DETAILED ACTION
This is the initial Office action based on the application filed on February 28, 2024.
Claims 1-20 are pending.
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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference signs mentioned in the description: “S402” and “S404” should be included in Figure 4. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0042], line 6, recites “and/or the processor 110.” It should read -- and/or the processor 104 --.
Appropriate correction is required.
Claim Objections
Claims 3 and 5 are objected to because of the following informalities:
Claim 3 recites, in lines 1-2, “for each first sub-recipe” and, in line 5, “of at least one first sub-recipe.” It should read -- for each of the at least one first sub-recipe – and -- of the at least one first sub-recipe -- respectively.
Claim 5 recites, in lines 1-2, “for each second sub-recipe” and, in lines 5-6, “of at least one second sub-recipe.” It should read -- for each of the at least one second sub-recipe – and -- of the at least one second sub-recipe – respectively.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 3 and 5 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 3 recites in lines 3-5 the limitation “wherein the second user input further includes at least one from among information that relates to at least one argument included in the first list of arguments and information that relates to a respective user edit of a name of at least one first sub-recipe.” The claims are rendered vague and indefinite because it is unclear to the Examiner if “includes at least one from among” is referring only to the “information that relates to at least one argument included in the first list of arguments” or if it is referring to “the information that relates to at least one argument” and “information that relates to a respective user edit.” In the interest of compact prosecution, the Examiner subsequently interprets the limitation as “includes at least one from among” referring to the “information that relates to at least one argument” and “information that relates to a respective user edit.”
Claim 5 recites in lines 3-6 the limitation “wherein the second user input further includes at least one from among information that relates to at least one argument included in the second list of arguments and information that relates to a respective user edit of a name of at least one second sub-recipe.” The claims are rendered vague and indefinite because it is unclear to the Examiner if “includes at least one from among” is referring only to the “information that relates to at least one argument included in the second list of arguments” or if it is referring to “the information that relates to at least one argument” and “information that relates to a respective user edit.” In the interest of compact prosecution, the Examiner subsequently interprets the limitation as “includes at least one from among” referring to the “information that relates to at least one argument” and “information that relates to a respective user edit.”
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim Interpretation: Under the broadest reasonable interpretation (BRI), the limitations of Claim 1 are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP § 2111.
Step 1: Claim 1 is directed to a method, which is a process (a series of steps or acts), and falls within one of the statutory categories of invention.
Step 2A, Prong One: Claim 1 recites the limitation:
analyzing the information that relates to the first recipe in order to automatically determine a list of sub-recipes for possible inclusion in the first recipe.
This recited step, under the broadest reasonable interpretation (BRI), cover performance of the steps in the human mind alone or with the aid of pen and paper. That is, other than reciting:
by the at least one processor.
Nothing in the claim precludes the steps from practically being performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper. For example, the limitation (a) in the context of the claim encompasses a human observing and evaluating information that relates to the first recipe in order to analyze the information and automatically determine a list of sub-recipes for possible inclusion in the first recipe using observation, evaluation, and judgment. See MPEP § 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind alone or with the aid of pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element:
by the at least one processor.
The additional element (1) is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the judicial exception using generic computer components. The processor is used as a tool to perform the receiving, analyzing, and transmitting steps of the claim. See MPEP § 2106.05(f).
Also, the claim recites the additional elements:
receiving, from a user, a first user input that includes information that relates to a first recipe;
transmitting, to the user, the list and a request for user feedback; and
receiving, from the user in response to the transmission of the list and the request for user feedback, a second user input that includes a set of selected sub-recipes to be included in the first recipe.
The additional elements (2) to (4) are mere data gathering/transmitting recited at a high level of generality, and thus are insignificant extra-solution activities. See MPEP § 2106.05(g). Furthermore, all uses of the recited judicial exception require such data gathering/transmitting and, as such, the additional elements do not impose any meaningful limits on the claim. The additional elements amount to necessary data gathering/transmitting. See MPEP § 2106.05.
Accordingly, even when viewed in combination, the 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 claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional element:
by the at least one processor.
The additional element (1) amounts to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply a judicial exception using generic computer components cannot provide an inventive concept.
Also, the claim recites the additional elements:
receiving, from a user, a first user input that includes information that relates to a first recipe;
transmitting, to the user, the list and a request for user feedback; and
receiving, from the user in response to the transmission of the list and the request for user feedback, a second user input that includes a set of selected sub-recipes to be included in the first recipe.
The additional elements (2) to (4) simply append well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to receive user input and transmit a list and a request for feedback to a user. Therefore, the limitations remain insignificant extra-solution activities even upon reconsideration and do not amount to significantly more.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as a combination adds nothing that is not already present when looking at the additional elements taken individually. Even when considered in combination, the additional elements represent mere instructions to apply a judicial exception using generic computer components and insignificant extra-solution activities, and therefore do not provide an inventive concept. The claim is not patent eligible.
Claims 2-9 are rejected under 35 U.S.C. 101 as directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more for at least the reasons stated above.
Claim 2 recites the limitations:
wherein when the information that relates to the first recipe includes a recipe name, the analyzing comprises: parsing the recipe name to determine a library, a package, a tool, and an action name;
using a probability distribution that relates to at least one prior usage of each of the library, the package, the tool, and the action name to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe; and
generating the list such that each of the at least one first sub-recipe and each respective probability score is included in the list.
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Claim 3 recites the limitation:
wherein the first forecast includes, for each first sub-recipe, a corresponding first list of arguments, and wherein the second user input further includes at least one from among information that relates to at least one argument included in the first list of arguments and information that relates to a respective user edit of a name of at least one first sub-recipe.
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Claim 4 recites the limitations:
wherein when the information that relates to the first recipe includes a recipe description, the analyzing comprises: parsing the recipe description to determine a set of word embeddings and a set of sentence embeddings;
comparing the set of word embeddings and the set of sentence embeddings to identify at least one related recipe;
using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective similarity score for each of the at least one second sub-recipe; and
generating the list such that each of the at least one second sub-recipe and each respective similarity score is included in the list.
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Claim 5 recites the limitation:
wherein the second forecast includes, for each second sub-recipe, a corresponding second list of arguments, and wherein the second user input further includes at least one from among information that relates to at least one argument included in the second list of arguments and information that relates to a respective user edit of a name of at least one second sub-recipe.
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Claim 6 recites the limitation:
wherein the analyzing is performed by applying an artificial intelligence (AI) algorithm that is trained by using a data set that contains historical information that relates to previously-used recipes.
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Claim 7 recites the limitations:
augmenting the data set by adding the set of selected sub-recipes to the historical information that relates to the previously-used recipes; and
updating a training of the AI algorithm by using the augmented data set.
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Claim 8 recites the limitations:
wherein the information that relates to the first recipe includes both a recipe name and a recipe description, and wherein the analyzing comprises: parsing the recipe name to determine a library, a package, a tool, and an action name;
using a probability distribution that relates to at least one prior usage of each of the library, the package, the tool, and the action name to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe;
parsing the recipe description to determine a set of word embeddings and a set of sentence embeddings;
comparing the set of word embeddings and the set of sentence embeddings to identify at least one related recipe;
using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective similarity score for each of the at least one second sub-recipe; and
generating the list such that each of the at least one first sub-recipe, each respective probability score, each of the at least one second sub-recipe, and each respective similarity score is included in the list.
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Claim 9 recites the limitations:
on a display;
displaying a user interface that includes a first field that prompts the user to provide the recipe name, a second field that prompts the user to provide the recipe description, a first clickable button that facilitates a user submission of the first user input, a third field that shows the list of sub-recipes for possible inclusion in the first recipe, a fourth field that shows a recipe template that is editable by the user, and a second clickable button that facilitates a user submission of the second user input.
These claims are dependent on Claim 1, but do not add any feature or subject matter that would solve the judicial exception deficiencies of Claim 1.
Claims 2, 4, and 8 (additional elements (a) to (c) recited in Claim 2, additional elements (a) to (d) recited in Claim 4, and additional elements (a) to (f) recited in Claim 8) recite further mental steps which can be practically performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper and thus, fail to make the claim any less abstract (see MPEP § 2106.04(a)(2)(III)).
Claims 3, 5-7, and 9 recite further additional elements that do not integrate the judicial exception into a practical application of the judicial exception. Specifically, the additional element (a) recited in Claim 9 fails to meaningfully limit the claim because it amounts to no more than mere instructions to apply the judicial exception using generic computer components. See MPEP § 2106.05(f). The additional element (a) recited in Claim 6 and the additional element (b) recited in Claim 7 fail to meaningfully limit the claim because they do not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. See MPEP § 2106.05(f). The additional element (a) recited in Claim 3 and the additional element (a) recited in Claim 5 fail to meaningfully limit the claim because they amount to merely indicating a field of use or technological environment in which to apply a judicial exception which does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP § 2106.05(h). The additional element (a) recited in Claim 7 and the additional element (b) recited in Claim 9 fail to meaningfully limit the claim because they are mere data gathering/outputting recited at a high level of generality, and thus are insignificant extra-solution activities. See MPEP § 2106.05(g). Therefore, Claims 2-9 when considered both individually and as a combination fail to integrate the abstract idea into a practical application.
The additional elements recited in Claims 2-9 are also not sufficient to amount to significantly more than the judicial exception. Specifically, Claims 2, 4, and 8 do not amount to significantly more than the abstract idea because they recite further mental steps that fail to make the claim any less abstract and do not recite any further additional elements. The additional element (a) recited in Claim 9 does not amount to significantly more than the abstract idea because it amounts to no more than mere instructions to apply the judicial exception using generic computer components which cannot provide an inventive concept. See MPEP § 2106.05(f). The additional element (a) recited in Claim 6 and the additional element (b) recited in Claim 7 do not amount to significantly more than the abstract idea because they do not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. See MPEP § 2106.05(f). The additional element (a) recited in Claim 3 and the additional element (a) recited in Claim 5 do not amount to significantly more because they amount to merely indicating a field of use or technological environment in which to apply a judicial exception which does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP § 2106.05(h). The additional element (a) recited in Claim 7 and the additional element (b) recited in Claim 9 do not amount to significantly more because they are mere data gathering/outputting recited at a high level of generality, and thus are insignificant extra-solution activities which simply appends well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to augment a data set and display a user interface with fields/buttons. Therefore, Claims 2-9 do not add any steps or additional elements, when considered both individually and as a combination, that amount to significantly more than the above-identified judicial exception that would convert Claim 1 into patent-eligible subject matter.
Claims 1-9 are therefore not drawn to patent-eligible subject matter as they are directed to an abstract idea without significantly more.
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Claim Interpretation: Under the broadest reasonable interpretation (BRI), the limitations of Claim 10 are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP § 2111.
Step 1: Claim 10 is directed to a system, which is a machine, and falls within one of the statutory categories of invention.
