Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 06/03/2026 has been entered. Claims 1, 8, and 9 have been amended. Claims 10-13 have been newly added. Claims 1-13 remain pending in the application.
Response to Arguments
Claim Rejections Under 35 U.S.C. 101
Rejections of claims 1-9 have been withdrawn in response to applicant's amendments.
Claim Rejections Under 35 U.S.C. 103
Regarding the newly amended independent claim 1, Applicant argues that
“First, Gaur does not teach the two-device architecture recited in the claims. The claims require receiving user setting information "from another information processing device having received user setting information that is information set by a user" via a predefined API. In Gaur, the user directly inputs a prompt into the system. (Gaur, paragraph [0032], "In step S 101, the user inputs a prompt"). There is no separate "other information processing device" that first receives user setting information from a user and then forwards it via an API to the claimed device. Second, Gaur does not teach vectorizing user setting information received from another device and comparing it against vector information of heading information of prompts. While Gaur mentions that "[t]he search may use vector search technology or other methods," (Gaur, paragraph [0033]), Gaur does not disclose: (a) vectorizing user setting information received via an API from another device, (b) comparing the vectorized user setting information against vector information of heading information of prompts stored in a prompt database, or (c) acquiring prompt candidates based on this vector similarity. The amended claims specifically require that the search unit "vectorizes the user setting information and acquires, as prompt candidates, prompts from a prompt database based on similarity of vectors between the vectorized user setting information and vector information of heading information of the prompts stored in the prompt database." Gaur's general mention of vector search technology does not teach this specific technical implementation. Third, Gaur does not teach providing the searched prompt back to another information processing device via the API. In Gaur, the combined prompt is used internally within the same system to generate ETL program code using an LLM. (Gaur, paragraph [0036]). The prompt is not provided to a separate external device via an API as required by the claims. Cheng does not cure these deficiencies. Cheng's normalized API is used for transforming prompts into vendor-specific requests to external LLM servers. (Cheng, paragraph [0126], "a data gateway . . . may transform the one or more prompts into a normalized API request"; paragraph [0127], "the data gateway may transmit the normalized API request to an external vendor server hosting one or more neural network based NLP models"). Cheng does not teach or suggest the claimed inter-device prompt provision architecture where user setting information is received from another device via a predefined API, prompts are searched from a prompt database using vector similarity with heading information, and the searched prompts are provided back to that device via the API. For at least these reasons, Applicant submits that the combination of Gaur and Cheng fails to teach or suggest the amended claims.
In response, Examiner relies on a new combination of references.
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 4 and 8-9 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2 and 6 of co-pending U.S. Application 18/945,315. Although the claims at issue are not identical, they are not patentably distinct from each other because they are obvious variants of each other. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
The chart below shows the correspondence between the claims in the current application and the claims in the patent.
Instant Application 18/945,372
Co-pending Application 18/945,315
1. An information processing device comprising:
a reception unit that receives, from another information processing device having received user setting information that is information set by a user,
a search unit that searches for a prompt to be input to generative AI that is used in the other information processing device, based on the user setting information received by the reception unit;
and a providing unit that provides, as a provided prompt, the prompt searched by the search unit to the other information processing device
6. The information processing device according to claim 5, comprising:
a reception unit that receives a search request from the user;
and a search unit that searches for, as the prompts, two or more prompts corresponding to the search request received by the reception unit,
wherein the providing unit provides, to the user, each of the prompts searched by the search unit in a selectable manner.
4. The information processing device according to claim 1,
comprising a billing unit that charges an administrator of the other information processing device for a providing fee of the provided prompt, when the provided prompt is provided by the providing unit.
2. The information processing device according to claim 1,
comprising a billing unit that performs billing processing for the user to whom the provided information is provided by the providing unit.
6. wherein the providing unit provides, to the user, each of the prompts searched by the search unit in a selectable manner.
Claims 8 and 9 correspond to claim 1, and are rejected accordingly.
