DETAILED ACTION
This Office Action is in response to the correspondence filed by the applicant on 10/10/2024.
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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The Information Statements (IDS) filed on 10/10/2024 and 04/02/2025 have been accepted and considered in this office action and are in compliance with the provisions of 37 CFR 1.97.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-20 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of co-pending Application No. 18/911,882 and in further view of HEAD (US 2019/0057187 A1). Although the claims, at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are rejected as being unpatentable over the claims of the co-pending application in further view of LAMANNA. Please see below for the mapping in the table, where the bolded limitations indicate the corresponding limitations between the co-pending application and instant application.
Instant application 18/911,903
Co-pending application: 18/911,882
1. A method of language generation, the method comprising:
receiving, by a server and from a user device, a plurality of user preferences for natural-language outputs generated by a machine-learning language model based on user-provided natural-language text inputs, the plurality of user preferences provided by a user of the user device;
receiving, by a server and from the user device, a natural-language text prompt provided by the user to a chat application operating on the user device;
receiving a plurality of operator preferences for the natural-language outputs, the plurality of operator preferences determined by an operator of the server;
[generating, by the server, set of reconciled preferences according to a rules engine, the set of reconciled preferences including fewer than all of the plurality of operator preferences and the plurality of user preferences;]
modifying, by the server, a system prompt for the machine-learning language model based on the set of reconciled preferences to generate a modified system prompt;
providing, by the server, the modified system prompt as an initial input to the machine-learning language model;
providing, by the server and after providing the modified system prompt, the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output;
transmitting, by the server, the natural-language text output to the user device; and
communicating, by the chat application and via the user device, the natural-language text output to the user.
1. A method of automated pre-prompt generation, the method comprising:
receiving, by a user device, an indication of at least one user preference for a user, the at least one user preference indicative of at least one first characteristic, preferred by a user, of natural-language outputs generated by a machine-learning language model based on user-provided natural-language text inputs;
receiving, by a server and from the user device, a natural-language text prompt provided by the user to a chat application operating on the user device;
receiving, by the server and from the user device, the at least one user preference;
receiving at least one first operator preference indicative of at least one second characteristic, preferred by an operator of the server, of the natural-language outputs;
modifying, by the server, a system prompt for the machine-learning language model based on the received at least one user preference and the at least one first operator preference to generate a modified system prompt;
providing, by the server, the modified system prompt as an initial input to the machine-learning language model;
providing, by the server and after providing the modified system prompt, the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output;
transmitting, by the server, the natural-language text output to the user device; and
communicating, by the chat application via the user device, the natural-language text output to the user.
Co-pending Application is silent to the bracketed limitations: [generating, by the server, set of reconciled preferences according to a rules engine, the set of reconciled preferences including fewer than all of the plurality of operator preferences and the plurality of user preferences].
HEAD discloses a computer-implemented method comprising:
[generating, by the server, set of reconciled preferences according to a rules engine, the set of reconciled preferences including fewer than all of the plurality of operator preferences and the plurality of user preferences] (Par 36 – “For example, one set of rules provides that priority is given to one designated source of data, e.g., a doctor over the user 201, in the event of a conflict of data. As another example, another set of rules may average the different designated sources of a data in the event of a conflict of data (i.e., the setting provided by the doctor of the user 201 is averaged with the preferred setting of the user 201). For instance, an average brightness setting may be calculated based upon the doctor's recommended brightness setting and the preferred brightness setting of the user 201. The implementation of such rules allows the A/V device adjustment system 100 to improve the process of determining an A/V device setting for the user 201.”).
The co-pending application and HEAD pertain to the same art of analyzing preference data for rendering an output to a user. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the method/system of the co-pending application to include generating set of reconciled preferences, as taught by HEAD.
One of ordinary skill would have been motivated to include generating a set of reconciled preferences in order to provide an output to a user in a manner that satisfies both the user and a health wellness recommendation.
Other independent claim 7 is also similar to the independent claim 20 of the co-pending application. With respect to the dependent claims, each of the claims maps to a corresponding dependent claim of the co-pending application or are found within the scope of the independent claim.