Step 2A, Prong One: Claim 10 recites the limitation:
analyze the information that relates to the first recipe in order to automatically determine a list of sub-recipes for possible inclusion in the first recipe.
This recited step, under the broadest reasonable interpretation (BRI), cover performance of the steps in the human mind alone or with the aid of pen and paper. That is, other than reciting:
a processor;
a memory;
a display;
a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to;
via the communication interface.
Nothing in the claim precludes the steps from practically being performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper. For example, the limitation (a) in the context of the claim encompasses a human observing and evaluating information that relates to the first recipe in order to analyze the information and automatically determine a list of sub-recipes for possible inclusion in the first recipe using observation, evaluation, and judgment. See MPEP § 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind alone or with the aid of pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements:
a processor;
a memory;
a display;
a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to;
via the communication interface.
The additional elements (1) to (5) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the judicial exception using generic computer components. The processor, memory, display, and communication interface are used as tools to perform the receiving, analyzing, and transmitting steps of the claim. See MPEP § 2106.05(f).
Also, the claim recites the additional elements:
receive, from a user, a first user input that includes information that relates to a first recipe;
transmit, to the user, the list and a request for user feedback; and
receive, from the user in response to the transmission of the list and the request for user feedback, a second user input that includes a set of selected sub-recipes to be included in the first recipe.
The additional elements (6) to (8) are mere data gathering/transmitting recited at a high level of generality, and thus are insignificant extra-solution activities. See MPEP § 2106.05(g). Furthermore, all uses of the recited judicial exception require such data gathering/transmitting and, as such, the additional elements do not impose any meaningful limits on the claim. The additional elements amount to necessary data gathering/transmitting. See MPEP § 2106.05.
Accordingly, even when viewed in combination, the 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 claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional elements:
a processor;
a memory;
a display;
a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to;
via the communication interface.
The additional elements (1) to (5) amount to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply a judicial exception using generic computer components cannot provide an inventive concept.
Also, the claim recites the additional elements:
receive, from a user, a first user input that includes information that relates to a first recipe;
transmit, to the user, the list and a request for user feedback; and
receive, from the user in response to the transmission of the list and the request for user feedback, a second user input that includes a set of selected sub-recipes to be included in the first recipe.
The additional elements (6) to (8) simply append well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to receive user input and transmit a list and a request for feedback to a user. Therefore, the limitations remain insignificant extra-solution activities even upon reconsideration and do not amount to significantly more.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as a combination adds nothing that is not already present when looking at the additional elements taken individually. Even when considered in combination, the additional elements represent mere instructions to apply a judicial exception using generic computer components and insignificant extra-solution activities, and therefore do not provide an inventive concept. The claim is not patent eligible.
Claims 11-18 are rejected under 35 U.S.C. 101 as directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more for at least the reasons stated above.
Claim 11 recites the limitations:
the processor is further configured to;
wherein when the information that relates to the first recipe includes a recipe name, perform the analysis by: parsing the recipe name to determine a library, a package, a tool, and an action name;
using a probability distribution that relates to at least one prior usage of each of the library, the package, the tool, and the action name to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe; and
generating the list such that each of the at least one first sub-recipe and each respective probability score is included in the list.
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Claim 12 recites the limitation:
wherein the first forecast includes, for each first sub-recipe, a corresponding first list of arguments, and wherein the second user input further includes at least one from among information that relates to at least one argument included in the first list of arguments and information that relates to a respective user edit of a name of at least one first sub-recipe.
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Claim 13 recites the limitations:
the processor is further configured to;
wherein when the information that relates to the first recipe includes a recipe description, perform the analysis by: parsing the recipe description to determine a set of word embeddings and a set of sentence embeddings;
comparing the set of word embeddings and the set of sentence embeddings to identify at least one related recipe;
using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective similarity score for each of the at least one second sub-recipe; and
generating the list such that each of the at least one second sub-recipe and each respective similarity score is included in the list.
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Claim 14 recites the limitation:
wherein the second forecast includes, for each second sub-recipe, a corresponding second list of arguments, and wherein the second user input further includes at least one from among information that relates to at least one argument included in the second list of arguments and information that relates to a respective user edit of a name of at least one second sub-recipe.
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Claim 15 recites the limitation:
wherein the processor is further configured to;
perform the analysis by applying an artificial intelligence (AI) algorithm that is trained by using a data set that contains historical information that relates to previously-used recipes.
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Claim 16 recites the limitations:
wherein the processor is further configured to;
augment the data set by adding the set of selected sub-recipes to the historical information that relates to the previously-used recipes; and
update a training of the AI algorithm by using the augmented data set.
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Claim 17 recites the limitations:
wherein the processor is further configured to;
wherein the information that relates to the first recipe includes both a recipe name and a recipe description, and perform the analysis by: parsing the recipe name to determine a library, a package, a tool, and an action name;
using a probability distribution that relates to at least one prior usage of each of the library, the package, the tool, and the action name to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe;
parsing the recipe description to determine a set of word embeddings and a set of sentence embeddings;
comparing the set of word embeddings and the set of sentence embeddings to identify at least one related recipe;
using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective similarity score for each of the at least one second sub-recipe; and
generating the list such that each of the at least one first sub-recipe, each respective probability score, each of the at least one second sub-recipe, and each respective similarity score is included in the list.
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Claim 18 recites the limitations:
wherein the processor is further configured to cause the display to;
display a user interface that includes a first field that prompts the user to provide the recipe name, a second field that prompts the user to provide the recipe description, a first clickable button that facilitates a user submission of the first user input, a third field that shows the list of sub-recipes for possible inclusion in the first recipe, a fourth field that shows a recipe template that is editable by the user, and a second clickable button that facilitates a user submission of the second user input.
These claims are dependent on Claim 10, but do not add any feature or subject matter that would solve the judicial exception deficiencies of Claim 10.
Claims 11, 13, and 17 (additional elements (b) to (d) recited in Claim 11, additional elements (b) to (e) recited in Claim 13, and additional elements (b) to (g) recited in Claim 17) recite further mental steps which can be practically performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper and thus, fail to make the claim any less abstract (see MPEP § 2106.04(a)(2)(III)).
Claims 11-18 recite further additional elements that do not integrate the judicial exception into a practical application of the judicial exception. Specifically, the additional element (a) recited in Claim 11, the additional element (a) recited in Claim 13, the additional element (a) recited in Claim 15, the additional element (a) recited in Claim 16, the additional element (a) recited in Claim 17, the additional element (a) recited in Claim 18 fail to meaningfully limit the claim because they amount to no more than mere instructions to apply the judicial exception using generic computer components. See MPEP § 2106.05(f). The additional element (b) recited in Claim 15 and the additional element (c) recited in Claim 16 fail to meaningfully limit the claim because they do not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. See MPEP § 2106.05(f). The additional element (a) recited in Claim 12 and the additional element (a) recited in Claim 14 fail to meaningfully limit the claim because they amount to merely indicating a field of use or technological environment in which to apply a judicial exception which does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP § 2106.05(h). The additional element (b) recited in Claim 16 and the additional element (b) recited in Claim 18 fail to meaningfully limit the claim because they are mere data gathering/outputting recited at a high level of generality, and thus are insignificant extra-solution activities. See MPEP § 2106.05(g). Therefore, Claims 11-18 when considered both individually and as a combination fail to integrate the abstract idea into a practical application.
The additional elements recited in Claims 11-18 are also not sufficient to amount to significantly more than the judicial exception. Specifically, the additional element (a) recited in Claim 11, the additional element (a) recited in Claim 13, the additional element (a) recited in Claim 15, the additional element (a) recited in Claim 16, the additional element (a) recited in Claim 17, the additional element (a) recited in Claim 18 do not amount to significantly more than the abstract idea because they amount to no more than mere instructions to apply the judicial exception using generic computer components which cannot provide an inventive concept. See MPEP § 2106.05(f). The additional element (b) recited in Claim 15 and the additional element (c) recited in Claim 16 do not amount to significantly more than the abstract idea because they do not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. See MPEP § 2106.05(f). The additional element (a) recited in Claim 12 and the additional element (a) recited in Claim 14 do not amount to significantly more because they amount to merely indicating a field of use or technological environment in which to apply a judicial exception which does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP § 2106.05(h). The additional element (b) recited in Claim 16 and the additional element (b) recited in Claim 18 do not amount to significantly more because they are mere data gathering/outputting recited at a high level of generality, and thus are insignificant extra-solution activities which simply appends well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to augment a data set and display a user interface with fields/buttons. Therefore, Claims 11-18 do not add any steps or additional elements, when considered both individually and as a combination, that amount to significantly more than the above-identified judicial exception that would convert Claim 10 into patent-eligible subject matter.
Claims 10-18 are therefore not drawn to patent-eligible subject matter as they are directed to an abstract idea without significantly more.
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Claim Interpretation: Under the broadest reasonable interpretation (BRI), the limitations of Claim 19 are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP § 2111.
Step 1: Claim 19 is directed to a non-transitory computer-readable storage medium, which is an article of manufacture, and falls within one of the statutory categories of invention.
Step 2A, Prong One: Claim 19 recites the limitation:
analyze the information that relates to the first recipe in order to automatically determine a list of sub-recipes for possible inclusion in the first recipe.
This recited step, under the broadest reasonable interpretation (BRI), cover performance of the steps in the human mind alone or with the aid of pen and paper. That is, other than reciting:
A non-transitory computer readable storage medium storing instructions for generating a recipe for software code maintenance, the storage medium comprising executable code which, when executed by a processor, causes the processor to.
Nothing in the claim precludes the steps from practically being performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper. For example, the limitation (a) in the context of the claim encompasses a human observing and evaluating information that relates to the first recipe in order to analyze the information and automatically determine a list of sub-recipes for possible inclusion in the first recipe using observation, evaluation, and judgment. See MPEP § 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind alone or with the aid of pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element:
A non-transitory computer readable storage medium storing instructions for generating a recipe for software code maintenance, the storage medium comprising executable code which, when executed by a processor, causes the processor to.
The additional element (1) is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the judicial exception using generic computer components. The non-transitory computer readable storage medium and processor are used as tools to perform the receiving, analyzing, and transmitting steps of the claim. See MPEP § 2106.05(f).
Also, the claim recites the additional elements:
receive, from a user, a first user input that includes information that relates to a first recipe;
transmit, to the user, the list and a request for user feedback; and
receive, from the user in response to the transmission of the list and the request for user feedback, a second user input that includes a set of selected sub-recipes to be included in the first recipe.
The additional elements (2) to (4) are mere data gathering/transmitting recited at a high level of generality, and thus are insignificant extra-solution activities. See MPEP § 2106.05(g). Furthermore, all uses of the recited judicial exception require such data gathering/transmitting and, as such, the additional elements do not impose any meaningful limits on the claim. The additional elements amount to necessary data gathering/transmitting. See MPEP § 2106.05.