Each patent claim in the above chart contains all the limitations recited in the corresponding claim of the current application. In other words, each patent claim is either 1) narrower than or 2) substantially equivalent to the corresponding claim of the instant application. It would have been obvious to a person of ordinary skill in the data processing art at the time the invention was made to omit elements when the remaining elements perform as before. A person of ordinary skill could have arrived at the present claims by omitting the details of the patent claims. See In re Karlson (CCPA) 136 USPQ 184, decided January 16, 1963 (“Omission of element and its function in combination is obvious expedient if remaining elements perform same functions as before.”).
Regarding claim 1, 18/945,315 ‘ discloses the features of claim 1 of the instant application as shown above,
However, ‘18/945,315’ does not recite “
“the user setting information via an API defined in advance; wherein the search unit vectorizes the user setting information and acquires, as prompt candidates, prompts from a prompt database based on similarity of vectors between the vectorized user setting information and vector information of heading information of the prompts stored in the prompt database; “
However, Cheng discloses:
the user setting information via an API defined in advance; via the API; (Cheng ,Fig. 12, item 1102 “Receive, via a communication interface, a natural language processing (NLP) task request comprising a user input from a user application” ,item 1206 “Transform, by a data gateway, the one or more prompts into a normalized API request”, item 1208 “Transmit, by the data gateway, the normalized API request to an external vendor server hosting one or more neural network based NLP models”, item 1210 “Translate the normalized API request to a vendor-specific request for generating a vendor-specific response by the one or more neural network based NLP models”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of ‘18/945,315 ‘ with the teaching of Cheng to utilize an interface with normalized APis as an alternative to custom integrations with internal and externally hosted foundational models and to directly interface with the generative services and/or LLM Gateway, (Cheng [0025]) and also to allow generative AI features to be compatible among different AI models with the generative AI platform 110, (Cheng, [0030]). In this way, the customized generative AI stack thus supports a full spectrum of domain-adaptive prompts to enable a full spectrum of personalized and adaptive AI chat applications, (Cheng, abstract).
However, ‘18/945,315’ in view of Cheng does not recite “
“wherein the search unit vectorizes the user setting information and acquires, as prompt candidates, prompts from a prompt database based on similarity of vectors between the vectorized user setting information and vector information of heading information of the prompts stored in the prompt database; “
However, Lester discloses:
wherein the search unit vectorizes the user setting information (Lester , [0201] The search can use a similarity function between prompts such as cosine distance; [0200] The initial user prompt, or first prompt, can then be utilized for semantic search over a library of prompts ;[0054] the system may first initialize the prompt as a fixed-length sequence of vectors… the systems and methods can attach these vectors to the beginning of each embedded input and feed the combined sequence into the model; [0090] the task may comprise generating an embedding for input data; )
and acquires, as prompt candidates, prompts from a prompt database based on similarity of vectors between the vectorized user setting information and vector information of heading information of the prompts stored in the prompt database; (Lester, [0199]- [0202], e.g. [0200] The initial user prompt, or first prompt, can then be utilized for semantic search over a library of prompts (e.g., a library of second prompts, in which the library of second prompts includes pretrained prompts trained based on datasets not used by the user). These prompts can have associated metadata, such as the frozen model used, the date trained, and, most importantly, the dataset used. [0201] The search can use a similarity function between prompts such as cosine distance… The utility of the prompts can be determined by a variety of metrics and the determined utility may be utilized for prompt ranking and/or for user ranking; [0193] The second prompt metadata and the example dataset can be processed with the machine-learned model to generate the augmented first prompt; [0194] the augmented first prompt can be stored in a library of prompts and may be used for semantic search prompt tuning of other prompts; [0199] the semantic search can involve comparing the initial/query prompt to a library of pretrained prompts, supplied by the service/cloud provider for various tasks. Each prompt can have associated metadata. Multiple metrics such as L2, cosine, or max product can be used to determine similar prompts; [0048] A particular output can be selected based on a preferred prompt or based on a prompt that has the highest correlation to the desired task.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of ‘18/945,315’ in view of Cheng with the teaching of Lester to provide a semantic search that can allow for the determination and isolation of similar prompts to use for retraining or tuning and also to improve computational efficiency and improvements in the functioning of a computing system and allow for localized prompt generation with a lessened computational cost. Moreover, prompt tuning with the prompt tuning training API can allow for a user to leverage a server computing system with a database of prompts to generate prompts even if the user has a computing device with limited computational resources, (Lester [0268]- [0269]).