Claims 1-20 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of co-pending Application No. 18/911,895 and in further view of HEAD (US 2019/0057187 A1). Although the claims, at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are rejected as being unpatentable over the claims of the co-pending application in further view of LAMANNA. Please see below for the mapping in the table, where the bolded limitations indicate the corresponding limitations between the co-pending application and instant application.
Instant application 18/911,903
Co-pending application: 18/911,895
1. A method of language generation, the method comprising:
receiving, by a server and from a user device, a plurality of user preferences for natural-language outputs generated by a machine-learning language model based on user-provided natural-language text inputs, the plurality of user preferences provided by a user of the user device;
receiving, by a server and from the user device, a natural-language text prompt provided by the user to a chat application operating on the user device;
receiving a plurality of operator preferences for the natural-language outputs, the plurality of operator preferences determined by an operator of the server;
[generating, by the server, set of reconciled preferences according to a rules engine, the set of reconciled preferences including fewer than all of the plurality of operator preferences and the plurality of user preferences;]
modifying, by the server, a system prompt for the machine-learning language model based on the set of reconciled preferences to generate a modified system prompt;
providing, by the server, the modified system prompt as an initial input to the machine-learning language model;
providing, by the server and after providing the modified system prompt, the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output;
transmitting, by the server, the natural-language text output to the user device; and
communicating, by the chat application and via the user device, the natural-language text output to the user.
1. A method of natural language generation, the method comprising:
receiving, by a user device, an indication of at least one user preference for a user, the at least one user preference indicative of at least one first characteristic, preferred by user, of natural-language outputs generated by a machine-learning language model based on natural-language text inputs;
receiving, by the server and from the user device, the at least one user preference;
modifying, by the server, a system prompt for the machine-learning language model based on the received at least one user preference to generate a modified system prompt;
providing, by the server, the modified system prompt as an initial input to the machine-learning language model;
receiving, by a server and from the user device, a natural-language text prompt and a user identifier for the user, the natural-language text prompt provided by the user to a chat application operating on the user device;
querying, by the server, a first database with the user identifier to retrieve first information;
generating, by the processor, a representation of the first information and the natural-language prompt;
querying, by the processor, a second database using the representation to retrieve second information;
generating a modified text prompt based on the natural-language prompt, the first information, and the second information;
providing, by the server and after providing the modified system prompt, the modified text prompt as an input to the machine-learning language model to generate a natural-language text output;
transmitting, by the server, the natural-language text output to the user device; and
causing, by the user device, the chat application to communicate the natural-language text output to the user.
Co-pending Application is silent to the bracketed limitations: [generating, by the server, set of reconciled preferences according to a rules engine, the set of reconciled preferences including fewer than all of the plurality of operator preferences and the plurality of user preferences].
HEAD discloses a computer-implemented method comprising:
[generating, by the server, set of reconciled preferences according to a rules engine, the set of reconciled preferences including fewer than all of the plurality of operator preferences and the plurality of user preferences] (Par 36 – “For example, one set of rules provides that priority is given to one designated source of data, e.g., a doctor over the user 201, in the event of a conflict of data. As another example, another set of rules may average the different designated sources of a data in the event of a conflict of data (i.e., the setting provided by the doctor of the user 201 is averaged with the preferred setting of the user 201). For instance, an average brightness setting may be calculated based upon the doctor's recommended brightness setting and the preferred brightness setting of the user 201. The implementation of such rules allows the A/V device adjustment system 100 to improve the process of determining an A/V device setting for the user 201.”).
The co-pending application and HEAD pertain to the same art of analyzing preference data for rendering an output to a user. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the method/system of the co-pending application to include generating set of reconciled preferences, as taught by HEAD.
One of ordinary skill would have been motivated to include generating a set of reconciled preferences in order to provide an output to a user in a manner that satisfies both the user and a health wellness recommendation.
