DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 10/10/2024 and 04/02/2025 were was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
Claims 1-5, 7, 9-11, 15-17, 19, and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 7-9, 11-16 of copending Application No. 18/911,854 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious variations of each other.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Regarding Claim 1 (drawn to a method):
Current Application
Claim 1:
A method comprising:
receiving, by a processor of a network-connected device, a natural-language prompt from a user and a user identifier corresponding to the user;
querying, by the processor, a first database with the user identifier to retrieve first information;
generating, by the processor, a vector embedding representative of the first information and the natural-language prompt;
querying, by the processor, a second database using the vector embedding to retrieve second information, wherein: the second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments, and the second information comprises at least one text segment of the plurality of text segments; and
generating, by a language model executed by the processor, a natural-language response text responsive to the user query based on the natural-language prompt, the first information, and the second information.
‘854
Claim 1:
A method of automated technical support, the method comprising:
receiving, by a processor of a network-connected device, a first natural-language prompt from a user and a user identifier corresponding to the user, the first natural-language prompt including at least one technical support query;
querying, by the processor, a first database with the user identifier to retrieve first information;
generating, by the processor, a first vector embedding representative of the first information and the first natural-language prompt;
querying, by the processor, a second database using the first vector embedding to retrieve second information, wherein: the second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments, and the second information comprises at least one text segment of the plurality of text segments; and
generating, by a language model executed by the processor, a first natural-language response text based on the first natural-language prompt, the first information, and the second information, the first natural-language response text responsive to the at least one technical support query.
Regarding Claim 16 (drawn to a system):
Current Application
Claim 16:
A system comprising:
a first database configured to store first user-specific information;
a second database configured to store a plurality of vector embeddings representative of a plurality of natural-language text segments, each vector embedding of the plurality of vector embeddings representative of one natural-language text segment of the plurality of natural-language text segments;
a network-connected device in electronic communication with the first database and with the second database, the network-connected device comprising:
a processor; and
at least one memory encoded with instructions that, when executed, cause the processor to:
receive a natural-language prompt from a user and a user identifier corresponding to the user;
query the first database with the user identifier to retrieve first information;
generate a vector embedding representative of the first information and the natural-language prompt;
query the second database using the vector embedding to retrieve second information; and
generate, using a language model executed by the processor, a natural-language response text responsive to the user query based on the natural-language prompt, the first information, and the second information..
‘854
Claim 16:
A system comprising:
a first database configured to store first user-specific information;
a second database configured to store a plurality of vector embeddings representative of a plurality of natural-language text segments, each first vector embedding of the plurality of vector embeddings representative of one natural-language text segment of the plurality of natural-language text segments;
a network-connected device in electronic communication with the first database and with the second database, the network-connected device comprising:
a processor; and
at least one memory encoded with instructions that, when executed, cause the processor to:
receive a first natural-language prompt from a user and a user identifier corresponding to the user, the first natural-language prompt including at least one technical support query;
query the first database with the user identifier to retrieve first information;
generate a first vector embedding representative of the first information and the first natural-language prompt;
query the second database using the first vector embedding to retrieve second information; and
generate, using a language model executed by the processor, a first natural-language response text based on the first natural-language prompt, the first information, and the second information, the first natural-language response text responsive to the at least one technical support query.
As shown in the tables above, it is clear that all the elements of the application claims 1 and 16 are to be found in patent claims1 and 16, as the application claims 1 and 16 fully encompasses patent claims 1 and 16. The difference between the application claims 1 and 16and the patent claims 1 and 16 lies in the fact that the patent claims includes more elements and is thus more specific. Thus the invention of claims 1 and 16 of the patent is in effect a “species” of the “generic” invention of the application claims 1 and 16. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claim 2 of the current application corresponds to claim 7 of copending Application No. 18/911,854.
Claim 3 of the current application corresponds to claim 8 of copending Application No. 18/911,854.
Claim 4 of the current application corresponds to claim 9 of copending Application No. 18/911,854.
Claim 6 of the current application corresponds to claim 11 of copending Application No. 18/911,854.
Claim 7 of the current application corresponds to claim 12 of copending Application No. 18/911,854.
Claim 9 of the current application corresponds to claim 13 of copending Application No. 18/911,854.
Claim 10 of the current application corresponds to claim 14 of copending Application No. 18/911,854.
Claim 11 of the current application corresponds to claim 5 of copending Application No. 18/911,854.
Claim 15 of the current application corresponds to claim 15 of copending Application No. 18/911,854.
