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 .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/11/2026 has been entered.
Response to Amendment
3. The Amendment filed on 03/11/2026 has been entered. Claims 1, 3, 5, 10, 13, 16, 17, 18 and 21 have been amended, claims 1, 3, 5 – 14, 16 – 19 and 21 - 23 are pending in the application, with claims 2, 4, 15 and 20 cancelled from consideration.
NOTE: In Applicant’s amended claim listing, mention is made to cancel claims 20 – 22 however in the submitted claims, only claim 20 is actually cancelled but claims 21 and 22 are still presented. Designations are not made in the claim listing to actually cancel said claims.
Response to Arguments
35 U.S.C. §103
4. Applicant's arguments, see Remarks pp. 10 -15, filed 03/11/2026, with
respect to the rejections of claims 1, 3, 5 – 14, 16 – 19 and 21 - 23 under 35 U.S.C. §103 have been fully considered and they are persuasive.
The crux of Applicant’s arguments is that the amendments to the independent claims are not taught by the art of record
Examiner respectfully agrees
Upon further consideration new grounds of rejection have been necessitated due
to Applicant's amendments and are made in view of Kazuki KYAKUNO(United States Patent Publication Number 2025/0103627) hereinafter Kyakuno
Claim Rejections – 35 U.S.C. §103
5. 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.
6. The factual inquiries set forth in Graham v John Deere Co., 383 U.S. 1, 148 USPQ
459 (1966), that are applied for establishing a background for determining obviousness
under 35 U.S.C. 103 are summarized as follows:
a. Determining the scope and contents of the prior art
b. Ascertaining the differences between the prior art and the claims at issue
c. Resolving the level of ordinary skill in the pertinent art
d. Considering objective evidence present in the application indicating
obviousness or nonobviousness
Claims 1, 3, 16 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (United States Patent Publication Number 20250139160), hereinafter Miller, in view of Ju et al. (United States Patent Publication Number 20170300744 ), hereinafter referred to as Ju and in further view of Kazuki KYAKUNO(United States Patent Publication Number 2025/0103627) hereinafter Kyakuno
Regarding claim 1 Miller teaches a method (method [0020], [0184] [0185], [0188]) of generating an automated response (automatically generated content [0084], [0139]) to a user prompt, (prompt generated in response to and/or using a user query [0038]) the method (method [0020], [0184] [0185], [0188]) comprising: receiving, (receiving [0116], [0123]) by a processor (one or more graphics processing units (GPUs)
and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) of a network-connected device, (Fig. 1, (104) content composer and content transmission system 104 (which may include a stitcher component, such
as a server … is connected to a network 102 (e.g., a wide area network, the Internet, a local area network, or other network). [0047]) a first natural-language prompt (first request or query [0182]) such as “a first natural-language prompt” SEE EXAMPLES "Surprise me with a content recommendation," "provide random recommendations." [0149], "I'm in the mood for food shows" [0174], "pick up where I left off', "access
watchlist," "surprise me," "let's watch something new." [ 0177] from a user; (a user [00156]) generating, (generating [0016]) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) a first vector embedding (first vector converted from the first request or query [0182]) representative of the first natural-language prompt; (first request or query [0182]) such as “a first natural-language prompt” SEE EXAMPLES "Surprise me with a content recommendation," "provide random recommendations." [0149], "I'm in the mood for food shows" [0174], "pick up where I left off', "access watchlist," "surprise me," "let's watch something new." [ 0177] querying, by the processor, (one or more graphics processing units (GPUs)
and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) a vector database (Fig. 5, (508) query vector database [0157]) using the first vector embedding (first vector converted from the first request or query [0182]) to identify (perform a similarity search [0182) a second vector embedding (second vector [0182]) representative of a second natural-language prompt (corresponding to a corresponding previously received request or query [0182]) and having a similarity score (similarity score ][0071]) with the first vector embedding (first vector converted from the first request or query [0182]) above a defined threshold, (above the threshold [0071]) wherein the vector database (Fig. 5, (508) query vector database [0157]) comprises a plurality of vector embeddings stored (vectors stored in a vector database [0182]) each vector embedding representative of a natural language prompt; (vectors stored in a vector database corresponding to previously received queries or requests; [0182]) and producing a first natural-language response (Fig. 5, (512) return cached response [0164]) to the first natural-language prompt, (first request or query [0182]) such as “a first natural-language prompt” SEE EXAMPLES "Surprise me with a content recommendation," "provide random recommendations." [0149], "I'm in the mood for food shows" [0174], "pick up where I left off', "access watchlist," "surprise me," "let's watch something new." [0177] wherein producing (producing [0023]) the first natural-language response (Fig. 5, (512) return cached response [0164]) comprises retrieving, (retrieve [0126]) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) the first natural-language response (Fig. 5, (512) return cached response [0164]) to the second natural language prompt (corresponding to a corresponding previously received request or query [0182]) when the second vector embedding (second vector [0182]) is identified (identified [0033], [0038], [0044]) in querying the vector database. (Fig. 5, (508) query vector database [0157]) the first natural-language response (a first request or query [0182]) in association with the response identifier (first vector [0182]) of the second vector embedding (any one of the vectors stored in the vector database [0182])
Miller does not fully disclose in association with a response identifier, from a cache database; stored in a cache database; and wherein producing the first natural-language response comprises generating, by a language model executed by the processor, the first natural-language response to the first natural-language prompt when querying the vector database fails to identify the second vector embedding; and requesting, by the processor, the user to approve of or disapprove of the first natural- language response, based on content of the first natural-language response and relevance to the first natural-language prompt, by selecting a relevance indicator on a user device, the relevance indicator representing approval or disapproval, wherein in response to the user selecting the relevance indicator representing disapproval of the first natural-language response and wherein the first natural-language response is a response retrieved from the cache database: producing, by the processor: a list of suggested natural language prompts, each suggested natural language prompt having a vector embedding representative of the suggested natural-language prompt and having a similarity score with the first vector embedding above the defined threshold; a request for the user to select a suggested natural-language prompt from the list of suggested natural-language prompts; and retrieving, by the processor, an alternative natural-language response associated with the selected suggested natural- language prompt from the cache database.