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 1-3 and 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over GAUR (US20250165231A1) in view of Cheng (US 2024/0303473 Al) in further view of Lester (US 2023/0325725 Al)
Regarding claim 1, GAUR discloses: An information processing device comprising: a reception unit that receives, from another information processing device having received user setting information that is information set by a user, the user setting information via an application programming interface (API) defined in advance; (GAUR [0083] Processor(s) 710 can be configured to conduct program code generation for data processing programs, which can include, for receipt of a user prompt (Sl0l); Fig. 3, user_prompt; [0032] In step Sl0l, the user inputs a prompt. An example prompt can be "The OEE of the product A for the last three days". [0082] when information or an execution instruction is received by API unit 765, it may be communicated to one or more other units (e.g., logic unit 760, input unit 770, output unit 775); [0077] Computer device 705 can be communicatively coupled (e.g., via IO interface 725) to external storage 745 and network 750 for communicating with any number of networked components, devices, and systems, including one or more computer devices of the same or different configuration. Computer device 705 or any connected computer device can be functioning as, providing services of or referred to as a server, client, thin server, general machine, special-purpose machine, or another label; Fig. 7)
a search unit that searches for a prompt to be input to generative artificial intelligence (AI) that is used in the other information processing device, based on the user setting information received by the reception unit, (GAUR, [0032] In step S101, the user inputs a prompt. [0033] In step S102, the related prompt search unit (11) searches the prompt database (21) for prompts that are related to the prompt entered in step S101. The search may use vector search technology or other methods; [0036] In step S501, the ETL program code generation unit (15) generates the ETL program code using the combined prompt generated in step S103. The ETL program code can be generated using the Large Language Model (LLM) or any other method.)
and a providing unit that provides, as a provided prompt, the prompt searched by the search unit to the other information processing device via the API. (GAUR, [0034] In step S103, the prompt combining unit (12) generates a combined prompt that combines the prompt entered by the user in step S101 with the related prompts found in step S102. [0035] In step S104, the prompt improvement unit (13) searches the program template database (22) for program templates that are related to the combined prompt generated in step S103. [0082] when information or an execution instruction is received by API unit 765, it may be communicated to one or more other units (e.g., logic unit 760, input unit 770, output unit 775). In some instances, logic unit 760 may be configured to control the information flow among the units and direct the services provided by API unit 765, the input unit 770, the output unit 775, in some example implementations described above. For example, the flow of one or more processes or implementations may be controlled by logic unit 760 alone or in conjunction with API unit 765)
However, GAUR is silent to disclose:
an API defined in advance; wherein the search unit vectorizes the user setting information and acquires, as prompt candidates, prompts from a prompt database based on similarity of vectors between the vectorized user setting information and vector information of heading information of the prompts stored in the prompt database;
However, Cheng discloses:
an API defined in advance (Cheng ,Fig. 12, item 1206 “Transform, by a data gateway, the one or more prompts into a normalized API request”, item 1208 “Transmit, by the data gateway, the normalized API request to an external vendor server hosting one or more neural network based NLP models”, item 1210 “Translate the normalized API request to a vendor-specific request for generating a vendor-specific response by the one or more neural network based NLP models”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of GAUR with the teaching of Cheng to utilize an interface with normalized APIs as an alternative to custom integrations with internal and externally hosted foundational models and to directly interface with the generative services and/or LLM Gateway, (Cheng [0025]) and also to allow generative AI features to be compatible among different AI models with the generative AI platform 110, (Cheng, [0030]). In this way, the customized generative AI stack thus supports a full spectrum of domain-adaptive prompts to enable a full spectrum of personalized and adaptive AI chat applications, (Cheng, abstract).
