen by the user that reflect their particular areas of interest or the specific information they seek. [0063] During the training phase, the prompt writer model 504 is exposed to a vast array of such combinations, along with the corresponding output prompts that are deemed suitable for LLM processing. The model 504 learns to recognize patterns and relationships between the inputs and the desired output. It understands how to integrate the essence of the user query 502-B with the nuances brought in by the selected intent indicators 502-C, crafting a prompt 506 that is both grammatically coherent and rich in context. The output prompt 506 generated by the model 504 encapsulates the user's expressed intent, ensuring that the LLM receives a well-structured and informative input that can lead to accurate and relevant search results.”) wherein the training text prompt includes a training entity phrase surrounded by a first tag and a second tag, and the training replacement phrase comprises a ground-truth variant of the training entity phrase; and (See BADJATIYA [0073-0074] “FIG. 6 is a diagram illustrating a detailed view of an in-line word and phrase recommendation engine 124 and corresponding alternative word and phrase model 602, for use in generating suggested alternative words and phrases for use with a prompt for an LLM, consistent with some embodiments. With some embodiments, the in-line word and phrase recommendation engine 124 is designed to enhance the user's ability to refine prompts by offering alternative suggestions for specific words or phrases. The model 602 within the recommendation engine 124 is trained to identify key terms within a prompt 506 that could benefit from alternative expressions. This training involves analyzing a large corpus of text to understand the context in which different words or phrases are used and identifying potential synonyms or related phrases that could serve as replacements or supplements to the original text. [0074] During the training phase, the model 602 learns to recognize patterns of language usage and the semantic relationships between words and phrases. It is provided with examples of prompts where certain words or phrases have been replaced with alternatives, along with the outcomes in terms of the prompt's effectiveness in conveying the intended meaning. Through this process, the model 602 becomes adept at suggesting, for each of several words or phrases in a current prompt 506, a list of alternatives 604 that are contextually appropriate and semantically rich, thereby expanding the user's options for refining their prompt.”) training, using the training set, a language generation model to generate a replacement phrase by performing autoregressive token generation based on a text prompt, (See BADJATIYA [0002] “This is achieved in part by training the model to predict the next word or token in a sequence based on the context provided by the preceding words or tokens. This approach, known as autoregressive language modeling,”) wherein the replacement phrase comprises a variant of an entity phrase in the text prompt. (see BADJATIYA Figure 6 Element 604)
As to independent Claim 16, BADJATIYA teaches 16. (Currently amended) A system for media processing, comprising: at least one memory; at least one processor executing instructions stored in the at least one memory; (see BADJATIYA [0106] The machine 1300 may include processors 1310, memory 1330,”) an entity marking model comprising entity marking parameters stored in the at least one memory, (see BADJATIYA [0020] Retrieval-Augmented Generation (RAG) systems represent an innovative solution to the inherent limitations of standalone LLMs by integrating the capabilities of search engines with the generative prowess of LLMs. RAG-based LLM systems operate by first employing a retrieval component to search for and fetch relevant documents or data in real-time, thus ensuring access to the most current information available. This retrieval step helps to overcome the training data cut-off issue inherent in LLMs, as it allows a RAG-based LLM system to supplement the LLM's knowledge with up-to-date content, provided as context to the LLM, for example, as part of the prompt. Once the relevant information is retrieved, the generation component of the RAG system, typically an LLM, synthesizes the information into a coherent and contextually appropriate response. By combining the strengths of both search engines and LLMs, RAG-based systems can provide accurate and current answers to queries that require the latest data, while also maintaining the conversational and comprehensive answer format that LLMs are known for. This hybrid approach addresses the need for real-time data and mitigates the impact of any biases or gaps in the LLM's training dataset, ensuring that users receive responses that are both informative and reflective of the latest available information.”) the entity marking model trained to mark the entity phrase within a text prompt to obtain a revised prompt, (see BADJATIYA Figure 6, Element 602) wherein marking the entity phrase comprises inserting a first tag before the entity phrase and a second tag after the entity phrase, and (see BADJATIYA Figure 9 and 10 Element 808-A, 808-B) wherein the revised prompt includes the first tag, the entity phrase, and the second tag; and (see BADJATIYA Figure 9 and Figure 10 Element 808-A, 808-B, first “plan” second “trip” underlined ) a language generation model comprising text generation parameters stored in the at least one memory, the language generation model trained to generate a replacement phrase based on