Accordingly, even when viewed in combination, the 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 claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional element:
A non-transitory computer readable storage medium storing instructions for generating a recipe for software code maintenance, the storage medium comprising executable code which, when executed by a processor, causes the processor to.
The additional element (1) amounts to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply a judicial exception using generic computer components cannot provide an inventive concept.
Also, the claim recites the additional elements:
receive, from a user, a first user input that includes information that relates to a first recipe;
transmit, to the user, the list and a request for user feedback; and
receive, from the user in response to the transmission of the list and the request for user feedback, a second user input that includes a set of selected sub-recipes to be included in the first recipe.
The additional elements (2) to (4) simply append well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to receive user input and transmit a list and a request for feedback to a user. Therefore, the limitations remain insignificant extra-solution activities even upon reconsideration and do not amount to significantly more.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as a combination adds nothing that is not already present when looking at the additional elements taken individually. Even when considered in combination, the additional elements represent mere instructions to apply a judicial exception using generic computer components and insignificant extra-solution activities, and therefore do not provide an inventive concept. The claim is not patent eligible.
Claim 20 is rejected under 35 U.S.C. 101 as directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more for at least the reasons stated above.
Claim 20 recites the limitations:
wherein when executed by the processor, the executable code further causes the processor to;
wherein the information that relates to the first recipe includes both a recipe name and a recipe description, and perform the analysis by: parsing the recipe name to determine a library, a package, a tool, and an action name;
using a probability distribution that relates to at least one prior usage of each of the library, the package, the tool, and the action name to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe;
parsing the recipe description to determine a set of word embeddings and a set of sentence embeddings;
comparing the set of word embeddings and the set of sentence embeddings to identify at least one related recipe;
using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective similarity score for each of the at least one second sub-recipe; and
generating the list such that each of the at least one first sub-recipe, each respective probability score, each of the at least one second sub-recipe, and each respective similarity score is included in the list.
This claim is dependent on Claim 19, but does not add any feature or subject matter that would solve the judicial exception deficiencies of Claim 19.
The additional elements (b) to (g) recited in Claim 20 recite further mental steps which can be practically performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper and thus, fail to make the claim any less abstract (see MPEP § 2106.04(a)(2)(III)).
Claim 20 recites further additional elements that do not integrate the judicial exception into a practical application of the judicial exception. Specifically, the additional element (a) recited in Claim 20 fails to meaningfully limit the claim because it amounts to no more than mere instructions to apply the judicial exception using generic computer components. See MPEP § 2106.05(f). Therefore, Claim 20 when considered both individually and as a combination fail to integrate the abstract idea into a practical application.
The additional elements recited in Claim 20 are also not sufficient to amount to significantly more than the judicial exception. Specifically, the additional element (a) recited in Claim 20 does not amount to significantly more than the abstract idea because it amounts to no more than mere instructions to apply the judicial exception using generic computer components which cannot provide an inventive concept. See MPEP § 2106.05(f). Therefore, Claim 20 does not add any steps or additional elements, when considered both individually and as a combination, that amount to significantly more than the above-identified judicial exception that would convert Claim 19 into patent-eligible subject matter.
Claims 19 and 20 are therefore not drawn to patent-eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 2007/0106497 (hereinafter “Ramsey”) in view of US 11,328,265 (hereinafter “Givoly”).
As per Claim 1, Ramsey discloses:
A method (paragraph [0037], “In addition, the disclosed subject matter may be implemented as a system, method, apparatus or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer or processor based device to implement aspects detailed herein (emphasis added).”) for generating a recipe for software code maintenance, the method being implemented by at least one processor (Figure 1: 120), the method comprising:
receiving, by the at least one processor (Figure 1: 120) from a user, a first user input that includes information that relates to a first recipe (paragraph [0119], “To perform these semantic analysis and cross-mapping actions, the NLSI first receives the user's natural language input [information] (e.g., statement, query, request, and the like) through a user interface (emphasis added)”; paragraph [0122], “Once the user input has been broken into tokens, a search is performed using those tokens to find or return the best tasks or scripts [recipes] relative to the initial mappings extracted from the available scripts (emphasis added).”; paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts are found following semantic analysis of that natural language input, a determination is made as to which scripts [recipes] to execute and/or present to the user (emphasis added).”; paragraph [0084] “In general, Keywords are terms that might be used to identify a particular script [recipe]. These are words that would be entered as part of a user query, provided in the form of a natural language input, as described in further detail below (emphasis added).”; paragraph [0032] “Scripts [recipes] or other code, such as "Active Content Wizards" (ACWs), are predefined sets of instructions that can perform almost any task that could be performed manually […] In general, such scripts have the ability to perform virtually any function on a client machine, and do not necessarily require user intervention or interaction once the script has been activated (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses the NLSI receiving a user’s natural language input in order to find the best potentially matching scripts and that keywords within a user query may be used to identify a certain script (recipe). One of ordinary skill in the art would readily comprehend that the natural language input/user query is information that relates to a desired script (first recipe).];
analyzing, by the at least one processor (Figure 1: 120), the information that relates to the first recipe in order to automatically determine a list of sub-recipes for possible inclusion in the first recipe (Figure 7: 710, 715, 730; paragraph [0118], “For example, as noted above, semantic mapping of the user's natural language input is provided with respect to the initial mappings of the available scripts. In providing this mapping, a "user" first inputs a natural language query. This natural language query [information that relates to the first recipe] is analyzed in order to search for and determine appropriate task/script cross-mapping, and to probabilistically rank the cross-mapping results in order of likelihood (emphasis added).”; paragraph [0113], “In general, semantic analysis attempts to match a natural language input to certain tasks or actions provided by an automated system. Typically, semantic processing breaks the natural language input into strings of characters called tokens. The NLSI described herein provides a system that analyzes these tokens as well as the user "context" in which the natural language input was provided to determine the appropriate task or tasks. Scripts, ACWs or other code corresponding to the identified tasks are then either executed automatically, or presented to the user for further input or selection, as described in further detail below (emphasis added).”; paragraph [0067], “To address such issues, a "Natural Language Script Interface" (NLSI), as described herein, provides a natural language interface and query system for automatically interpreting natural language inputs to select, initiate, and/or otherwise present one or more scripts to the user (emphasis added).”; paragraph [0160], “The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”; paragraph [0163], “A simple example of a tested embodiment of the NLSI is […] the user enters the natural language input "set background image bliss." The NLSI evaluates this natural language input 810 and returns a list of four potentially matching tasks/scripts (emphasis added).”; paragraph [0153], “[…] to allow the user to select the desired script from the predicted list of scripts, then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses the user selecting a desired script from a list of predicted scripts as well as the NLSI automatically analyzing the natural language query/input (information that relates to the first recipe) received from the user to present a list of potentially matching tasks/scripts (recipes) for user selection. Ramsey also discloses an example where a user enters a natural language input relating to the desired script and the NLSI returning a list of four potentially matching tasks/scripts. One of ordinary skill in the art would readily comprehend that the NLSI automatically returns (determines) a list of matching/relating scripts (sub-recipes) to present to the user after analyzing the natural language input (automatically interpreting the input). Moreover, one of ordinary skill in the art would readily comprehend that presenting the list of scripts (sub-recipes) to the user for selection that matches the desired script (first recipe) described in the natural language query means the scripts presented are for possible inclusion in the desired script; the user selected scripts are the desired script.];
transmitting, by the at least one processor (Figure 1: 120) to the user, the list and a request for user feedback (paragraph [0160], “The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”; paragraph [0163], “A simple example of a tested embodiment of the NLSI is […] the user enters the natural language input "set background image bliss." The NLSI evaluates this natural language input 810 and returns [transmits] a list of four potentially matching tasks/scripts (emphasis added).”; paragraph [0151], “Rating, comparison and/or feedback can also be provided [requested] on a user's overall level of satisfaction with the presented list of predicted scripts by providing a user interface, such as a clickable rating form, for example, to allow the user to indicate the user's overall satisfaction with the presented list, or of individual elements within that list (emphasis added).”); and
receiving, by the at least one processor (Figure 1: 120) from the user in response to the transmission of the list […], a second user input that includes a set of selected sub-recipes to be included in the first recipe (Figure 7: 730, 740; paragraph [0113], “The NLSI described herein provides a system that analyzes these tokens as well as the user "context" in which the natural language input was provided to determine the appropriate task or tasks. Scripts, ACWs or other code corresponding to the identified tasks are then either executed automatically, or presented [transmitted] to the user for further input or selection, as described in further detail below (emphasis added).”; paragraph [0160], “In various embodiments, the user interacts with one or more of the presented scripts via the user interface 735 to select, complete, and/or execute 740 one or more of the identified scripts, as described above (emphasis added).”; paragraph [0163], “A simple example of a tested embodiment of the NLSI is […] the user enters the natural language input "set background image bliss." The NLSI evaluates this natural language input 810 and returns a list of four potentially matching tasks/scripts (emphasis added).”; paragraph [0169], “As illustrated by FIG. 9, following user selection of the first task 820, an Active Content Wizard (ACW) type script is activated to guide the user through changing the desktop background image to the standard "Bliss" image. It should be noted that in an alternate embodiment, selection of the first task 820 automatically initiates changing of the desktop background image to the standard "Bliss" image without further user interaction (emphasis added).”; paragraph [0153], ““[…] to allow the user to select the desired script from the predicted list of scripts, then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses presenting the list of scripts potentially matching the natural language input (first user input) describing the desired script (first recipe) and execution of user selected scripts from the list. Ramsey also discloses an example of a user selecting a task/script (sub-recipe) from the presented list of possibly matching scripts and the selected script being executed. One of ordinary skill in the art would readily comprehend that to execute the selected script, the NLSI received a user input (second user input) of a set of selected scripts (sub-recipes), in response to the transmission of the list of matching scripts (sub-recipes), to be included in the first recipe. In other words, the selected scripts (sub-recipes) matched the desired script (first recipe) completely or closely enough to execute, that the selected scripts are the desired script (first recipe).].
Ramsey does not explicitly disclose:
receiving, by the at least one processor from the user in response to the transmission of the list and the request for user feedback, a second user input that includes a set of selected sub-recipes to be included in the first recipe.
However, Givoly discloses:
receiving, in response to the transmission of the request for user feedback, a [user input] (col. 38 lines 35-37 & lines 43-47, “In operation, the interface 2000 may be utilized to prompt a user for feedback associated with a variety of automated operations […] Furthermore, in various embodiments, the user may have the ability to respond to the prompt by entering text, selecting a one click button, selecting from a list (e.g. a drop down list, etc.), using an audible input, and/other any other response technique (emphasis added).”; col. 37 lines 53-58, “The virtual personal assistant code may prompt the user in a variety of ways. For example, in various embodiments, the virtual personal assistant code may include a pop-up dialog box, a window, an animation, audio capability, a toolbar, and/or any other capability for prompting a user and receiving a user response [user input] (emphasis added).”).