However, GAUR in view of Cheng does not clearly disclose: wherein the search unit vectorizes the user setting information and acquires, as prompt candidates, prompts from a prompt database based on similarity of vectors between the vectorized user setting information and vector information of heading information of the prompts stored in the prompt database;
However, Lester discloses:
wherein the search unit vectorizes the user setting information (Lester, [0200] The initial user prompt, or first prompt, can then be utilized for semantic search over a library of prompts ; [0201] The search can use a similarity function between prompts such as cosine distance; [0054] the system may first initialize the prompt as a fixed-length sequence of vectors… the systems and methods can attach these vectors to the beginning of each embedded input and feed the combined sequence into the model; [0090] the task may comprise generating an embedding for input data; )
and acquires, as prompt candidates, prompts from a prompt database based on similarity of vectors between the vectorized user setting information and vector information of heading information of the prompts stored in the prompt database; (Lester, [0199]- [0202], e.g. [0200] The initial user prompt, or first prompt, can then be utilized for semantic search over a library of prompts (e.g., a library of second prompts, in which the library of second prompts includes pretrained prompts trained based on datasets not used by the user). These prompts can have associated metadata, such as the frozen model used, the date trained, and, most importantly, the dataset used. [0201] The search can use a similarity function between prompts such as cosine distance… The utility of the prompts can be determined by a variety of metrics and the determined utility may be utilized for prompt ranking and/or for user ranking; [0193] The second prompt metadata and the example dataset can be processed with the machine-learned model to generate the augmented first prompt; [0194] the augmented first prompt can be stored in a library of prompts and may be used for semantic search prompt tuning of other prompts; [0199] the semantic search can involve comparing the initial/query prompt to a library of pretrained prompts, supplied by the service/cloud provider for various tasks. Each prompt can have associated metadata. Multiple metrics such as L2, cosine, or max product can be used to determine similar prompts; [0048] A particular output can be selected based on a preferred prompt or based on a prompt that has the highest correlation to the desired task.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of GAUR in view of Cheng with the teaching of Lester to provide a semantic search that can allow for the determination and isolation of similar prompts to use for retraining or tuning and also to improve computational efficiency and improvements in the functioning of a computing system and allow for localized prompt generation with a lessened computational cost. Moreover, prompt tuning with the prompt tuning training API can allow for a user to leverage a server computing system with a database of prompts to generate prompts even if the user has a computing device with limited computational resources, (Lester [0268]- [0269]).
Claims 8 and 9 correspond to claim 1 and are rejected accordingly.
Regarding claim 2, GAUR in view of Cheng in view of Lester discloses all of the features with respect to claim 1 as outlined above. Claim 2 further recites: wherein the user setting information is a user prompt that is a prompt input or selected by the user, and the search unit searches for a prompt that is assumed to be capable of acquiring appropriate information from the generative Al more than the user prompt received by the reception unit, the prompt being provided as the provided prompt. (GAUR, [0032] In step S101, the user inputs a prompt. [0033] In step S102, the related prompt search unit (11) searches the prompt database (21) for prompts that are related to the prompt entered in step S101. The search may use vector search technology or other methods; [0036] In step S501, the ETL program code generation unit (15) generates the ETL program code using the combined prompt generated in step S103. The ETL program code can be generated using the Large Language Model (LLM) or any other method; [0066] in step S104, the prompt improvement unit (13) searches for the existence of a program template in the program template database (22) that is related to the combined prompt generated in step S103; [0009] If the result is not as expected, the prompt entered by the user, the ETL program code generated, and the result of the judgment that the result was not as expected are recorded in the prompt database. Then, the ETL program code is generated again using LLM. Since the prompt database contains examples of ETL program code that are not as expected, the LLM generates different ETL program code. Repeating this process produces the desired ETL program code.)