the revised prompt, (see BADJATIYA Figure 9 808-B) wherein the replacement phrase comprises a variant of the entity phrase and (see BADJATIYA Figure 9 and Figure 10 Element 808-A, 808-B) wherein the replacement phrase is generated based on a context of the revised prompt. (see BADJATIYA [0088] Concurrent with the selection of the “Plan a Trip” intent indicator, the prompt writer component 122 of the system 100, as shown in FIG. 5, generates an initial prompt intended for use with the LLM of the AI-based search assistant. The generated prompt, “Help me plan a memorable trip to Amsterdam,” is crafted to encapsulate the user's travel-related search intent. The words associated with the selected intent indicator may be formatted distinctively, such as through bolding or color change, to emphasize the elements of the query that have been refined by the user's selection. This initial prompt serves as a structured input to the LLM, which is designed to provide the user with comprehensive and contextually relevant information for planning a trip to Amsterdam. The system's ability to dynamically update the prompt in real time, as the user selects various intent indicators, showcases the interactive and adaptive nature of the inventive system, enhancing the overall search experience.”)
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.
Claims 2-9, 11,14, 15, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over BADJATIYA (U.S. Patent Number US 20250335775 A1), Tambi (U.S. Patent Number US 20240104131 A1)
As to Claim 2, BADJATIYA teaches 2. The method of claim 1,
BADJATIYA does not specifically teach further comprising: identifying, using a natural language processing model, the entity phrase from the text prompt by identifying a part of speech tag. However, Tambi does teach this limitation (see Tambi, [0022] “According to some embodiments, the query processing apparatus identifies a target phrase in an original query. The target phrase is a phrase to be replaced in the original query. The query processing apparatus then replaces the target phrase with a mask token to obtain a modified query and generates an alternative query using the masked language model. The query processing apparatus then retrieves a search result (e.g., images related to the alternative query).”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of BADJATIYA to incorporate identifying, using a natural language processing model, the entity phrase from the text prompt by identifying a part of speech tag of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
As to Claim 3, BADJATIYA teaches 3. The method of claim 1,
BADJATIYA does not specifically teach further comprising: generating a plurality of replacement phrases including the replacement phrase; However, Tambi does teach this limitation (see Tambi [0019] The present disclosure describes systems and methods for query processing. Embodiments of the present disclosure include a query processing apparatus configured to generate alternative queries based on an original query (i.e., to retrieve more varied search results). The present disclosure involves creating a modified query by using a mask token in place of a target phrase. A masked language model (MLM) generates candidate alternative phrases based on the modified query by filling the mask token with nearest neighbors, respectively. One or more alternative queries are then selected by comparing candidate phrase embedding to the target phrase embedding. Accordingly, the query processing apparatus provides a search result related to an alternative query such as images depicting the query. and receiving a user input selecting the replacement phrase from among the plurality of replacement phrases, wherein the augmented prompt is generated based on the user input. (see Tambi, [0031] “User device 105 may be a personal computer, laptop computer, mainframe computer, palmtop computer, personal assistant, mobile device, or any other suitable processing apparatus. In some examples, user device 105 includes software that incorporates a query processing application. In some examples, the query processing application on user device 105 may include functions of query processing apparatus 110. In some examples, user device 105 includes a user interface that displays one or more alternative queries to user 100. The user interface receives the original query from user 100 and receives a user input indicating a target phrase (e.g., “grunge”) of the original query.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of BADJATIYA to incorporate generating a plurality of replacement phrases including the replacement phrase; and receiving a user input selecting the replacement phrase from among the plurality of replacement phrases, wherein the augmented prompt is generated based on the user input of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 4, BADJATIYA teaches 4. The method of claim 1,