Claim 17 of the current application corresponds to claim 5 of copending Application No. 18/911,854.
Claim 19 of the current application corresponds to claim 9 of copending Application No. 18/911,854.
Claim 20 of the current application corresponds to claim 11 of copending Application No. 18/911,854.
Claims 5, 8, 12-14, and 18 of the current application do not correspond to any claims of copending Application No. 18/911,854.
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. Independent claims 1 and 16 relate to the statutory category of method/process and machine/apparatus. The independent claims 1 recites “receiving, by a processor of a network-connected device, a natural-language prompt from a user and a user identifier corresponding to the user; querying, by the processor, a first database with the user identifier to retrieve first information; generating, by the processor, a vector embedding representative of the first information and the natural-language prompt; querying, by the processor, a second database using the vector embedding to retrieve second information, wherein: the second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments, and the second information comprises at least one text segment of the plurality of text segments; and generating, by a language model executed by the processor, a natural-language response text responsive to the user query based on the natural-language prompt, the first information, and the second information”. Independent Claim 16, recites “… receive a natural-language prompt from a user and a user identifier corresponding to the user; query the first database with the user identifier to retrieve first information; generate a vector embedding representative of the first information and the natural-language prompt; query the second database using the vector embedding to retrieve second information; and generate, using a language model executed by the processor, a natural-language response text responsive to the user query based on the natural-language prompt, the first information, and the second information”.
The limitation of claims 1 and 16 of “receiv(ing)…”, “query(ing)…, “generat(ing)…”, “query(ing)…”, and “generat(ing)…” as drafted covers mental activity. More specifically, for claim 1, a human when prompted for information, can query a table/ list to determine what information a particular user has access to and from that information, they can then query a second table/list to retrieve more information related to the original prompt. The second table/list contains a plurality of text segments that comprise a response to the original prompt. The information gathered from the first query and the second query are used to generate a response to the original prompt.
This judicial exception is not integrated into a practical application. In particular claims 1 and 16 recite the additional elements of “processor”, “memory”, “network-connected device”, and “language model” which are recited generally in the as filed specification. For example, in paragraphs [0013]-[0015], there is a description of using a general purpose operating system. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea int a practical application, the additional elements of using a computer as a general computer is noted. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claim 2, the claim relates to receiving dialog communication from a user and from that dialog communication determining the user and the prompt for information. The claim relates to a mental activity of looking at a dialog communication and extracting the information necessary from the dialog communication. No additional limitations are present.
With respect to claim 3, 4, 5, and 19, the claim relates to transmitting the necessary information. The claim relates to a mental activity of sending the requested information. No additional limitations are present.
With respect to claims 6 and 20, the claims relate to the textual format of the first table/list. The claims relate to a mental activity of understanding what is listed in the first table/list. No additional limitations are present.
With respect to claim 7, the claim relates to receiving a dialog communication history of the user and from the history separate the questions and the answers and create the textual segments for the table/list. No additional limitations are present.
With respect to claims 8 and 18, the claims relate to where the information is a feature about the user. The claim relates to a mental activity of using a characteristic to describe the user. No additional limitations are present.
With respect to claim 9, the claim relate to improving the response by combining the information from the first table/list and information from the second table/list. The claim relates to a mental activity of providing a better response to the original information request. No additional limitations are present.
With respect to claim 10, the claim relates to using an application programming interface to submit queries. An application programming interface is well known element of a general purpose computing device. Accordingly, this additional element does not integrate the abstract idea into a practical application.
With respect to claims 11, 13 and 17, the claims relate to searching other tables/lists to get the necessary information. The claims relate to a menta activity of searching other sources depending what information is needed. No additional limitations are present.
With respect to claims 12 and 14, the claims relate to what data is stored in the different tables/lists. The claims relate to a mental activity of the different tables/lists having different information depending on what the user’s query is asking. No additional limitations are present.
With respect to claim 15, the claim relates to separating the data based on the user. The claim relates to searching the tables/lists based on the user and retrieving the information related to the user. No additional limitations are present.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 6, 8, 9, 11, and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang Tao (CN115114418).