Regarding claim 3, GAUR in view of Cheng in view of Lester discloses all of the features with respect to claim 2 as outlined above. Claim 3 further recites: wherein the search unit searches for the prompt to be provided as the provided prompt, based on the user prompt and attributes of the user received by the reception unit. (GAUR, [0029] The prompt database (21) contains IDs, prompts entered by users, record IDs of related prompts, …, user evaluations of generated ETL program code, user names, and so on in accordance with the desired implementation. [0032] In step S101, the user inputs a prompt. [0033] In step S102, the related prompt search unit (11) searches the prompt database (21) for prompts that are related to the prompt entered in step S101. The search may use vector search technology or other methods; [0040] In step S701, the program code evaluation unit (18) inputs information about the prompt to the prompt database (21). In this example, the information shown in 21-1 of FIG. 3 is entered. Specifically, the prompt entered by the user in step S101, the related prompt extracted in step S102, the combined prompt generated in step S103, the ETL program code generated in step S501, the evaluation result received in step S504, and the user name are entered)
Regarding claim 7, GAUR in view of Cheng in view of Lester discloses all of the features with respect to claim 1 as outlined above. Claim 7 further recites: wherein the user setting information is information indicating a request of the user, and the search unit searches for a prompt that is a prompt corresponding to the information indicating the request received by the reception unit, the prompt being provided as the provided prompt. (GAUR, [0032] In step S101, the user inputs a prompt. An example prompt can be “The OEE of the product A for the last three days”. This input can be done by keyboard, touch screen, voice, or otherwise in accordance with the desired implementation. The prompt input is stored in the related prompt search unit (11) and the prompt combining unit (12). [0033] In step S102, the related prompt search unit (11) searches the prompt database (21) for prompts that are related to the prompt entered in step S101. The search may use vector search technology or other methods.[0034]In step S103, the prompt combining unit (12) generates a combined prompt that combines the prompt entered by the user in step S101 with the related prompts found in step S102. In this example, since no related prompt was found in step S102, the prompt entered by the user in step S101 is the combined prompt.)
Regarding claim 10, GAUR in view of Cheng in view of Lester discloses all of the features with respect to claim 2 as outlined above. GAUR in view of Cheng does not clearly disclose: wherein the search unit uses a prompt comparison/determination model that is a large language model to determine whether a prompt candidate is more appropriate than the user prompt.
However, Lester discloses:
wherein the search unit uses a prompt comparison/determination model that is a large language model to determine whether a prompt candidate is more appropriate than the user prompt. (Lester, [0191]-[0192], e.g. the one or more second prompts can be determined based on a semantic search of a library of prompts stored with associated metadata for each respective prompt of the library of prompts; The systems and methods can then generate an augmented first prompt with the machine-learned model based at least in part on the one or more second prompts. Generating the augmented first prompt can involve retraining the first prompt on one or more second prompt datasets associated with the one or more second prompts with the highest similarity scores; [0036] the pre-trained machine-learned model can include a large language model pre-trained with mask training)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of GAUR in view of Cheng with the teaching of Lester to provide a semantic search that can allow for the determination and isolation of similar prompts to use for retraining or tuning and also to improve computational efficiency and improvements in the functioning of a computing system and allow for localized prompt generation with a lessened computational cost. Moreover, prompt tuning with the prompt tuning training API can allow for a user to leverage a server computing system with a database of prompts to generate prompts even if the user has a computing device with limited computational resources, (Lester [0268]- [0269]).
Regarding claim 11, GAUR in view of Cheng in view of Lester discloses all of the features with respect to claim 1 as outlined above. GAUR in view of Cheng does not clearly disclose: wherein the search unit searches for the prompt from the prompt database containing a plurality of prompts, each of the prompts being provided by a prompt provider that is different from an administrator of the other information processing device.
However, Lester discloses:
wherein the search unit searches for the prompt from the prompt database containing a plurality of prompts, each of the prompts being provided by a prompt provider that is different from an administrator of the other information processing device. (Lester, [0201] The search can use a similarity function between prompts such as cosine distance…The library of prompts can be stored on a server computing system that allows other users to upload their own prompts for generating a larger library …User supplied prompts can be provided as freely accessible to all or can be provided as restricted to certain users (e.g., a prompt may only be accessible to users with certain credentials or may be accessible in exchange for other resources), establishing a service for curated datasets)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of GAUR in view of Cheng with the teaching of Lester to provide a semantic search that can allow for the determination and isolation of similar prompts to use for retraining or tuning and also to improve computational efficiency and improvements in the functioning of a computing system and allow for localized prompt generation with a lessened computational cost. Moreover, prompt tuning with the prompt tuning training API can allow for a user to leverage a server computing system with a database of prompts to generate prompts even if the user has a computing device with limited computational resources, (Lester [0268]- [0269]).