BADJATIYA does not specifically teach further comprising: identifying an additional entity phrase in the text prompt; and generating an additional replacement phrase for the additional entity phrase, wherein the augmented prompt includes the additional replacement phrase. However, Tambi does teach this limitation (See Tambi [0043] According to some embodiments, machine learning model 220 identifies a set of candidate alternative phrases based on the filtered set of replacement tokens. In some examples, machine learning model 220 compares the target phrase embedding to the set of candidate alternative phrase embeddings. Machine learning model 220 is an example of, or includes aspects of, the corresponding element described with reference to FIGS. 9 and 10.”)(see Tambi [0046-0047] According to some embodiments, masked language model 225 generates an alternative query based on the modified query using a masked language model (MLM), where the alternative query includes an alternative phrase in place of the target phrase that is consistent with a context of the target phrase. In some examples, masked language model 225 generates a set of replacement tokens based on the modified query. [0047] In some examples, masked language model 225 generates an additional alternative phrase based on the modified query. Masked language model 225 generates an additional alternative query that includes the additional alternative phrase in place of the target phrase. In some examples, masked language model 225 generates an additional alternative query based on the additional target phrase, where the additional alternative query includes an additional alternative phrase in place of the additional target phrase.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of BADJATIYA to incorporate identifying an additional entity phrase in the text prompt; and generating an additional replacement phrase for the additional entity phrase, wherein the augmented prompt includes the additional replacement phrase of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 5, BADJATIYA in view of Tambi teaches 5. The method of claim 4,
Furthermore, Tambi teaches wherein: the additional replacement phrase is generated based on the replacement phrase. (See Tambi [0043] According to some embodiments, machine learning model 220 identifies a set of candidate alternative phrases based on the filtered set of replacement tokens. In some examples, machine learning model 220 compares the target phrase embedding to the set of candidate alternative phrase embeddings. Machine learning model 220 is an example of, or includes aspects of, the corresponding element described with reference to FIGS. 9 and 10.”)(see Tambi [0046-0047] According to some embodiments, masked language model 225 generates an alternative query based on the modified query using a masked language model (MLM), where the alternative query includes an alternative phrase in place of the target phrase that is consistent with a context of the target phrase. In some examples, masked language model 225 generates a set of replacement tokens based on the modified query. [0047] In some examples, masked language model 225 generates an additional alternative phrase based on the modified query. Masked language model 225 generates an additional alternative query that includes the additional alternative phrase in place of the target phrase. In some examples, masked language model 225 generates an additional alternative query based on the additional target phrase, where the additional alternative query includes an additional alternative phrase in place of the additional target phrase.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of BADJATIYA to incorporate the additional replacement phrase is generated based on the replacement phrase of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 6, BADJATIYA teaches 6. The method of claim 1,
BADJATIYA does not specifically teach further comprising: displaying the entity phrase; receiving a selection of the entity phrase; and displaying the replacement phrase in response to the selection. However, Tambi does teach this limitation (see Tambi, [0031] “User device 105 may be a personal computer, laptop computer, mainframe computer, palmtop computer, personal assistant, mobile device, or any other suitable processing apparatus. In some examples, user device 105 includes software that incorporates a query processing application. In some examples, the query processing application on user device 105 may include functions of query processing apparatus 110. In some examples, user device 105 includes a user interface that displays one or more alternative queries to user 100. The user interface receives the original query from user 100 and receives a user input indicating a target phrase (e.g., “grunge”) of the original query.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of BADJATIYA to incorporate displaying the entity phrase; receiving a selection of the entity phrase; and displaying the replacement phrase in response to the selection of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 7, BADJATIYA teaches 7. The method of claim 1,
BADJATIYA does not specifically teach further comprising: generating, using an image generation model, a synthetic image based on the augmented prompt, wherein the synthetic image depicts an entity described by the replacement phrase. However, Tambi does teach this limitation (see Tambi, [0059] According to some embodiments, search interface 255 provides an image based on the alternative query via searching a database of candidate images. In some examples, an image generation model may be used to generate or synthesize a set of images based on the alternative query. Search interface 255 provides the set of images. Search interface 255 is an example of, or includes aspects of, the corresponding element described with reference to FIGS. 6 and 7.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of BADJATIYA to incorporate generating, using an image generation model, a synthetic image based on the augmented prompt, wherein the synthetic image depicts an entity described by the replacement phrase of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 8, BADJATIYA teaches 8. The method of claim 1,