Regarding Claim 1, Yang Tao discloses a method comprising:
receiving, by a processor of a network-connected device, a natural-language prompt from a user and a user identifier corresponding to the user ((Query: Who wrote the Compendium of Materia Medica, a medical book)) (paragraph [n0131]);
querying, by the processor, a first database with the user identifier to retrieve first information (As shown in Figure 6, firstly, based on the question information (Query: Who wrote the Compendium of Materia Medica, a medical book), top K related answer information (answer information 602 and answer information 604) is retrieved from the answer information database) (paragraph [n0131]);
generating, by the processor, a vector embedding representative of the first information and the natural-language prompt (The question answering prediction model obtained through the (i-l)th round of training converts the current sample question information into the current sample question representation vector and the current sample answer information into the current sample original answer representation vector) (paragraph [n0135]);
querying, by the processor, a second database using the vector embedding to retrieve second information (The question-answering prediction model obtained through the (i-l)th round of training transforms the original answer representation vector of the current sample into multiple current sample answer representation vectors) (paragraph [n0136]), wherein: the second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments (Input the current sample question information and the current sample answer information into the question answering prediction model) (paragraph [n0135]), and the second information comprises at least one text segment of the plurality of text segments (The question-answering prediction model obtained through the (i-l)th round of training transforms the original answer representation vector of the current sample into multiple current sample answer representation vectors) (paragraph [n0136]); and
generating, by a language model executed by the processor, a natural-language response text responsive to the user query based on the natural-language prompt, the first information, and the second information (The question-answering prediction model obtained through the (i-l)th round of training transforms the original answer representation vector of the current sample into multiple current sample answer representation vectors, and determines the target answer representation vector of the current sample based on the multiple current sample answer representation vectors) (paragraph [n0136]).
Regarding Claim 6, Yang Tao discloses the method, wherein the first database is at least one of a structured database and a semi-structured database (The article retrieval module, due to the extremely large number of articles in the database millions or tens of millions-needs to retrieve the top K documents that are highly relevant to the user's question from such a massive document library) (paragraph [n0074]).
Regarding Claim 8, Yang Tao discloses the method, wherein the first information describes a first attribute of the user (data such as user information are involved) paragraph [n0184]).
Regarding Claim 9, Yang Tao discloses the method, wherein generating the natural-language response text comprises:
generating an augmented natural-language prompt by combining text information from the natural-language prompt, the first information, and the second information (The question answering prediction model obtained through the (i-l)th round of training converts the current sample question information into the current sample question representation vector and the current sample answer information into the current sample original answer representation vector) (paragraph [n0135]);
generating, by the language model, the natural-language response text based on the augmented natural-language prompt As shown in Figure 6, firstly, based on the question information (Query: Who wrote the Compendium of Materia Medica, a medical book), top K related answer information (answer information 602 and answer information 604) is retrieved from the answer information database. Then, the answer to the question (Answer: Li Shizhen) is used to back-label these retrieved answer information) (paragraph [n0131]).
Regarding Claim 11, Yang Tao discloses the method, and further comprising querying, by the processor, a third database with the user identifier to retrieve third information, and wherein generating, by the processor, the vector embedding comprises a generating a vector embedding representative of the first information, the third information, and the natural-language prompt (The question-answering prediction model obtained through the (i-l)th round of training transforms the original answer representation vector of the current sample into multiple current sample answer representation vectors, and determines the target answer representation vector of the current sample based on the multiple current sample answer representation vectors) (paragraph [n0136]).
Regarding Claim 16, Yang Tao discloses a system comprising:
a first database configured to store first user-specific information (In the i-th
round, the target sample question information input to the question-answering prediction
model is "Who is Zhang San's brother?", and the sample question information set includes the sample question information "Who is Zhang San's brother?") (paragraph [n0130]);
a second database configured to store a plurality of vector embeddings representative of a plurality of natural-language text segments, each vector embedding of the plurality of vector embeddings representative of one natural-language text segment of the plurality of natural-language text segments (Input the current sample question information and the current sample answer information into the question answering prediction model. The question answering prediction model obtained through the (i-l)th round of training converts the current sample question information into the current sample question representation vector and the current sample answer information into the current sample original answer representation vector) (paragraph [n0135]);
a network-connected device in electronic communication with the first database and with the second database (According to another aspect of the embodiments of this application, an electronic device for implementing the above-described information retrieval method is also provided. The electronic device may be the terminal device or server) (paragraph [n0226]), the network-connected device comprising:
a processor (the electronic device includes a memory 1002 and a processor 1004) (paragraph [n0226]); and
at least one memory ((the electronic device includes a memory 1002 and a processor 1004) (paragraph [n0226]) encoded with instructions that (The
memory 1002 stores a computer program, and the processor 1004 is configured to execute the steps of any of the above method embodiments through the computer program) (paragraph [n0226]), when executed, cause the processor to:
receive a natural-language prompt from a user and a user identifier corresponding to the user ((Query: Who wrote the Compendium of Materia Medica, a medical book)) (paragraph [n0131]);
query the first database with the user identifier to retrieve first information (As shown in Figure 6, firstly, based on the question information (Query: Who wrote the Compendium of Materia Medica, a medical book), top K related answer information (answer information 602 and answer information 604) is retrieved from the answer information database) (paragraph [n0131]);
generate a vector embedding representative of the first information and the natural-language prompt (The question answering prediction model obtained through the (i-l)th round of training converts the current sample question information into the current sample question representation vector and the current sample answer information into the current sample original answer representation vector) (paragraph [n0135]);
query the second database using the vector embedding to retrieve second information (The question-answering prediction model obtained through the (i-l)th round of training transforms the original answer representation vector of the current sample into multiple current sample answer representation vectors) (paragraph [n0136]); and
generate, using a language model executed by the processor, a natural-language response text responsive to the user query based on the natural-language prompt, the first information, and the second information (The question-answering prediction model obtained through the (i-l)th round of training transforms the original answer representation vector of the current sample into multiple current sample answer representation vectors, and determines the target answer representation vector of the current sample based on the multiple current sample answer representation vectors) (paragraph [n0136]).