Ramsey is within the same field of endeavor as the claimed invention regarding the generation and presentation of a list of scripts for user selection. Givoly is also within the same field of endeavor as the claimed invention regarding obtaining user feedback for desired items.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Givoly into the teaching of Ramsey to include “receiving, by the at least one processor from the user in response to the transmission of the list and the request for user feedback, a second user input that includes a set of selected sub-recipes to be included in the first recipe.” The modification would be obvious because one of ordinary skill in the art would be motivated to utilize user feedback to “more effectively search for” desired items (Givoly, col. 31 lines 19-23).
As per Claim 10, Ramsey discloses:
A computing apparatus (Figure 1; paragraph [0037], “In addition, the disclosed subject matter may be implemented as a system, method, apparatus or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer or processor based device to implement aspects detailed herein (emphasis added).”) for generating a recipe for software code maintenance, the computing apparatus comprising: a processor (paragraph [0043], “Components of computer 110 may include, but are not limited to, processing unit(s) 120, a system memory 130, and a system bus 121 that couples various system components including the system memory to the processing unit 120 (emphasis added).”); a memory (Components of computer 110 may include, but are not limited to, processing unit(s) 120, a system memory 130, and a system bus 121 that couples various system components including the system memory to the processing unit 120 (emphasis added).); a display (paragraph [0105], “Further, the interface module 610 provides various types of user interfaces for receiving the user input. For example, a graphical user interface (GUI) allows users to express their intentions by directly manipulating the graphical and/or textual objects displayed on display device (emphasis added).”); and a communication interface coupled to each of the processor, the memory, and the display (paragraph [0041], “Particular "worker nodes," as described in further detail below, may also include devices having at least some minimum computational capability in combination with a communications interface, including, for example, home appliances, security systems […] (emphasis added).”; paragraph [0049], “These and other input devices are often connected to the processing unit 120 through a user input interface 160 that is coupled to the system bus 121, but may be connected by other interface and bus structures, such as, for example, a parallel port, game port, or a universal serial bus (USB). A monitor 191 or other type of display device is also connected to the system bus 121 via an interface, such as a video interface 190. (emphasis added).”), wherein the processor is configured to: […]
Claim 10 is an apparatus claim corresponding to method Claim 1 and the remainder of Claim 10 is rejected for the same reasons as given in the rejection of Claim 1.
As per Claim 19, Ramsey discloses:
A non-transitory computer readable storage medium storing instructions (paragraph [0045], “Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology; CD-ROM, digital versatile disks (DVD), or other optical disk storage; magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices; or any other medium which can be used to store the desired information and which can be accessed by computer 110 (emphasis added).”; paragraph [0044], “By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data (emphasis added).”) for generating a recipe for software code maintenance, the storage medium comprising executable code which, when executed by a processor (paragraph [0038], “While the subject matter described herein is generally discussed in the context of computer-executable instructions of a computer program that runs on a computer and/or computers […] Moreover, those skilled in the art will appreciate that the inventive methods may be practiced with other computer system configurations, including single-processor or multiprocessor computer systems […] (emphasis added).”), causes the processor to: […].
Claim 19 is a non-transitory computer readable storage medium claim corresponding to method Claim 1 and the remainder of Claim 19 is rejected for the same reasons as given in the rejection of Claim 1.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsey in view of Givoly as applied to Claims 1 and 10 above, and further in view of US 2011/0055544 (hereinafter “Vidal”).
As per Claim 2, the rejection of Claim 1 is incorporated; and Ramsey discloses “information that relates to the first recipe (paragraph [0084], “In general, Keywords are terms that might be used to identify a particular script [recipe]. These are words that would be entered as part of a user query, provided in the form of a natural language input, as described in further detail below (emphasis added).”; paragraph [0153], “[…] to allow the user to select the desired script from the predicted list of scripts […] (emphasis added).”),” “a recipe name (paragraph [0083], “Scripts also include a unique identifier, such as a name or other identifier, for allowing selection of individual scripts once those scripts have been identified as possible or probable matches to the user's query. For example, a script for changing the refresh rate of a display device might be identified as "ChangeRefreshRate" or simply as "Script123." (emphasis added).”),” “the analyzing comprises: using a probability distribution that relates to at least one prior usage of each of the [information relating to a query] to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe (paragraph [0118], “For example, as noted above, semantic mapping of the user's natural language input is provided with respect to the initial mappings of the available scripts. In providing this mapping, a "user" first inputs a natural language query. This natural language query is analyzed in order to search for and determine appropriate task/script cross-mapping, and to probabilistically rank the cross-mapping results in order of likelihood (emphasis added).”; paragraph [0119], “To perform these semantic analysis and cross-mapping actions, the NLSI first receives the user's natural language input (e.g., statement, query, request, and the like) through a user interface. This input is then broken or divided into a "token" or set(s) of tokens, with each tokens representing a string of characters […] Further, there are various ranking systems that can be utilized by these techniques. For example, an information retrieval system can utilize an Okapi-based ranking system, a query classifier (query to script mapping) can utilize a Naive-Bayesian model [uses a probability distribution] […] (emphasis added).”; paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts are found following semantic analysis of that natural language input, a determination is made as to which scripts to execute and/or present to the user. In one embodiment, two sub-scores are generated, one for the information retrieval portion and one for the query classifier portion of the semantic analysis-based script matching. Next, the structure of the script (i.e., the above-described "Task Interface") is reviewed relative to the semantic analysis, and a ranking is performed on the script structure, producing yet another sub-score. Each of these sub-scores is then combined to determine a final score based on each of the sub-scores. In one embodiment, this final score, or ranking, is then used to provide feedback to improve performance of future queries for the current and/or future users (emphasis added).”; paragraph [0160], “Once the scripts have been identified, and any slots filled, if applicable or possible, the identified scripts are scored and ranked 725 in order of most probable to least probable matches to the users presumed intent, in order to construct [generate] a probable script list [first forecast]. The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”; paragraph [0153], “[…] to allow the user to select the desired script from the predicted list of scripts, then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses generating a probable script list of potentially matching scripts to the user query. One of ordinary skill in the art would readily comprehend that presenting the probable script list (first forecast) of potentially matching scripts (sub-recipes) to the user for selection that matches the desired script (first recipe) described in the natural language query means the scripts presented are for possible inclusion in the desired script, in other words, the user selected scripts are the desired script. Ramsey discloses probabilistically ranking appropriate scripts (sub-recipes) that potentially match the natural language query and a query classifier utilizing a Naive-Bayesian model in a ranking system. Ramsey also discloses that ranking is a final score that was calculated using a sub-score generated for the query classifier (probability score) and that the rank is used to provide feedback in improving the performance of future queries. One of ordinary skill in the art would readily comprehend that using a Naive-Bayesian model in a ranking system to generate and present a ranked list of scripts includes using a probability distribution in generating the list of potentially matching scripts in a ranked order where the rank was calculated using a score for the query classifier (probability score). Moreover, one of ordinary skill in the art would readily comprehend that the ranking being used to improve performance for future queries means that the probability distribution used may relate to at least one prior usage of each of the information relating to a query for improved performance.],” and “generating the list such that each of the at least one first sub-recipe and each respective probability score is included in the list (paragraph [0118], “For example, as noted above, semantic mapping of the user's natural language input is provided with respect to the initial mappings of the available scripts. In providing this mapping, a "user" first inputs a natural language query. This natural language query is analyzed in order to search for and determine appropriate task/script cross-mapping, and to probabilistically rank the cross-mapping results in order of likelihood (emphasis added).”; paragraph [0119], “To perform these semantic analysis and cross-mapping actions, the NLSI first receives the user's natural language input (e.g., statement, query, request, and the like) through a user interface. This input is then broken or divided into a "token" or set(s) of tokens, with each tokens representing a string of characters […] Further, there are various ranking systems that can be utilized by these techniques. For example, an information retrieval system can utilize an Okapi-based ranking system, a query classifier (query to script mapping) can utilize a Naive-Bayesian model [uses a probability distribution] […] (emphasis added).”; paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts are found following semantic analysis of that natural language input, a determination is made as to which scripts to execute and/or present to the user. In one embodiment, two sub-scores are generated, one for the information retrieval portion and one for the query classifier portion of the semantic analysis-based script matching […] Each of these sub-scores is then combined to determine a final score based on each of the sub-scores. In one embodiment, this final score, or ranking, is then used to provide feedback to improve performance of future queries for the current and/or future users (emphasis added).”; paragraph [0160], “Once the scripts have been identified, and any slots filled, if applicable or possible, the identified scripts are scored and ranked 725 in order of most probable to least probable matches to the users presumed intent, in order to construct [generate] a probable script list. The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses probabilistically ranking appropriate scripts (sub-recipes) that potentially match the natural language query and a query classifier utilizing a Naive-Bayesian model (uses a probability distribution) in a ranking system. Ramsey also discloses that ranking is a final score that was calculated using a sub-score generated for the query classifier (probability score). One of ordinary skill in the art would readily comprehend that generating the probable script list in ranked order includes a respective probability score for each script (sub-recipe) because the order of each script corresponds to its probabilistic rank which was computed using a sub-score for the query classifier (probability score).],” but the combination of Ramsey and Givoly does not explicitly disclose:
wherein when the information that relates to the first recipe includes a recipe name, the analyzing comprises:
parsing the recipe name to determine a library, a package, a tool, and an action name;
using a probability distribution that relates to at least one prior usage of each of the library, the package, the tool, and the action name to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe.
However, Vidal discloses:
wherein when the information that relates to the [action] includes [metadata] (paragraph [0036], “The tagging tool 116 can be configured to parse the metadata and the other information associated with the action in order to extract any information that describes the action, the reasons the action was performed, and any software programs, files, and software libraries affected by the action (emphasis added).”), the analyzing comprises:
parsing the [metadata] to determine a library, a package, a tool, and an action name (paragraph [0036], “The tagging tool 116 can be configured to parse the metadata and the other information associated with the action in order to extract any information that describes the action, the reasons the action was performed, and any software programs, files, and software libraries affected by the action. For example, the tagging tool 116 can parse and extract information such as the name of the software package 108, the version of the software package 108, the version of the previous software package 108 if updating, the reason the package manager 110 is performing the action (e.g. new software installation, software installation update) [action name], date of the action, and a list of software programs [tool], files, and software libraries affected by the action (emphasis added).”).
the library, the package, the tool, and the action name (Figure 4A; paragraph [0036], “For example, the tagging tool 116 can parse and extract information such as the name of the software package 108, the version of the software package 108, the version of the previous software package 108 if updating, the reason the package manager 110 is performing the action (e.g. new software installation, software installation update) [action name], date of the action, and a list of software programs [tool], files, and software libraries affected by the action (emphasis added).”).