BADJATIYA does not specifically teach further comprising: retrieving a media item from a database based on the augmented prompt. However, Tambi does teach this limitation (see Tambi, [0030] “Query processing apparatus 110 replaces the target phrase with a mask token to obtain a modified query and generates an alternative query based on the modified query using a masked language model, where the alternative query includes an alternative phrase in place of the target phrase that is consistent with a context of the target phrase. For example, the alternative phrase is “scratched background with geometric shapes”. Query processing apparatus 110 retrieves a search result (e.g., images related to the alternative phrase) from database 120. The alternative query and the search result is returned to user 100 via cloud 115 and user device 105.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of BADJATIYA to incorporate retrieving a media item from a database based on the augmented prompt of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 9, BADJATIYA teaches 9. The method of claim 1,
BADJATIYA does not specifically teach further comprising: receiving a refresh command; and generating an additional replacement phrase based on the refresh command. However, Tambi does teach this limitation (see Tambi, Figure 5, see Tambi [0091] “At operation 505, the user provides an original query. In some cases, the operations of this step refer to, or may be performed by, a user as described with reference to FIG. 1. In some examples, the user uploads the original query via a query upload element of a user interface. The original query recites “grunge background with geometric shapes.” The word “grunge” is underscored to indicate it is a target phrase selected by the user to be replaced.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of BADJATIYA to incorporate receiving a refresh command; and generating an additional replacement phrase based on the refresh command of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 11, BADJATIYA teaches 11. The method of claim 1,
BADJATIYA does not specifically teach wherein: the language generation model is trained to generate the replacement phrase using a training set including a training text prompt and a training replacement phrase. However, Tambi does teach this limitation (see Tambi [0051] According to some embodiments, embedding model 230 encodes the target phrase to obtain a target phrase embedding. In some examples, embedding model 230 encodes a set of candidate alternative phrases to obtain a set of candidate alternative phrase embeddings. In some examples, embedding model 230 encodes the alternative query to obtain an alternative query embedding. Embedding model 230 compares the alternative query embedding to one or more image embeddings. Embedding model 230 is an example of, or includes aspects of, the corresponding element described with reference to FIG. 3.”) (see Tambi [0052] “A word embedding is a learned representation for text where words that have the same meaning have a similar representation. GloVe and Word2vec are examples of systems for obtaining a vector representation of words. GloVe is an unsupervised algorithm for training a network using on aggregated global word-word co-occurrence statistics from a corpus. Similarly, a Word2vec model may include a shallow neural network trained to reconstruct the linguistic context of words. GloVe and Word2vec models may take a large corpus of text and produces a vector space as output. In some cases, the vector space may have a large number of dimensions. Each word in the corpus is assigned a vector in the space. Word vectors are positioned in the vector space in a manner such that similar words are located nearby in the vector space. In some”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of BADJATIYA to incorporate the language generation model is trained to generate the replacement phrase using a training set including a training text prompt and a training replacement phrase of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 14, BADJATIYA teaches 14. The method of claim 12,
BADJATIYA does not specifically teach wherein training the language generation model comprises: generating, using the language generation model, a training output based on the training text prompt; computing a loss function based on the training output and the training replacement phrase; and updating parameters of the language generation model based on the loss function. However, Tambi does teach this limitation (see Tambi [0045] “During the training process, the parameters and weights of machine learning model 220 are adjusted to increase the accuracy of the result (i.e., by attempting to minimize a loss function which corresponds to the difference between the current result and the target result). The weight of an edge increases or decreases the strength of the signal transmitted between nodes. In some cases, nodes have a threshold below which a signal is not transmitted at all. In some examples, the nodes are aggregated into layers. Different layers perform different transformations on their inputs. The initial layer is known as the input layer and the last layer is known as the output layer. In some cases, signals traverse certain layers multiple times.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of BADJATIYA to incorporate generating, using the language generation model, a training output based on the training text prompt; computing a loss function based on the training output and the training replacement phrase; and updating parameters of the language generation model based on the loss function of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 15, BADJATIYA teaches 15. The method of claim 12,