Regarding Claim 17, Yang Tao discloses the system wherein: the system further comprises a third database configured to store second user-specific information, the instructions, when executed, cause the processor to query the third database with the user identifier to retrieve third information, and the vector embedding is representative of the first information, the third information, and the natural-language prompt (The question-answering prediction model obtained through the (i-l)th round of training transforms the original answer representation vector of the current sample into multiple current sample answer representation vectors, and determines the target answer representation vector of the current sample based on the multiple current sample answer representation vectors) (paragraph [n0136]).
Claim 18 is rejected for the same reason as claim 8.
Regarding Claim 19, Yang Tao discloses the system, herein the system further comprises a user device in electronic communication with the network-connected device (in this embodiment, the above-mentioned information retrieval method can be applied to a hardware environment consisting of a server 101 and a terminal device 103) (paragraph [n0044]), and the instructions, when executed, further cause the processor to transmit, to the user device, an electronic indication of the natural-language response text to the user device (on the terminal device 103, based on the matching degree between the target question information and the candidate answer information, determine whether the candidate answer information is the answer information that matches the target question information) (paragraph [n0050]).
Claim 20 is rejected for the same reason as claim 6.
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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Yang Tao in view of Callegari et al. (US 2024/0362422).
Regarding Claim 10, Yang Tao teaches the method, wherein querying the first database comprises: transmitting, by the network-connected device, a query request for the first database to a database server, wherein: the database server comprises the first database (Database 105 can be set up on the server or independently of the server to provide data storage services for server 101, such as a game data storage server) (paragraph [[n0044]).
Yang Tao fails to teach the request includes an application programming interface command for an application programming interface operated by the database server to query the first database; executing, by the application programming interface, the application programming interface command to query the first database to retrieve the first information; transmitting, by the database server, the retrieved first information to the network-connected device.
Callegari et al teaches the request includes an application programming interface command for an application programming interface operated by the database server to query the first database (the prompt interface 24 may be implemented as a prompt interface application programming interface (API)) (the input to the prompt interface may be made by an API call from a calling software program to the prompt interface API) (page 2, paragraph [0017]);
executing, by the application programming interface, the application programming interface command to query the first database to retrieve the first information (the input to the prompt interface may be made by an API call from a calling software program to the prompt interface API) (page 2, paragraph [0017]);
transmitting, by the database server, the retrieved first information to the network-connected device (output can be returned in an API response from the prompt interface API to the calling software program) (page 2, paragraph [0017]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Yang Tao with the teachings of Callegari improve functionality and communication by allowing the sharing of data.
Allowable Subject Matter
Claims 2-5, 7, and 12-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the 35 USC 101 rejections and Double Patenting Rejections above are overcome.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Watson et al. (US 11,971,914) discloses an artificial intelligence-based search method for searching content of a big data source.
Radhakrishnan et al. (US 12,111,858) discloses database system interaction embedding and indexing for text retrieval and generation.
Nelson et al. (US 2024/0394291) discloses automated domain adaption or semantic search using embedding vectors.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATWANT K SINGH whose telephone number is (571)272-7468. The examiner can normally be reached Monday thru Friday 9:00 AM to 6:00 PM EST.
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/SATWANT K SINGH/Primary Examiner, Art Unit 2653
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