Vidal is within the same field of endeavor as the claimed invention regarding parsing and extracting desired information.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Vidal into the combined teachings of Ramsey and Givoly to include “wherein when the information that relates to the first recipe includes a recipe name, the analyzing comprises: parsing the recipe name to determine a library, a package, a tool, and an action name; using a probability distribution that relates to at least one prior usage of each of the library, the package, the tool, and the action name to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe.” The modification would be obvious because one of ordinary skill in the art would be motivated to use a tagging tool, that determines a library, a package, a tool, and an action name affected by an action, to track the history of actions performed by a package manager/system and the effects of additional actions, so “the user of the computing system can efficiently and reliably modify the computing system with assurances that the modification will not damage existing software of the computing system” (Vidal, paragraphs [0017 & 0036]).
Claim 11 is an apparatus claim corresponding to method Claim 2 and is rejected for the same reasons as given in the rejection of that claim.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsey in view of Givoly and Vidal as applied to Claims 2 and 11 above, and further in view of US 8,244,804 (hereinafter “Casselman”).
As per Claim 3, the rejection of Claim 2 is incorporated; and Ramsey discloses “wherein the first forecast includes […] (paragraph [0160], “Once the scripts have been identified, and any slots filled, if applicable or possible, the identified scripts are scored and ranked 725 in order of most probable to least probable matches to the users presumed intent, in order to construct a probable script list [first forecast]. The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”),” “first sub-recipe (paragraph [0153], “[…] to allow the user to select the desired script from the predicted list of scripts [first sub-recipes], then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”; paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts are found following semantic analysis of that natural language input, a determination is made as to which scripts [first sub-recipes] to execute and/or present to the user (emphasis added).”),” and “second user input (paragraph [0160], “In various embodiments, the user interacts with one or more of the presented scripts via the user interface 735 to select, complete, and/or execute 740 one or more of the identified scripts, as described above (emphasis added).”; paragraph [0169], “As illustrated by FIG. 9, following user selection of the first task 820 [second user input], an Active Content Wizard (ACW) type script is activated to guide the user through changing the desktop background image to the standard "Bliss" image. It should be noted that in an alternate embodiment, selection of the first task 820 automatically initiates changing of the desktop background image to the standard "Bliss" image without further user interaction (emphasis added).”; paragraph [0153], ““[…] to allow the user to select the desired script from the predicted list of scripts, then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”),” but the combination of Ramsey, Givoly, and Vidal does not explicitly disclose:
wherein the first forecast includes, for each first sub-recipe, a corresponding first list of arguments, and wherein the second user input further includes at least one from among information that relates to at least one argument included in the first list of arguments and information that relates to a respective user edit of a name of at least one first sub-recipe.
However, Casselman discloses:
for each [script], a corresponding first list of arguments, and wherein the [input] further includes at least one from among information that relates to at least one argument included in the first list of arguments and information that relates to a respective user edit of a name of at least one first sub-recipe (col. 9 lines 60-62, “For each script 602, 604, an identifier (delimited by "id" tags), a list of input parameters [corresponding first list of arguments] (bounded by "params" tags), and the script itself ("bounded by "script" tags) is provided (emphasis added).”; col. 9 lines 3-5, “In some implementations, executeScript may perform type validation on one or more of the input parameters being passed to the script [information that relates to at least one argument included in the first list of arguments] 210 (emphasis added).”; col. 9 lines 42-50, “The script execution method 514 for the client device 202, called "executeScript", accepts at least two input parameters, in a fashion similar to that of the script execution method 504 for the server device 204. The first parameter ("jsld") is a character string identifying the particular script 210 within the XML file that is to be executed. The next one or more parameters are based on the input data 212 received from the user, and are employed by executeScript to return the result 214a to the application 206 (emphasis added).”) [Examiner’s Remarks: Note that Casselman discloses a list of input parameters for each script and a script execution method accepting at least two input parameters based on input data received from the user. One of ordinary skill in the art would readily comprehend that the input parameters, based on input data received from the user, passed to the script is user input that includes information relating to at least one parameter (argument) included in the list of parameters (arguments) for that script since each script has a list of input parameters.].
Casselman is within the same field of endeavor as the claimed invention regarding the transmission of script-related information.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Casselman into the combined teachings of Ramsey, Givoly, and Vidal to include “wherein the first forecast includes, for each first sub-recipe, a corresponding first list of arguments, and wherein the second user input further includes at least one from among information that relates to at least one argument included in the first list of arguments and information that relates to a respective user edit of a name of at least one first sub-recipe.” The modification would be obvious because one of ordinary skill in the art would be motivated to have a corresponding list of parameters for each script based on user input to ensure effective and proper execution of a script and provide the user input from a client device to a server to perform the same operations as a client device to validate results and “thus preventing any possible spoofing of results in the client while accelerating the presentation of the results to the user” (Casselman, col. 7 lines 55-60 & col. 12 lines 7-15).
Claim 12 is an apparatus claim corresponding to method Claim 3 and is rejected for the same reasons as given in the rejection of that claim.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsey in view of Givoly as applied to Claims 1 and 10 above, and further in view of US 2020/0193153 (hereinafter “Lee”).
As per Claim 4, the rejection of Claim 1 is incorporated; and Ramsey discloses “wherein when the information that relates to the first recipe includes a recipe description, the analyzing comprises: parsing the recipe description to determine a set of [tokens] (paragraph [0075], “The semantic analysis module then deconstructs [parses] the user input into a set of one or more "tokens" (see Section 4.2), and acts to identify one or more best or most probable matches between the user input and one or more of the scripts 505 as a function of the initial mappings 510 and the tokens (emphasis added).”; paragraph [0113], “Typically, semantic processing breaks [parses] the natural language input into strings of characters called tokens. The NLSI described herein provides a system that analyzes these tokens as well as the user "context" in which the natural language input was provided to determine the appropriate task or tasks. Scripts, ACWs or other code corresponding to the identified tasks are then either executed automatically, or presented to the user […] (emphasis added).”; paragraph [0121], “In order to divide the input into a token or set(s) of tokens, the semantic analysis of the user input is used to evaluate statements, such as, for example, "I want to find a flight from Boston to Seattle" [recipe description] and to break that statement into tokens. In this case, each token represents either one word (e.g., "I," "want," "to," "find," etc.) or a phrase ("I want to find," "a flight," etc.). In various embodiments, this semantic analysis is configured to recognize key words, terms, phrases, and the like, which can be referred to as named entity recognition (emphasis added).”),” “comparing the set of [tokens] to identify at least one related recipe (paragraph [0075], “The semantic analysis module then deconstructs the user input into a set of one or more "tokens" (see Section 4.2), and acts to identify one or more best or most probable matches between the user input and one or more of the scripts [related recipe] 505 as a function of the initial mappings 510 and the tokens (emphasis added).”; paragraph [0122], “Once the user input has been broken into tokens, a search is performed using those tokens to find or return the best tasks or scripts relative to the initial mappings extracted from the available scripts (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses a search being performed using the tokens to find the best scripts. One of ordinary skill in the art would readily comprehend that performing the search includes comparing the tokens to information relating to the scripts (recipes) in order to find the best scripts (related recipes).],” “using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective [rank] for each of the at least one second sub-recipe (paragraph [0113], “Typically, semantic processing breaks the natural language input into strings of characters called tokens. The NLSI described herein provides a system that analyzes these tokens as well as the user "context" in which the natural language input was provided to determine the appropriate task or tasks. Scripts, ACWs or other code corresponding to the identified tasks are then either executed automatically, or presented to the user for further input or selection […] (emphasis added).”; paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts [at least one related recipe] are found following semantic analysis of that natural language input, a determination is made as to which scripts [at least one second sub-recipe] to execute and/or present to the user (emphasis added).”; paragraph [0160], “Once the scripts have been identified, and any slots filled, if applicable or possible, the identified scripts are scored and ranked 725 in order of most probable to least probable matches to the users presumed intent, in order to construct [generate] a probable script list [second forecast]. The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses that if multiple potentially matching scripts to the user input are found a determination is made as to which scripts to present to the user. One of ordinary skill in the art would readily comprehend that the multiple potentially matching scripts found are related scripts to the user’s desired script (first recipe) described through the user input (natural language input). These related scripts are used to generate a list (second forecast) of closely matching scripts (second sub-recipes) to present to the user. Moreover, these related scripts are used to determine which scripts to present to the user for selection meaning these scripts (sub-recipes) are for possible inclusion in the desired script (first recipe) since the user selecting any of these scripts for execution means it closely or completely matches the desired script (first recipe) described in the user input.],” and “generating the list such that each of the at least one second sub-recipe and each respective [rank] is included in the list (paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts are found following semantic analysis of that natural language input, a determination is made as to which scripts [at least one second sub-recipe] to execute and/or present to the user (emphasis added).”; paragraph [0160], “Once the scripts have been identified, and any slots filled, if applicable or possible, the identified scripts are scored and ranked 725 in order of most probable to least probable matches to the users presumed intent, in order to construct [generate] a probable script list. The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”),” but the combination of Ramsey and Givoly does not explicitly disclose:
parsing the recipe description to determine a set of word embeddings and a set of sentence embeddings;
comparing the set of word embeddings and the set of sentence embeddings to identify at least one related recipe;
using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective similarity score for each of the at least one second sub-recipe; and
generating the list such that each of the at least one second sub-recipe and each respective similarity score is included in the list.
However, Lee discloses:
a set of word embeddings and a set of sentence embeddings (paragraph [0287], “The method of Example 2, wherein the plurality of input text segments and plurality of stored text segments that are compared by the text similarity machine learning model each comprise one or more word embeddings (emphasis added).”; paragraph [0126], “For example, a fixed length tensor encoding for a text segment may be created using techniques such as sentence embedding or document embedding. Sentence embeddings algorithms that may be used include Skip-Thoughts, Quick-Thoughts, DiscSent, InferSent, Universal Sentence, and other algorithms (emphasis added).”; paragraph [0127], “The fixed length encodings that are created from a text segment may be referred to as text segment embeddings. In some embodiments, the fixed length encodings are generated such that text segments with similar semantic meanings are mapped by the encoding process to encodings that are close together in tensor space. That is, when the encodings are graphed in multi-dimensional space, the points appear relatively closer to each other as compared to the encodings of unrelated sentences (emphasis added).”);
similarity score (Figure 9: 903; paragraph [0250], “In step 1044, for each input text segment, a list of stored text segments may be retrieved and compared with the input text segment such as by method 820 to generate similarity scores (emphasis added).”);
Lee is within the same field of endeavor as the claimed invention regarding the utilization of word embeddings and similarity scores to compare items.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Lee into the combined teachings of Ramsey and Givoly to include “parsing the recipe description to determine a set of word embeddings and a set of sentence embeddings; comparing the set of word embeddings and the set of sentence embeddings to identify at least one related recipe; using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective similarity score for each of the at least one second sub-recipe; and generating the list such that each of the at least one second sub-recipe and each respective similarity score is included in the list.” The modification would be obvious because one of ordinary skill in the art would be motivated to utilize similarity scores to rank items that are made up of word embeddings and eliminate items with “a similarity score below a threshold or below a specified rank to reduce memory and processing requirements” (Lee, paragraphs [0211 & 0287]).