BADJATIYA does not specifically teach wherein obtaining the training set comprises: obtaining an additional replacement phrase comprising an additional variant of the training entity phrase. However, Tambi does teach this limitation (See Tambi [0043] According to some embodiments, machine learning model 220 identifies a set of candidate alternative phrases based on the filtered set of replacement tokens. In some examples, machine learning model 220 compares the target phrase embedding to the set of candidate alternative phrase embeddings. Machine learning model 220 is an example of, or includes aspects of, the corresponding element described with reference to FIGS. 9 and 10.”)(see Tambi [0046-0047] According to some embodiments, masked language model 225 generates an alternative query based on the modified query using a masked language model (MLM), where the alternative query includes an alternative phrase in place of the target phrase that is consistent with a context of the target phrase. In some examples, masked language model 225 generates a set of replacement tokens based on the modified query. [0047] In some examples, masked language model 225 generates an additional alternative phrase based on the modified query. Masked language model 225 generates an additional alternative query that includes the additional alternative phrase in place of the target phrase. In some examples, masked language model 225 generates an additional alternative query based on the additional target phrase, where the additional alternative query includes an additional alternative phrase in place of the additional target phrase.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of BADJATIYA to incorporate obtaining an additional replacement phrase comprising an additional variant of the training entity phrase of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 17, BADJATIYA teaches 17. The system of claim 16,
Furthermore, Tambi teaches the system further comprising: an augmentation component configured to generate an augmented prompt that includes the replacement phrase. However, Tambi does teach this limitation (see Tambi, [0031] “User device 105 may be a personal computer, laptop computer, mainframe computer, palmtop computer, personal assistant, mobile device, or any other suitable processing apparatus. In some examples, user device 105 includes software that incorporates a query processing application. In some examples, the query processing application on user device 105 may include functions of query processing apparatus 110. In some examples, user device 105 includes a user interface that displays one or more alternative queries to user 100. The user interface receives the original query from user 100 and receives a user input indicating a target phrase (e.g., “grunge”) of the original query.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of BADJATIYA to incorporate an augmentation component configured to generate an augmented prompt that includes the replacement phrase of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 18, BADJATIYA teaches 18. The system of claim 16,
BADJATIYA does not specifically teach the system further comprising: an image generation model comprising image generation parameters stored in the at least one memory, the image generation model configured to generate an image based on the replacement phrase. However, Tambi does teach this limitation (see Tambi, [0059] According to some embodiments, search interface 255 provides an image based on the alternative query via searching a database of candidate images. In some examples, an image generation model may be used to generate or synthesize a set of images based on the alternative query. Search interface 255 provides the set of images. Search interface 255 is an example of, or includes aspects of, the corresponding element described with reference to FIGS. 6 and 7.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of BADJATIYA to incorporate an image generation model comprising image generation parameters stored in the at least one memory, the image generation model configured to generate an image based on the replacement phrase of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Regarding Claim 19, BADJATIYA teaches 19. The system of claim 16,
BADJATIYA does not specifically teach the system further comprising: a retrieval component configured to retrieve a media item from a database based on the replacement phrase. However, Tambi does teach this limitation (see Tambi Figure 7 Element 720)
Regarding Claim 20, BADJATIYA teaches 20. The system of claim 16,
BADJATIYA does not specifically teach the system further comprising: a user interface configured to receive a selection of the entity phrase and display the replacement phrase in response to the selection. However, Tambi does teach this limitation (see Tambi, [0031] “User device 105 may be a personal computer, laptop computer, mainframe computer, palmtop computer, personal assistant, mobile device, or any other suitable processing apparatus. In some examples, user device 105 includes software that incorporates a query processing application. In some examples, the query processing application on user device 105 may include functions of query processing apparatus 110. In some examples, user device 105 includes a user interface that displays one or more alternative queries to user 100. The user interface receives the original query from user 100 and receives a user input indicating a target phrase (e.g., “grunge”) of the original query.”)