Regarding claim 12, GAUR in view of Cheng in view of Lester discloses all of the features with respect to claim 1 as outlined above. GAUR in view of Cheng does not clearly disclose: wherein the providing unit provides a plurality of provided prompts and heading information of the plurality of provided prompts to the other information processing device.
However, Lester discloses:
wherein the providing unit provides a plurality of provided prompts and heading information of the plurality of provided prompts to the other information processing device. (Lester, [0199] Each prompt can have associated metadata. Multiple metrics such as L2, cosine, or max product can be used to determine similar prompts; [0073] server computing system 130 includes plural server computing devices, such server computing devices can operate according to sequential computing architectures, parallel computing architectures, or some combination thereof; [0048] prompt ensembling can include pairing the inputs with each prompt of the plurality of prompts and passing all of the pairs through the large frozen pre-trained machine-learned model such that there is at least one output for each prompt. A particular output can be selected based on a preferred prompt or based on a prompt that has the highest correlation to the desired task…the output with the highest confidence score may be provided; [0100] the central device data layer can communicate with a number of other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, and/or additional component…, the central device data layer can communicate with each device component using an API ( e.g., a private API).)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of GAUR in view of Cheng with the teaching of Lester to provide a semantic search that can allow for the determination and isolation of similar prompts to use for retraining or tuning and also to improve computational efficiency and improvements in the functioning of a computing system and allow for localized prompt generation with a lessened computational cost. Moreover, prompt tuning with the prompt tuning training API can allow for a user to leverage a server computing system with a database of prompts to generate prompts even if the user has a computing device with limited computational resources, (Lester [0268]- [0269]).
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over GAUR (US20250165231A1) in view of Cheng (US 2024/0303473 Al) in view of Lester (US 2023/0325725 Al) in further view of Lyons (US20240362968A1)
Regarding claim 4, GAUR in view of Cheng in view of Lester discloses all of the features with respect to claim 1 as outlined above GAUR in view of Cheng in view of Lester does not clearly disclose:
comprising a billing unit that charges an administrator of the other information processing device for a providing fee of the provided prompt, when the provided prompt is provided by the providing unit.
However Lyons discloses:
comprising a billing unit that charges an administrator of the other information processing device for a providing fee of the provided prompt, when the provided prompt is provided by the providing unit. (Lyons [0053] the prompt assistor can be provided based on a subscription model or preferred player model, such as where specific levels of prompt construction (e.g., access to specific words, levels of words, categories, subcategories, etc.) are available based on the level of the subscription.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of GAUR in view of Cheng in view of Lester with the teaching of Lyons to update the machine learning model to engineer improved prompts, to dynamically generate improved gaming content, to provide improved customizations, (Lyons, [0085]).
Regarding claim 5, GAUR in view of Cheng in view of Lester in view of Lyons discloses all of the features with respect to claim 4 as outlined above. GAUR in view of Cheng in view of Lester does not clearly disclose: comprising a determination unit that determines a higher fee as the providing fee for a provided prompt that is assumed to have a higher possibility of acquiring appropriate information from the generative AI.
However Lyons discloses:
comprising a determination unit that determines a higher fee as the providing fee for a provided prompt that is assumed to have a higher possibility of acquiring appropriate information from the generative AI. (Lyons, [0053] the prompt assistor can be provided based on a subscription model or preferred player model, such as where specific levels of prompt construction (e.g., access to specific words, levels of words, categories, subcategories, etc.) are available based on the level of the subscription… the processor can provide differing levels of access to, or use of, a machine learning model, such as by awarding a player a priority access to a queue for use of the machine learning model, awarding a faster output time of the machine learning model or higher- quality output of the machine learning model (e.g., the processor provides a quicker output of the machine learning model for a higher-priority access, such as an output that completes in 5 second, whereas the processor provides a slower output of the machine learning model for a lower-priority access, such as an output that completes in 15 seconds)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of GAUR in view of Cheng in view of Lester with the teaching of Lyons to update the machine learning model to engineer improved prompts, to dynamically generate improved gaming content, to provide improved customizations, (Lyons, [0085]).