Claim 13 is an apparatus claim corresponding to method Claim 4 and is rejected for the same reasons as given in the rejection of that claim.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsey in view of Givoly and Lee as applied to Claims 4 and 13 above, and further in view of Casselman.
As per Claim 5, the rejection of Claim 4 is incorporated; and Ramsey discloses “wherein the second forecast includes […] (paragraph [0160], “Once the scripts have been identified, and any slots filled, if applicable or possible, the identified scripts are scored and ranked 725 in order of most probable to least probable matches to the users presumed intent, in order to construct a probable script list [second forecast]. The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”),” “second sub-recipe (paragraph [0153], “[…] to allow the user to select the desired script from the predicted list of scripts [second sub-recipes], then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”; paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts are found following semantic analysis of that natural language input, a determination is made as to which scripts [second sub-recipes] to execute and/or present to the user (emphasis added).”),” and “second user input (paragraph [0160], “In various embodiments, the user interacts with one or more of the presented scripts via the user interface 735 to select, complete, and/or execute 740 one or more of the identified scripts, as described above (emphasis added).”; paragraph [0169], “As illustrated by FIG. 9, following user selection of the first task 820 [second user input], an Active Content Wizard (ACW) type script is activated to guide the user through changing the desktop background image to the standard "Bliss" image. It should be noted that in an alternate embodiment, selection of the first task 820 automatically initiates changing of the desktop background image to the standard "Bliss" image without further user interaction (emphasis added).”; paragraph [0153], ““[…] to allow the user to select the desired script from the predicted list of scripts, then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”),” but the combination of Ramsey, Givoly, and Lee does not explicitly disclose:
wherein the second forecast includes, for each second sub-recipe, a corresponding second list of arguments, and wherein the second user input further includes at least one from among information that relates to at least one argument included in the second list of arguments and information that relates to a respective user edit of a name of at least one second sub-recipe.
However, Casselman discloses:
for each [script], a corresponding second list of arguments, and wherein the [input] further includes at least one from among information that relates to at least one argument included in the second list of arguments and information that relates to a respective user edit of a name of at least one second sub-recipe (col. 9 lines 60-62, “For each script 602, 604, an identifier (delimited by "id" tags), a list of input parameters [corresponding second list of arguments] (bounded by "params" tags), and the script itself ("bounded by "script" tags) is provided (emphasis added).”; col. 9 lines 3-5, “In some implementations, executeScript may perform type validation on one or more of the input parameters being passed to the script [information that relates to at least one argument included in the second list of arguments] 210 (emphasis added).”; col. 9 lines 42-50, “The script execution method 514 for the client device 202, called "executeScript", accepts at least two input parameters, in a fashion similar to that of the script execution method 504 for the server device 204. The first parameter ("jsld") is a character string identifying the particular script 210 within the XML file that is to be executed. The next one or more parameters are based on the input data 212 received from the user, and are employed by executeScript to return the result 214a to the application 206 (emphasis added).”) [Examiner’s Remarks: Note that Casselman discloses a list of input parameters for each script and a script execution method accepting at least two input parameters based on input data received from the user. One of ordinary skill in the art would readily comprehend that the input parameters, based on input data received from the user, passed to the script is user input that includes information relating to at least one parameter (argument) included in the list of parameters (arguments) for that script since each script has a list of input parameters.].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Casselman into the combined teachings of Ramsey, Givoly, and Lee to include “wherein the second forecast includes, for each second sub-recipe, a corresponding second list of arguments, and wherein the second user input further includes at least one from among information that relates to at least one argument included in the second list of arguments and information that relates to a respective user edit of a name of at least one second sub-recipe.” The modification would be obvious because one of ordinary skill in the art would be motivated to have a corresponding list of parameters for each script based on user input to ensure effective and proper execution of a script and provide the user input from a client device to a server to perform the same operations as a client device to validate results and “thus preventing any possible spoofing of results in the client while accelerating the presentation of the results to the user” (Casselman, col. 7 lines 55-60 & col. 12 lines 7-15).
Claim 14 is an apparatus claim corresponding to method Claim 5 and is rejected for the same reasons as given in the rejection of that claim.
Claims 6, 7, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsey in view of Givoly as applied to Claims 1 and 10 above, and further in view of US 2023/0080439 (hereinafter “Saha”).
As per Claim 6, the rejection of Claim 1 is incorporated; and Ramsey discloses “wherein the analyzing is performed by applying a [NLSI] (paragraph [0113], “Typically, semantic processing breaks the natural language input into strings of characters called tokens. The NLSI described herein provides a system that analyzes these tokens as well as the user "context" in which the natural language input was provided to determine the appropriate task or tasks. Scripts, ACWs or other code corresponding to the identified tasks are then either executed automatically, or presented to the user for further input or selection, as described in further detail below (emphasis added).”; paragraph [0079], “Consequently, an evaluation, such as a conventional data mining or search of the available pool of scripts allows the NLSI to construct a searchable database, or the like, which includes search criteria such as keywords, task names, variable types, etc (emphasis added).”),” and “data set that contains historical information that relates to previously-used recipes (paragraph [0079], “In general, as described below, this initial mapping is achieved by evaluating the Task Interface associated with each ACW, or other script to identify possible mappings for each script. As discussed below, the Task Interface of each script provides various metadata and slot or variable information to describe an action or actions to be performed by the script. Consequently, an evaluation, such as a conventional data mining or search of the available pool of scripts allows the NLSI to construct a searchable database, or the like, which includes search criteria such as keywords, task names, variable types, etc (emphasis added).”; paragraph [0082], “The Task Interface generally defines each task (e.g., the script or other code to be executed), the associated data, and the manner in which task data is to be interpreted. Identification of the Task Interface by evaluation of each script allows the NLSI to provide initial mapping criteria that are stored in a table or database to which may then be searched as a function of a natural language input. This allows the NLSI to return one or more scripts to the user for user selection, or, depending upon the search results, automatically execute one or more scripts automatically without further user interaction (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses searching an available pool of scripts to construct a database that includes metadata related to the scripts and returning scripts for user selection and/or execution. One of ordinary skill in the art would readily comprehend that since the returned scripts come from the pool of available scripts and these scripts may be executed then the available pool of scripts contain previously-used scripts (recipes). Moreover, the database that contains the metadata of available pool of scripts must also contain the metadata (historical information) of the previously-used scripts (recipes).],” but the combination of Ramsey and Givoly does not explicitly disclose:
wherein the analyzing is performed by applying an artificial intelligence (AI) algorithm that is trained by using a data set that contains historical information that relates to previously-used recipes.
However, Saha discloses:
an artificial intelligence (AI) algorithm that is trained by using a data set (paragraph [0041], “The electronic device 102 may help to improve a training of the meta-learning model 102A based on the augmentation of the ML corpus database using good quality ML pipelines, which may perform well on the given dataset. The meta-learning model 102A may generate an abstract version of an ML pipeline (emphasis added).”; paragraph [0067], “Herein, the training dataset may be used to train the first ML model based on an update of weights associated with the first ML model. The test dataset may be used to test the trained first ML model to check whether an accuracy of the trained first ML model is within allowable limits (emphasis added).”).
Saha is within the same field of endeavor as the claimed invention regarding the use of AI trained on a database and augmenting data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Saha into the combined teachings of Ramsey and Givoly to include “wherein the analyzing is performed by applying an artificial intelligence (AI) algorithm that is trained by using a data set that contains historical information that relates to previously-used recipes.” The modification would be obvious because one of ordinary skill in the art would be motivated to train and update a machine learning model on an augmented database in order to “improve the quality” of the database and to efficiently and optimally “synthesize new higher-quality ML pipelines for user datasets” (Saha, paragraphs [0024 & 0040]).
As per Claim 7, the rejection of Claim 6 is incorporated; and Ramsey discloses “selected sub-recipes (paragraph [0160], “In various embodiments, the user interacts with one or more of the presented scripts via the user interface 735 to select, complete, and/or execute 740 one or more of the identified scripts, as described above (emphasis added).”; paragraph [0153], “[…] to allow the user to select the desired script from the predicted list of scripts, then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”)” and “the historical information that relates to the previously-used recipes (paragraph [0079], “In general, as described below, this initial mapping is achieved by evaluating the Task Interface associated with each ACW, or other script to identify possible mappings for each script. As discussed below, the Task Interface of each script provides various metadata and slot or variable information to describe an action or actions to be performed by the script. Consequently, an evaluation, such as a conventional data mining or search of the available pool of scripts allows the NLSI to construct a searchable database, or the like, which includes search criteria such as keywords, task names, variable types, etc (emphasis added).”; paragraph [0082], “The Task Interface generally defines each task (e.g., the script or other code to be executed), the associated data, and the manner in which task data is to be interpreted. Identification of the Task Interface by evaluation of each script allows the NLSI to provide initial mapping criteria that are stored in a table or database to which may then be searched as a function of a natural language input. This allows the NLSI to return one or more scripts to the user for user selection, or, depending upon the search results, automatically execute one or more scripts automatically without further user interaction (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses searching an available pool of scripts to construct a database that includes metadata related to the scripts and returning scripts for user selection and/or execution. One of ordinary skill in the art would readily comprehend that since the returned scripts come from the pool of available scripts and these scripts may be executed then the available pool of scripts contain previously-used scripts (recipes). Moreover, the database that contains the metadata of available pool of scripts must also contain the metadata (historical information) of the previously-used scripts (recipes).],” but the combination of Ramsey and Givoly does not explicitly disclose:
augmenting the data set by adding the set of selected sub-recipes to the historical information that relates to the previously-used recipes; and
updating a training of the AI algorithm by using the augmented data set.
However, Saha discloses:
augmenting the data set by adding the set of [ML pipelines] to the [data set] (abstract, “The operations may further include augmenting the ML corpus database to include the selected one or more ML pipelines and the set of first ML pipeline (emphasis added).”); and
updating a training of the AI algorithm by using the augmented data set (paragraph [0041], “The electronic device 102 may help to improve [update] a training of the meta-learning model 102A based on the augmentation of the ML corpus database using good quality ML pipelines, which may perform well on the given dataset (emphasis added).”; paragraph [0079], “Similarly, each of the first ML pipeline associated with the each of the plurality of ML projects, such as the ML project-1 114, the ML project-2 116, and the ML project-n 118 may be mutated and their corresponding selected one or more ML pipelines may be added to augment the database 108. With reference to FIG. 5, after augmentation the set of ML pipelines 114B corresponding to the ML project-1 114 may be changed [updated] to a set of ML pipelines 524 […] The augmentation of the database 108 may be then used to provide higher-quality and more consistent learning pipelines/features to the meta-learning model 102A to learn from the corpus and subsequently synthesize new higher-quality ML pipelines for other datasets (emphasis added).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Saha into the combined teachings of Ramsey and Givoly to include “augmenting the data set by adding the set of selected sub-recipes to the historical information that relates to the previously-used recipes; and updating a training of the AI algorithm by using the augmented data set.” The modification would be obvious because one of ordinary skill in the art would be motivated to train and update a machine learning model on an augmented database in order to “improve the quality” of the database and to efficiently and optimally “synthesize new higher-quality ML pipelines for user datasets” (Saha, paragraphs [0024 & 0040]).