Other independent claim 7 is also similar to the independent claim 20 of the co-pending application. With respect to the dependent claims, each of the claims maps to a corresponding dependent claim of the co-pending application or are found within the scope of the independent claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1 and 17 recite receiving, by a server and from a user device, a plurality of user preferences for natural-language outputs generated by a machine-learning language model based on user-provided natural-language text inputs, the plurality of user preferences provided by a user of the user device; receiving, by a server and from the user device, a natural-language text prompt provided by the user to a chat application operating on the user device; receiving a plurality of operator preferences for the natural-language outputs, the plurality of operator preferences determined by an operator of the server; generating, by the server, set of reconciled preferences according to a rules engine, the set of reconciled preferences including fewer than all of the plurality of operator preferences and the plurality of user preferences; modifying, by the server, a system prompt for the machine-learning language model based on the set of reconciled preferences to generate a modified system prompt; providing, by the server, the modified system prompt as an initial input to the machine-learning language model; providing, by the server and after providing the modified system prompt, the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output; transmitting, by the server, the natural-language text output to the user device; and communicating, by the chat application and via the user device, the natural-language text output to the user.
The recited limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a “server” and a “user device” nothing in the claim element precludes the step from practically being performed in the mind. For example, a person can receive a request from another person, the person can figure out the requesting person’s preference based on a previous experience, the person can also have his/her own preference in providing an answer to the request, the person can decides to how to provide the answer to the other person based on the requesting person’s preference and his/her own preference, and the person can further provides the answer based on the decision. The limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitations in the mind.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims only recite additional elements – “a server and a user device”. The additional elements in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the recited steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the recited steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding the dependent claims, claims 2 and 18 recite preference categories; claims 3 and 20 recite selecting user’s or operator’s preference; claim 4 recites selecting a user’s preference and an operator’s preference; claim 5 recites selecting user’s or operator’s preference for each category; claim 6 recites a membership, subscription, vendor, advertisement, data source category; claim 7 recites operator preferences in different categories; claim 8 recites user’s preferences in different categories; claim 9 recites selecting one of operator’s preference and user’s preference when they overlaps in a single category; claim 10 recites operator’s preferences in categories; claim 11 recites a user’s one-to-one correspondence; claim 12 recites a operator’s one-to-one correspondence; claim 13 recites a database with a user ID; claim 14 recites a database to retrieve the operator’s preferences; claim 15 recites using the user ID to access the database; claim 16 recites receiving preferences, storing the preferences, and transmitting data to be stored in a database; and claim 18 recites outputting to a user.
Even though the disclosed invention is described in the specification as improving computer technology, the claim provides no meaningful limitations such that this improvement is realized. Therefore, the claim does not amount to significantly more than the abstract idea itself.
Accordingly, the limitations of the Claims, whether considered individually or as an ordered combination, are not sufficient to add significantly more to improve technological functionality. As such, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
Claims 1-20 are would be allowable if rewritten to overcome the 101 and the double-patenting rejections.
Most pertinent prior art:
LUUS (US 2025/0173555 A1) discloses a method of language generation, the method comprising:
receiving, by a server and from a user device, a plurality of user preferences for natural-language outputs generated by a machine-learning language model based on user-provided natural-language text inputs (LUUS Par 57 – “All the above-discussed incident data 140, request prompts and responses 142, test results 144, result interpretations 146, and user preferred presentation format data 148 can be stored in an enterprise data storage 138.”), the plurality of user preferences provided by a user of the user device (LUUS Par 87 –“The user is provided with an opportunity to opt into the application services platform 110 being able to access the user data 136 a to enable the selected generative model 126 b to generate content according to user preferred style(s)/format(s). In some implementations, the first time that an application, such as the native application 114 or the browser application 112 presents the data analysis assistant to the user, the user is presented with a message that indicates that the user may opt into allowing the application services platform 110 to access user data included in the user database 136 to support the data analysis assistant functionality. The user may opt into allowing the application services platform 110 to access all or a subset of user data included in the user database 136. Furthermore, the user may modify their opt-in status at any time by accessing their user data and selectively opting into or opting out of allowing the application services platform 110 from accessing and utilizing user data from the user database 136 as a whole or individually.”; Par 107 – “The prompt construction unit 124 determines a preferred output format for the user based on user data associated with the user.”);