Regarding claim 6, GAUR in view of Cheng in view of Lester in view of Lyons discloses all of the features with respect to claim 5 as outlined above. GAUR in view of Cheng in view of Lester does not clearly disclose: when a prompt based on attributes of the user is the provided prompt, the determination unit determines a higher amount for the providing fee compared to a case where a prompt not based on the attributes of the user is the provided prompt.
However Lyons discloses:
when a prompt based on attributes of the user is the provided prompt, the determination unit determines a higher amount for the providing fee compared to a case where a prompt not based on the attributes of the user is the provided prompt. (Lyons, [0053] the prompt assistor can be provided based on a subscription model or preferred player model, such as where specific levels of prompt construction (e.g., access to specific words, levels of words, categories, subcategories, etc.) are available based on the level of the subscription; [0070]-[0071], e.g. [0071] the processor predicts, via the machine learning model based on detected player characteristics, an emotion (e.g., a probable emotional state) of a player (e.g., how the player is feeling, whether sad, happy, excited, bored, etc.). The processor can then use the predicted emotion to generate or select a prompt token that relates to, or is based on, the emotion. For instance, if the processor detects that a player is feeling a negative mood (e.g., sad, boredom, etc.) the processor may construct a prompt token that describes a word or phrase that will dynamically generate gaming content intended to induce a positive mood.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of GAUR in view of Cheng in view of Lester with the teaching of Lyons to update the machine learning model to engineer improved prompts, to dynamically generate improved gaming content, to provide improved customizations, (Lyons, [0085]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over GAUR (US20250165231A1) in view of Cheng (US 2024/0303473 Al) in view of Lester (US 2023/0325725 Al) in view of TUNSTALL-PEDOE (WO2023161630A1)
Regarding claim 13, GAUR in view of Cheng in view of Lester discloses all of the features with respect to claim 1 as outlined above. GAUR in view of Cheng in view of Lester does not clearly disclose: wherein the search unit has a type determination model that is a language model different from the generative Al of the other information processing device, and determines whether a type of the user setting information is a user prompt or request information using the type determination model.
However, TUNSTALL-PEDOE discloses:
wherein the search unit has a type determination model that is a language model different from the generative Al of the other information processing device, (TUNSTALL-PEDOE, page 139, section “Identifying when Accuracy is Important”- This classifier could be another LLM or even the same LLM with an earlier prompt or training for it to commence its continuation with an indication of the category of the continuation; pages 141-142, a classifier could be used to identify whether the prompt posed a risk of plagiarism issues, distinguishing between a request for the LLM-based system to produce a song or poem against a situation where it is answering a factual question.)
and determines whether a type of the user setting information is a user prompt or request information using the type determination model. (TUNSTALL-PEDOE, page 139, section “Identifying when Accuracy is Important” This classifier could be another LLM or even the same LLM with an earlier prompt or training for it to commence its continuation with an indication of the category of the continuation; pages 141-142, a classifier could be used to identify whether the prompt posed a risk of plagiarism issues, distinguishing between a request for the LLM-based system to produce a song or poem against a situation where it is answering a factual question.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of GAUR in view of Cheng in view of Lester with the teaching of TUNSTALL-PEDOE to provide new context data for the LLM, in order to improve the output, such as continuation text output, generated by the LLM in response to a prompt, and also to enable an improved version of that continuation output to be provided to a user (TUNSTALL-PEDOE, abstract).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Faezeh Forouharnejad whose telephone number is (571)270-7416. The examiner can normally be reached on Mondays, Wednesdays and Thursdays.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shah Sanjiv can be reached on (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/F.F. /
Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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