Claim 15 is an apparatus claim corresponding to method Claim 6 and is rejected for the same reasons as given in the rejection of that claim.
Claim 16 is an apparatus claim corresponding to method Claim 7 and is rejected for the same reasons as given in the rejection of that claim.
Claims 8, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsey in view of Givoly as applied to Claims 1, 10, and 19 above, and further in view of Vidal and Lee.
As per Claim 8, the rejection of Claim 1 is incorporated; and Ramsey discloses “information that relates to the first recipe (paragraph [0084], “In general, Keywords are terms that might be used to identify a particular script [recipe]. These are words that would be entered as part of a user query, provided in the form of a natural language input, as described in further detail below (emphasis added).”; paragraph [0153], “[…] to allow the user to select the desired script from the predicted list of scripts […] (emphasis added).”),” “a recipe name (paragraph [0083], “Scripts also include a unique identifier, such as a name or other identifier, for allowing selection of individual scripts once those scripts have been identified as possible or probable matches to the user's query. For example, a script for changing the refresh rate of a display device might be identified as "ChangeRefreshRate" or simply as "Script123." (emphasis added).”),” “a recipe description (paragraph [0163], “In this case, the user enters the natural language input "set background image bliss." [recipe description] The NLSI evaluates this natural language input 810 and returns a list of four potentially matching tasks/scripts (emphasis added).”),” “wherein the analyzing comprises: using a probability distribution that relates to at least one prior usage of each of the [information relating to a query] to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe (paragraph [0118], “For example, as noted above, semantic mapping of the user's natural language input is provided with respect to the initial mappings of the available scripts. In providing this mapping, a "user" first inputs a natural language query. This natural language query is analyzed in order to search for and determine appropriate task/script cross-mapping, and to probabilistically rank the cross-mapping results in order of likelihood (emphasis added).”; paragraph [0119], “To perform these semantic analysis and cross-mapping actions, the NLSI first receives the user's natural language input (e.g., statement, query, request, and the like) through a user interface. This input is then broken or divided into a "token" or set(s) of tokens, with each tokens representing a string of characters […] Further, there are various ranking systems that can be utilized by these techniques. For example, an information retrieval system can utilize an Okapi-based ranking system, a query classifier (query to script mapping) can utilize a Naive-Bayesian model [uses a probability distribution] […] (emphasis added).”; paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts are found following semantic analysis of that natural language input, a determination is made as to which scripts to execute and/or present to the user. In one embodiment, two sub-scores are generated, one for the information retrieval portion and one for the query classifier portion of the semantic analysis-based script matching. Next, the structure of the script (i.e., the above-described "Task Interface") is reviewed relative to the semantic analysis, and a ranking is performed on the script structure, producing yet another sub-score. Each of these sub-scores is then combined to determine a final score based on each of the sub-scores. In one embodiment, this final score, or ranking, is then used to provide feedback to improve performance of future queries for the current and/or future users (emphasis added).”; paragraph [0160], “Once the scripts have been identified, and any slots filled, if applicable or possible, the identified scripts are scored and ranked 725 in order of most probable to least probable matches to the users presumed intent, in order to construct [generate] a probable script list [first forecast]. The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”; paragraph [0153], “[…] to allow the user to select the desired script from the predicted list of scripts, then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses generating a probable script list of potentially matching scripts to the user query. One of ordinary skill in the art would readily comprehend that presenting the probable script list (first forecast) of potentially matching scripts (sub-recipes) to the user for selection that matches the desired script (first recipe) described in the natural language query means the scripts presented are for possible inclusion in the desired script, in other words, the user selected scripts are the desired script. Ramsey discloses probabilistically ranking appropriate scripts (sub-recipes) that potentially match the natural language query and a query classifier utilizing a Naive-Bayesian model in a ranking system. Ramsey also discloses that ranking is a final score that was calculated using a sub-score generated for the query classifier (probability score) and that the rank is used to provide feedback in improving the performance of future queries. One of ordinary skill in the art would readily comprehend that using a Naive-Bayesian model in a ranking system to generate and present a ranked list of scripts includes using a probability distribution in generating the list of potentially matching scripts in a ranked order where the rank was computed using a sub-score for the query classifier (probability score). Moreover, one of ordinary skill in the art would readily comprehend that the ranking being used to improve performance for future queries means that the probability distribution used may relate to at least one prior usage of each of the information relating to a query for improved performance.],” “parsing the recipe description to determine a set of [tokens] (paragraph [0075], “The semantic analysis module then deconstructs [parses] the user input into a set of one or more "tokens" (see Section 4.2), and acts to identify one or more best or most probable matches between the user input and one or more of the scripts 505 as a function of the initial mappings 510 and the tokens (emphasis added).”; paragraph [0113], “Typically, semantic processing breaks [parses] the natural language input into strings of characters called tokens. The NLSI described herein provides a system that analyzes these tokens as well as the user "context" in which the natural language input was provided to determine the appropriate task or tasks. Scripts, ACWs or other code corresponding to the identified tasks are then either executed automatically, or presented to the user […] (emphasis added).”; paragraph [0121], “In order to divide the input into a token or set(s) of tokens, the semantic analysis of the user input is used to evaluate statements, such as, for example, "I want to find a flight from Boston to Seattle" [recipe description] and to break that statement into tokens. In this case, each token represents either one word (e.g., "I," "want," "to," "find," etc.) or a phrase ("I want to find," "a flight," etc.). In various embodiments, this semantic analysis is configured to recognize key words, terms, phrases, and the like, which can be referred to as named entity recognition (emphasis added).”),” “comparing the set of [tokens] to identify at least one related recipe (paragraph [0075], “The semantic analysis module then deconstructs the user input into a set of one or more "tokens" (see Section 4.2), and acts to identify one or more best or most probable matches between the user input and one or more of the scripts [related recipe] 505 as a function of the initial mappings 510 and the tokens (emphasis added).”; paragraph [0122], “Once the user input has been broken into tokens, a search is performed using those tokens to find or return the best tasks or scripts relative to the initial mappings extracted from the available scripts (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses a search being performed using the tokens to find the best scripts. One of ordinary skill in the art would readily comprehend that performing the search includes comparing the tokens to information relating to the scripts (recipes) in order to find the best scripts (related recipes).],” “using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective [rank] for each of the at least one second sub-recipe (paragraph [0113], “Typically, semantic processing breaks the natural language input into strings of characters called tokens. The NLSI described herein provides a system that analyzes these tokens as well as the user "context" in which the natural language input was provided to determine the appropriate task or tasks. Scripts, ACWs or other code corresponding to the identified tasks are then either executed automatically, or presented to the user for further input or selection […] (emphasis added).”; paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts [at least one related recipe] are found following semantic analysis of that natural language input, a determination is made as to which scripts [at least one second sub-recipe] to execute and/or present to the user (emphasis added).”; paragraph [0160], “Once the scripts have been identified, and any slots filled, if applicable or possible, the identified scripts are scored and ranked 725 in order of most probable to least probable matches to the users presumed intent, in order to construct [generate] a probable script list [second forecast]. The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses that if multiple potentially matching scripts to the user input are found a determination is made as to which scripts to present to the user. One of ordinary skill in the art would readily comprehend that the multiple potentially matching scripts found are related scripts to the user’s desired script (first recipe) described through the user input (natural language input). These related scripts are used to generate a list (second forecast) of closely matching scripts (second sub-recipes) to present to the user. Moreover, these related scripts are used to determine which scripts to present to the user for selection meaning these scripts (sub-recipes) are for possible inclusion in the desired script (first recipe) since the user selecting any of these scripts for execution means it closely or completely matches the desired script (first recipe) described in the user input.],” and “generating the list such that each of the at least one first sub-recipe, each respective probability score, each of the at least one second sub-recipe, and each respective [rank] is included in the list (paragraph [0118], “For example, as noted above, semantic mapping of the user's natural language input is provided with respect to the initial mappings of the available scripts. In providing this mapping, a "user" first inputs a natural language query. This natural language query is analyzed in order to search for and determine appropriate task/script cross-mapping, and to probabilistically rank the cross-mapping results in order of likelihood (emphasis added).”; paragraph [0119], “To perform these semantic analysis and cross-mapping actions, the NLSI first receives the user's natural language input (e.g., statement, query, request, and the like) through a user interface. This input is then broken or divided into a "token" or set(s) of tokens, with each tokens representing a string of characters […] Further, there are various ranking systems that can be utilized by these techniques. For example, an information retrieval system can utilize an Okapi-based ranking system, a query classifier (query to script mapping) can utilize a Naive-Bayesian model [uses a probability distribution] […] (emphasis added).”; paragraph [0130], “For example, in one embodiment, if a user query is input into the NLSI and multiple potentially matching scripts are found following semantic analysis of that natural language input, a determination is made as to which scripts to execute and/or present to the user. In one embodiment, two sub-scores are generated, one for the information retrieval portion and one for the query classifier portion of the semantic analysis-based script matching […] Each of these sub-scores is then combined to determine a final score based on each of the sub-scores. In one embodiment, this final score, or ranking, is then used to provide feedback to improve performance of future queries for the current and/or future users (emphasis added).”; paragraph [0160], “Once the scripts have been identified, and any slots filled, if applicable or possible, the identified scripts [first and second sub-recipes] are scored and ranked 725 in order of most probable to least probable matches to the users presumed intent, in order to construct [generate] a probable script list. The probable script list is then presented 730 to the user via a user interface 735 (emphasis added).”) [Examiner’s Remarks: Note that Ramsey discloses probabilistically ranking appropriate scripts (sub-recipes) that potentially match the natural language query and a query classifier utilizing a Naive-Bayesian model (uses a probability distribution) in a ranking system. Ramsey also discloses that ranking is a final score that was calculated using a sub-score generated for the query classifier (probability score). One of ordinary skill in the art would readily comprehend that generating the probable script list in ranked order includes a respective probability score for each script (sub-recipe) because the order of each script corresponds to its probabilistic rank which was computed using a sub-score for the query classifier (probability score). As a result, both a probability score and rank are included in generating the list of scripts (first and second sub-recipes).],” but the combination of Ramsey and Givoly does not explicitly disclose:
wherein the information that relates to the first recipe includes both a recipe name and a recipe description, and wherein the analyzing comprises:
parsing the recipe name to determine a library, a package, a tool, and an action name;
using a probability distribution that relates to at least one prior usage of each of the library, the package, the tool, and the action name to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe;
parsing the recipe description to determine a set of word embeddings and a set of sentence embeddings;
comparing the set of word embeddings and the set of sentence embeddings to identify at least one related recipe;
using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective similarity score for each of the at least one second sub-recipe; and
generating the list such that each of the at least one first sub-recipe, each respective probability score, each of the at least one second sub-recipe, and each respective similarity score is included in the list.