BADJATIYA and Tambi are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of BADJATIYA to incorporate a user interface configured to receive a selection of the entity phrase and display the replacement phrase in response to the selection of Tambi. This allows for receiving more varied search results as recognized by Tambi [0014].
Claims 10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over BADJATIYA (U.S. Patent Number US 20250335775 A1), in view of Patterson (U.S. Patent Number US 20120197885 A1).
Regarding Claim 10, BADJATIYA teaches 10. The method of claim 1,
BADJATIYA does not specifically teach wherein marking the entity phrase comprises: inserting a first tag before the entity phrase and a second tag after the entity phrase. However, Patterson does teach this limitation (see Patterson, [0119] In one embodiment, the related phrase information is a related phase bit vector. This bit vector may be characterized as a "bi-bit" vector, in that for each related phrase g.sub.k there are two bit positions, g.sub.k-1, g.sub.k-2. The first bit position stores a flag indicating whether the related phrase g.sub.k is present in the document d (i.e., the count for g.sub.k in document d is greater than 0). The second bit position stores a flag that indicates whether a related phrase g.sub.j of g.sub.k is also present in document d. The related phrases g.sub.l of a related phrase g.sub.k of a phrase g.sub.j are herein called the "secondary related phrases of g.sub.j" The counts and bit positions correspond to the canonical order of the phrases in R (sorted in order of decreasing information gain). This sort order has the effect of making the related phrase g.sub.k that is most highly predicted by g.sub.j associated with the most significant bit of the related phrase bit vector, and the related phrase g.sub.l that is least predicted by g.sub.j associated with the least significant bit.”)
BADJATIYA and Patterson are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of combination of BADJATIYA and Tambi to incorporate marking the entity phrase comprises: inserting a first tag before the entity phrase and a second tag after the entity phrase of Patterson. This allows idexing, document annotation, searching, ranking, and other areas of document analysis and processing as recognized by Patterson [0014].
Regarding Claim 13, BADJATIYA teaches 13. The method of claim 12,
BADJATIYA does not specifically teach wherein obtaining the training set comprises: identifying the training entity phrase in the training text prompt; and inserting the first tag before the training entity phrase and the second tag after the training entity phrase. However, Patterson does teach this limitation (see Patterson [0119] “In one embodiment, the related phrase information is a related phase bit vector. This bit vector may be characterized as a "bi-bit" vector, in that for each related phrase g.sub.k there are two bit positions, g.sub.k-1, g.sub.k-2. The first bit position stores a flag indicating whether the related phrase g.sub.k is present in the document d (i.e., the count for g.sub.k in document d is greater than 0). The second bit position stores a flag that indicates whether a related phrase g.sub.j of g.sub.k is also present in document d. The related phrases g.sub.l of a related phrase g.sub.k of a phrase g.sub.j are herein called the "secondary related phrases of g.sub.j" The counts and bit positions correspond to the canonical order of the phrases in R (sorted in order of decreasing information gain). This sort order has the effect of making the related phrase g.sub.k that is most highly predicted by g.sub.j associated with the most significant bit of the related phrase bit vector, and the related phrase g.sub.l that is least predicted by g.sub.j associated with the least significant bit.”)
BADJATIYA and Patterson are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a method of BADJATIYA to incorporate obtaining the training set comprises: identifying the training entity phrase in the training text prompt; and inserting the first tag before the training entity phrase and the second tag after the training entity phrase of Patterson. This allows indexing, document annotation, searching, ranking, and other areas of document analysis and processing as recognized by Patterson [0014].
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 KRISTEN MICHELLE MASTERS whose telephone number is (703)756-1274. The examiner can normally be reached M-F 8:30 AM - 5:00 PM.
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, Pierre Louis Desir can be reached at 571-272-7799. 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.
/KRISTEN MICHELLE MASTERS/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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