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 .
This Office Action is in response to correspondence filed 26 July 2024 in reference to application 18/786,368. Claims 1-20 are pending and have been examined.
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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are direct towards “a computer readable medium” which the specification defines at paragraph 0049 to include communication media such as carrier waves which have been held to be non-statutory. Therefore claims 15-20 are directed towards non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8, and 15 recite receiving a translation request including: an initial prompt received via a user interface and including a first language passage and a translation instruction defining a desired translation for the first language passage, and a context data signal received via a context data source coupled to a device associated with the user interface and corresponding to a context of the initial prompt; generating a context instruction based on the context data signal; generating a modified prompt including the initial prompt and the context instruction; sending the modified prompt to a neural machine translation model (NMT) to process the modified prompt; and receiving a second language translation passage as a response to the modified prompt, the second translation language passage being a second language translation of the first language passage translated according to the translation instruction and the context instruction.
The limitation of receiving a translation request including: an initial prompt received via a user interface and including a first language passage and a translation instruction defining a desired translation for the first language passage, and a context data signal received via a context data source coupled to a device associated with the user interface and corresponding to a context of the initial prompt, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a processor,” and “a memory” in claim 1 and “a computer readable medium” in claim 15, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the recited computer components, “receiving” in the context of this claim encompasses a person reading an initial prompt from an interface and reading context data from the interface.
The limitation of generating a context instruction based on the context data signal, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “generating” in the context of this claim encompasses a person writing out context instructions after observing the context signal.
The limitation of generating a modified prompt including the initial prompt and the context instruction, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “generating” in the context of this claim encompasses a person writing out a new prompt combining the original prompt and the context instruction.
The limitation of sending the modified prompt to a neural machine translation model (NMT) to process the modified prompt, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “sending” in the context of this claim encompasses a person entering the prompt into the user interface of a neural machine translation model.
The limitation of receiving a second language translation passage as a response to the modified prompt, the second translation language passage being a second language translation of the first language passage translated according to the translation instruction and the context instruction, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “receiving” in the context of this claim encompasses a person reading the generated translation in the user interface of a neural machine translation model.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims only additionally recite using processor, memory and computer readable media to perform the various steps. These components are recited at a high-level of generality (isuch that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer components to perform the various steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 2, 9, and 16 further recite receiving a plurality of the context data signals from a plurality of the context data sources; generating a plurality of the context instructions from the plurality of the context data signals; and including the plurality of context signals in the modified prompt. Similar to above, each of these steps can be perform by a human as a mental process. For example, a person can perform “receiving” by reading context from a plurality of sources, “generating” by writing instructions based on the plurality of signals, and “including” by writing the instructions into the modified prompt. Similar to above, no other limitations are recited that provide a practical application for or significantly more than the abstract idea itself. The claims are not patent eligible.
Claims 3, 10, and 17 further recite the context data source comprises a sensor or monitor of the device. However this recitation does not change the fact a human could perform the step of receiving by reading data generated by a sensor. Similar to above, no other limitations are recited that provide a practical application for or significantly more than the abstract idea itself. The claims are not patent eligible.
Claims 4, 11, and 18 further recite discretizing the context data signal to a discrete format; mapping the discretized context data signal to a corresponding instruction bucket; and generating the context instruction using the corresponding instruction bucket, wherein the discretization of the context data signal to the discrete format is performed using a large language model (LLM). Similar to above, each of these steps can be perform by a human as a mental process. For example, a person can perform “discretizing” by categorizing context values into specific categories like hot and cold, “mapping” grouping context according to their categories, and “generating” by writing the instructions according to the categories. The claim additionally recites using an LLM to do the discretization, but no specifics of the LLM are given. Thus this LLM can be interpreted as being a generic computer component performing generic computer processes. Similar to above, no other limitations are recited that provide a practical application for or significantly more than the abstract idea itself. The claims are not patent eligible.
Claims 5, 12, and 19 further recite wherein the discretization of the context data signal comprises identifying the context data signal as corresponding with one of a plurality of categories associated with the context data signal. However this recitation does not change the fact a human could perform the step of discretization because a human could categorize signals as discussed above. Similar to above, no other limitations are recited that provide a practical application for or significantly more than the abstract idea itself. The claims are not patent eligible.
Claims 6, 13, and 20 further recite wherein the context instruction identifies the context category of the plurality of context categories with which the context data signal corresponds. However this recitation does not change the fact a human could perform the step of generating because a human could write instructions including the categories as described above. Similar to above, no other limitations are recited that provide a practical application for or significantly more than the abstract idea itself. The claims are not patent eligible.
Claims 7 and 14 further recite wherein: the context data signal comprises the time of day in which the initial prompt was received via the user interface; and the plurality of categories associated with the context data signal comprises: morning, afternoon, and evening. However this recitation does not change the fact a human could perform the step of discretization and generating because a human could write read a time of day, categorize them into morning, afternoon, and evening, and write context instructions accordingly. Similar to above, no other limitations are recited that provide a practical application for or significantly more than the abstract idea itself. The claims are not patent eligible.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 8-10, and 15-17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ouyang (US PAP 2025/0238638).