However, Vidal discloses:
wherein when the information that relates to the [action] includes both [metadata] and [other information] (paragraph [0036], “The tagging tool 116 can be configured to parse the metadata and the other information associated with the action in order to extract any information that describes the action, the reasons the action was performed, and any software programs, files, and software libraries affected by the action (emphasis added).”)
parsing the [metadata] to determine a library, a package, a tool, and an action name (paragraph [0036], “The tagging tool 116 can be configured to parse the metadata and the other information associated with the action in order to extract any information that describes the action, the reasons the action was performed, and any software programs, files, and software libraries affected by the action. For example, the tagging tool 116 can parse and extract information such as the name of the software package 108, the version of the software package 108, the version of the previous software package 108 if updating, the reason the package manager 110 is performing the action (e.g. new software installation, software installation update) [action name], date of the action, and a list of software programs [tool], files, and software libraries affected by the action (emphasis added).”).
the library, the package, the tool, and the action name (Figure 4A; paragraph [0036], “For example, the tagging tool 116 can parse and extract information such as the name of the software package 108, the version of the software package 108, the version of the previous software package 108 if updating, the reason the package manager 110 is performing the action (e.g. new software installation, software installation update) [action name], date of the action, and a list of software programs [tool], files, and software libraries affected by the action (emphasis added).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Vidal into the combined teachings of Ramsey and Givoly to include “wherein the information that relates to the first recipe includes both a recipe name and a recipe description, and wherein the analyzing comprises: parsing the recipe name to determine a library, a package, a tool, and an action name; using a probability distribution that relates to at least one prior usage of each of the library, the package, the tool, and the action name to generate a first forecast that relates to at least one first sub-recipe for possible inclusion in the first recipe, together with a respective probability score for each of the at least one first sub-recipe.” The modification would be obvious because one of ordinary skill in the art would be motivated to use a tagging tool, that determines a library, a package, a tool, and an action name affected by an action, to track the history of actions performed by a package manager/system and the effects of additional actions, so “the user of the computing system can efficiently and reliably modify the computing system with assurances that the modification will not damage existing software of the computing system” (Vidal, paragraphs [0017 & 0036]).
However, Lee discloses:
a set of word embeddings and a set of sentence embeddings (paragraph [0287], “The method of Example 2, wherein the plurality of input text segments and plurality of stored text segments that are compared by the text similarity machine learning model each comprise one or more word embeddings (emphasis added).”; paragraph [0126], “For example, a fixed length tensor encoding for a text segment may be created using techniques such as sentence embedding or document embedding. Sentence embeddings algorithms that may be used include Skip-Thoughts, Quick-Thoughts, DiscSent, InferSent, Universal Sentence, and other algorithms (emphasis added).”; paragraph [0127], “The fixed length encodings that are created from a text segment may be referred to as text segment embeddings. In some embodiments, the fixed length encodings are generated such that text segments with similar semantic meanings are mapped by the encoding process to encodings that are close together in tensor space. That is, when the encodings are graphed in multi-dimensional space, the points appear relatively closer to each other as compared to the encodings of unrelated sentences (emphasis added).”);
similarity score (Figure 9: 903; paragraph [0250], “In step 1044, for each input text segment, a list of stored text segments may be retrieved and compared with the input text segment such as by method 820 to generate similarity scores (emphasis added).”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Lee into the combined teachings of Ramsey, Givoly, and Vidal to include “parsing the recipe description to determine a set of word embeddings and a set of sentence embeddings; comparing the set of word embeddings and the set of sentence embeddings to identify at least one related recipe; using each of the at least one related recipe to generate a second forecast that relates to at least one second sub-recipe for possible inclusion in the first recipe, together with a respective similarity score for each of the at least one second sub-recipe; and generating the list such that each of the at least one first sub-recipe, each respective probability score, each of the at least one second sub-recipe, and each respective similarity score is included in the list.” The modification would be obvious because one of ordinary skill in the art would be motivated to utilize similarity scores to rank items that are made up of word embeddings and eliminate items with “a similarity score below a threshold or below a specified rank to reduce memory and processing requirements” (Lee, paragraphs [0211 & 0287]).
Claim 17 is an apparatus claim corresponding to method Claim 8 and is rejected for the same reasons as given in the rejection of that claim.
Claim 20 is a non-transitory computer readable storage medium claim corresponding to method Claim 8 and is rejected for the same reasons as given in the rejection of that claim.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsey in view of Givoly, Vidal, and Lee as applied to Claims 8 and 17 above, and further in view of US 2014/0019936 (hereinafter “Cohanoff”) and US 11,947,905 (hereinafter “Agee”).
As per Claim 9, the rejection of Claim 8 is incorporated; and the combination of Ramsey, Givoly, Vidal, and Lee discloses “displaying, on a display (Ramsey, paragraph [0105], “Further, the interface module 610 provides various types of user interfaces for receiving the user input. For example, a graphical user interface (GUI) allows users to express their intentions by directly manipulating the graphical and/or textual objects displayed on display device (emphasis added).”), a user interface that includes a second field that prompts the user to provide the recipe description (Ramsey, paragraph [0106], “For example, in one embodiment, a GUI is rendered to provide the user with a region [second field] or means to load, import, input, etc. a query [recipe description], statement, request, etc. and can include a region to present the results of such query, etc (emphasis added).”; paragraph [0163], “In particular, in this example, as illustrated by FIG. 8, a GUI type user interface window 800 is presented to the user for entry of a natural language input 810. In this case, the user enters the natural language input "set background image bliss." [recipe description] The NLSI evaluates this natural language input 810 and returns a list of four potentially matching tasks/scripts (820 through 850) […] (emphasis added).”), a first clickable button that facilitates a user submission of the first user input (Ramsey, paragraph [0107], “The user can also interact with the interface module 610 to select and provide information through various devices such as a mouse, a roller ball, a keypad, a keyboard, a pen and/or voice activation, for example. Typically, a mechanism such as a push button [first clickable button] or the enter key on the keyboard can be employed subsequent entering the information in order to initiate the search [facilitate user submission of the first user input] (emphasis added).”; paragraph [0163], “In particular, in this example, as illustrated by FIG. 8, a GUI type user interface window 800 is presented to the user for entry of a natural language input 810. In this case, the user enters the natural language input "set background image bliss." The NLSI evaluates this natural language input 810 and returns a list of four potentially matching tasks/scripts (820 through 850) […] (emphasis added).”), a third field that shows the list of sub-recipes for possible inclusion in the first recipe (Ramsey, paragraph [0106], “For example, in one embodiment, a GUI is rendered to provide the user with a region or means to load, import, input, etc. a query, statement, request, etc. and can include a region [third field] to present the results of such query, etc (emphasis added).”; paragraph [0163], “In particular, in this example, as illustrated by FIG. 8, a GUI type user interface window 800 is presented to the user for entry of a natural language input 810. In this case, the user enters the natural language input "set background image bliss." The NLSI evaluates this natural language input 810 and returns a list of four potentially matching tasks/scripts (820 through 850) [sub-recipes for possible inclusion in the first recipe] […] (emphasis added).”), and a second clickable button that facilitates a user submission (Givoly, col. 38 lines 35-37 & lines 43-47, “In operation, the interface 2000 may be utilized to prompt a user for feedback associated with a variety of automated operations […] Furthermore, in various embodiments, the user may have the ability to respond to the prompt by entering text, selecting a one click button [second clickable button], selecting from a list (e.g. a drop down list, etc.), using an audible input, and/other any other response technique (emphasis added).”) of the second user input (Ramsey, paragraph [0160], “In various embodiments, the user interacts with one or more of the presented scripts via the user interface 735 to select, complete, and/or execute 740 one or more of the identified scripts, as described above (emphasis added).”; paragraph [0169], “As illustrated by FIG. 9, following user selection of the first task 820 [second user input], an Active Content Wizard (ACW) type script is activated to guide the user through changing the desktop background image to the standard "Bliss" image. It should be noted that in an alternate embodiment, selection of the first task 820 automatically initiates changing of the desktop background image to the standard "Bliss" image without further user interaction (emphasis added).”; paragraph [0153], “[…] to allow the user to select the desired script from the predicted list of scripts, then to present the user with one or more of the possible variations of that script resulting from the various "Predicted Slot Fill Solutions" for the selected script (emphasis added).”),” but does not explicitly disclose:
a first field that prompts the user to provide the recipe name, a fourth field that shows a recipe template that is editable by the user.
However, Cohanoff discloses:
a first field that prompts the user to provide the recipe name (Figure 2D: 203; paragraph [0016], “FIGS. 2C-2H illustrates a graphical user interface receiving user input that identifies a scripting language, a name of a script to be generated, and the first software to be used in the script, in some embodiments of the invention (emphasis added).”).
Cohanoff is within the same field of endeavor as the claimed invention regarding script generation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cohanoff into the combined teachings of Ramsey, Givoly, Vidal, and Lee to include “a first field that prompts the user to provide the recipe name.” The modification would be obvious because one of ordinary skill in the art would be motivated to prompt a user for a script name to allow for effective identification of a desired script to be generated/modified and using an integrated tool such as a GUI to prompt the user aids in making business analysts “more productive” (Cohanoff, paragraphs [0001 & 0016]).
However, Agee discloses:
a fourth field that shows a recipe template that is editable by the user (Figure 2A: 202; col. 5 lines 26-28, “User 102 may also manually edit template text field 202 [fourth field], thereby altering candidate script template 420 (emphasis added).”).
Agee is within the same field of endeavor as the claimed invention regarding the use of an editable script template.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Agee into the combined teachings of Ramsey, Givoly, Vidal, Lee, and Cohanoff to include “a fourth field that shows a recipe template that is editable by the user.” The modification would be obvious because one of ordinary skill in the art would be motivated to utilize a script template builder that includes a script template field editable by a user and generates/executes a script preview to “facilitate the rapid development, validation, and deployment of templates that previously were difficult to create and required specialized expertise” (Agee, abstract & col. 2 lines 43-48).
Claim 18 is an apparatus claim corresponding to method Claim 9 and is rejected for the same reasons as given in the rejection of that claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2012/0079395 (hereinafter “Bengualid”) discloses identifying a related script, and returning a set of scripts with the highest similarity scores, and prompting a user to select a script from the set.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FEVEN H HURUY whose telephone number is (571) 272-3826. The examiner can normally be reached Mon-Fri. 7:30am-3:45pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wei Mui can be reached at (571) 272-3708. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/F.H.H./Examiner, Art Unit 2191
/WEI Y MUI/Supervisory Patent Examiner, Art Unit 2191