receiving, by a server and from the user device, a natural-language text prompt provided by the user to a chat application operating on the user device (LUUS Par 96 – “In one embodiment, for example, in step 502, the request processing module 122 can receive, via (e.g., a statistical analysis assistant application) a user interface of a client device (e.g., the client device 105) of a user, a first request (e.g., run the z-score test for last 4 weeks of data excluding load duration over 10 minutes) for first content (e.g., a statistical test result interpretation/summary) to be generated by a generative model (e.g., the general generative model 126 a, the selected generative model 126 b, etc.), the first request including a first prompt (e.g., “Can you run a Z-score test for last 4 weeks of data excluding load duration over 10 minutes?”) describing the first content to be generated, the first content being associated with one or more output values of a statistical test (e.g., a z-score test, a Mann-Kendall test, etc.).”);
receiving a plurality of operator preferences for the natural-language outputs, the plurality of operator preferences determined by an operator of the server (LUUS Par 111 – “A technical benefit of this approach is that the test result analysis presentation better reflects the presentation style/format preferred by the user than the generic or default presentation style/format generated by a generative model, such as the selected generative model 126 b.”);
generating, by the server, set of reconciled preferences according to a rules engine, the set of reconciled preferences including fewer than all of the plurality of operator preferences and the plurality of user preferences (LUUS Par 111 – “A technical benefit of this approach is that the test result analysis presentation better reflects the presentation style/format preferred by the user than the generic or default presentation style/format generated by a generative model, such as the selected generative model 126 b.”);
modifying, by the server, a system prompt for the machine-learning language model based on the set of reconciled preferences to generate a modified system prompt (LUUS Par 107 – “The prompt construction unit 124 determines a preferred output format for the user based on user data associated with the user. The second prompt can be constructed by appending the user data to the one or more prompts, the one or more responses, and the instruction string, and the instruction string can comprise instructions to the generative model to determine a preferred output format for the user based on the user data and to present the interpretation in the preferred output format as the first content.”);
providing, by the server, the modified system prompt [as an initial input] to the machine-learning language model (LUUS Par 107 – “The prompt construction unit 124 determines a preferred output format for the user based on user data associated with the user. The second prompt can be constructed by appending the user data to the one or more prompts, the one or more responses, and the instruction string, and the instruction string can comprise instructions to the generative model to determine a preferred output format for the user based on the user data and to present the interpretation in the preferred output format as the first content.”);
providing, by the server and [after providing the modified system prompt], the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output (LUUS Par 96 – “the first request including a first prompt (e.g., “Can you run a Z-score test for last 4 weeks of data excluding load duration over 10 minutes?”) describing the first content to be generated, the first content being associated with one or more output values of a statistical test (e.g., a z-score test, a Mann-Kendall test, etc.).”; Par 103 – “In step 512, the prompt construction unit 124 provides the one or more parameter values and the historical data to a calculation tool (e.g., the statistical analysis tool 132 a) to generate one or more output values associated with the statistical test. In one embodiment, the prompt construction unit 124 can select the calculation tool among a plurality of calculation tools based on the statistical test (e.g., the z-score test). Beside the nature of the data analysis and the specific calculations required, the appropriate calculation tool for the statistical test can also depend on factors such as open sources or commercial tools, software compatibility and integration, performance, data security and privacy, user familiarity/preferences, etc.”);
transmitting, by the server, the natural-language text output to the user device (LUUS Par 111 – “The selected generative model 126 b analyzes the test results, determines a preferred presentation style/format based on user data, and then causes the application of the user device to present the test result analysis/summary on the user interface based on the presentation style/format preferred by the user.”); and
communicating, by the chat application and via the user device, the natural-language text output to the user (LUUS Par 111 – “The selected generative model 126 b analyzes the test results, determines a preferred presentation style/format based on user data, and then causes the application of the user device to present the test result analysis/summary on the user interface based on the presentation style/format preferred by the user.”).
However, LUUS fails to teach all the recited limitations, particularly the [square-bracketed] limitations as indicated above. In other words, LUUS teaches constructing a second prompt (i.e., the modified system prompt) and providing the second prompt to a generative model to generate an output based on the presentation style/format preferred by the user, but LUUS does not explicitly teach after providing the second prompt to the generative model, then providing the first prompt (i.e., natural-language text prompt), as recited in the independent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C KIM whose telephone number is (571)272-3327. The examiner can normally be reached Monday to Friday 8:00 AM thru 4:00 PM EST.
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/JONATHAN C KIM/Primary Examiner, Art Unit 2655
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