Consider claim 1, Ouyang teaches A system (abstract), comprising:
a processor (0067, processors); and
a memory including instructions executable (0067, RAM, ROM, etc) by the processor to:
receive a translation request (0084-85, user may request a translation) including:
an initial prompt received via a user interface and including a first language passage and a translation instruction defining a desired translation for the first language passage (0084, “translate the input data into Spanish”), and
a context data signal received via a context data source coupled to a device associated with the user interface and corresponding to a context of the initial prompt (0088, receiving user comments based on candidate outputs);
generate a context instruction based on the context data signal (0088-94, collecting data to instruct LLM to generate desired outputs);
generate a modified prompt including the initial prompt and the context instruction (0094-96, generating a modified prompt based on user feedback);
send the modified prompt to a neural machine translation model (NMT) to process the modified prompt (0096, modified prompt sent to LLM); and
receive a second language translation passage as a response to the modified prompt, the second translation language passage being a second language translation of the first language passage translated according to the translation instruction and the context instruction (0096, candidate output using modified prompt is received from LLM, which in an example includes language translation).
Consider claim 2, Ouyang teaches The system of Claim 1, further including instructions executable by the processor to:
receive a plurality of the context data signals from a plurality of the context data sources (0088-90, sources including user comments, user scores, and machine generated scores);
generate a plurality of the context instructions from the plurality of the context data signals (0088-94, using comments and scores to generate data to modify prompts); and
include the plurality of context signals in the modified prompt (0094-96, modifying the prompts based on the collected comments and scores).
Consider claim 3, Ouyang teaches the system of Claim 1, wherein the context data source comprises a sensor or monitor of the device (0090, machine generated scores, here the machine is acting as a monitor of the device).
Consider claim 8, Ouyang teaches a method for utilizing context data in performing machine translations (abstract), comprising:
receiving a translation request (0084-85, user may request a translation) including:
an initial prompt received via a user interface and including a first language passage and a translation instruction defining a desired translation for the first language passage (0084, “translate the input data into Spanish”), and
a context data signal received via a context data source coupled to a device associated with the user interface and corresponding to a context of the initial prompt (0088, receiving user comments based on candidate outputs);
generating a context instruction based on the context data signal (0088-94, collecting data to instruct LLM to generate desired outputs);
generating a modified prompt including the initial prompt and the context instruction (0094-96, generating a modified prompt based on user feedback);
sending the modified prompt to a neural machine translation model (NMT) to process the modified prompt (0096, modified prompt sent to LLM); and
receiving a second language translation passage as a response to the modified prompt, the second translation language passage being a second language translation of the first language passage translated according to the translation instruction and the context instruction (0096, candidate output using modified prompt is received from LLM, which in an example includes language translation).
Claim 9 contains similar limitations as claim 2 and therefore is therefore rejected for the same reasons.
Claim 10 contains similar limitations as claim 3 and therefore is therefore rejected for the same reasons.
Consider claim 15, Ouyang teaches a computer-readable medium storing instructions (0067, processors, RAM, ROM, etc) that are operative upon execution by a processor to:
receive a translation request (0084-85, user may request a translation) including:
an initial prompt received via a user interface and including a first language passage and a translation instruction defining a desired translation for the first language passage (0084, “translate the input data into Spanish”), and
a context data signal received via a context data source coupled to a device associated with the user interface and corresponding to a context of the initial prompt (0088, receiving user comments based on candidate outputs);
generate a context instruction based on the context data signal (0088-94, collecting data to instruct LLM to generate desired outputs);
generate a modified prompt including the initial prompt and the context instruction (0094-96, generating a modified prompt based on user feedback);
send the modified prompt to a neural machine translation model (NMT) to process the modified prompt (0096, modified prompt sent to LLM); and
receive a second language translation passage as a response to the modified prompt, the second translation language passage being a second language translation of the first language passage translated according to the translation instruction and the context instruction (0096, candidate output using modified prompt is received from LLM, which in an example includes language translation).
Claim 16 contains similar limitations as claim 2 and therefore is therefore rejected for the same reasons.
Claim 17 contains similar limitations as claim 3 and therefore is therefore rejected for the same reasons.
Allowable Subject Matter
Claims 4-7, 11-14, and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Consider claim 4, Ouyang teaches he system of Claim 1, but does not specifically teach “further including instructions executable by the processor to: discretize the context data signal to a discrete format; map the discretized context data signal to a corresponding instruction bucket; and generate the context instruction using the corresponding instruction bucket, wherein the discretization of the context data signal to the discrete format is performed using a large language model (LLM)” when combined with each and every other limitation of the claim, the base claim and any intervening claims. Rather Ouyang using the contextual data as is to generate translations without further processing. Therefore claim 4 contains allowable subject matter.
Claims 5-7 depend on and further limit claim 4 and therefore contain allowable subject matter as well.
Claims 11 and 15 contain similar limitations as claim 4 and therefore contain allowable subject matter as well.
Claims 12-14, 19, and 20 depend on and further limit claims 1, 11 and 15 and therefore contain allowable subject matter as well.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Beagle et al. (US PAP 2025/0285043) teaches a similar method of transforming text inputs based on prompt modification, although in this case it translates natural language to a formal language.
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DOUGLAS GODBOLD
Examiner
Art Unit 2655
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655