Ju teaches in association with a response identifier, (ABS., identity identifier) (Fig. 1, (140) identity identifier [0049]) such as “response identifier” from a cache database (a cache database [0057]) stored in a cache database(a cache database [0057])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller to incorporate the teachings of Ju wherein in association with a response identifier from a cache database. By doing so a cached identity identifier of the target vector can be determined. Ju [0011]
Kyakuno teaches and wherein producing the first natural-language response (response to a question [0037]) comprises generating, by a language model (Fig. 1 generated by LLM (SERVER DEVICE (LLM) [0027]) executed by the processor, (processor [0200])the first natural-language response (response to a question [0037]to the first natural-language prompt (a question received by the user device 10 from a user, the server device 60 generates an answer in a natural language using the LLM [0037]) when querying (querying [0096]) the vector database (vector database (DB) 32. [0032]) fails to identify the second vector embedding; (query vector embedding [0144]) and requesting, by the processor, (processor [0200]) the user (the user [0110]) to approve of (approves [0104]) or disapprove (disapproves [0110]) of the first natural- language response, (response to a question received by the user device 10 from a user, [0037]) based on content (content for which an answer is sought [0041]) of the first natural-language response (response to a question received by the user device 10 from a user, [0037]) and relevance ( relevance to the query [0057]) to the first natural-language prompt, (a question received by the user device 10 from a user, [0037]) by selecting a relevance indicator (Fig. 6 confirmation portion [0054]) such as “relevance indicator” SEE [0136] The display in (b-2) of FIG. 5 is the same as that of (b-1) of FIG. 5, but no check box 107 is displayed in the confirmation field 103, and words "do you want to confirm by the administrator?", a "yes" button, and a "no" button are displayed in the confirmation dialog 104. on a user device, (Figs. 5, 6, 7A – 7C and 8A – 8C user device [0018] – [0022]) the relevance indicator (Fig. 6 confirmation portion [0054]) such as “relevance indicator” SEE [0136] The display in (b-2) of FIG. 5 is the same as that of (b-1) of FIG. 5, but no check box 107 is displayed in the confirmation field 103, and words "do you want to confirm by the administrator?", a "yes" button, and a "no" button are displayed in the confirmation dialog 104. representing approval (transmission approval [0105], [0106], [0108], [0111]) or disapproval, (transmission disapproval [0107]) wherein in response to the user selecting (when the administrator device 40 disapproves the transmission, [0110])the relevance indicator (Fig. 6 confirmation portion [0054]) such as “relevance indicator” SEE [0136] The display in (b-2) of FIG. 5 is the same as that of (b-1) of FIG. 5, but no check box 107 is displayed in the confirmation field 103, and words "do you want to confirm by the administrator?", a "yes" button, and a "no" button are displayed in the confirmation dialog 104. representing disapproval (transmission disapproval [0107]) SEE [0131] When the user determines that the search result cannot be transmitted to the server device 60 and selects the "no" button of the confirmation dialog 104 of the first natural-language response (response to a question received by the user device 10 from a user, [0037])and wherein the first natural-language response (response to a question received by the user device 10 from a user, [0037])is a response retrieved (search results [0132]) from the cache database: (storage portion [0048]) such as “cache database” producing, by the processor: (processor [0200]) a list of suggested natural language prompts, (feature vectors of documents stored in the vector DB 32, [0043]) each suggested natural language prompt (feature vectors of documents stored in the vector DB 32, [0043])having a vector embedding representative of the suggested natural-language prompt and having a similarity score with the first vector embedding above the defined threshold; ( a document having high relevance (high similarity) to the calculated query. [0043])a request for the user to select a suggested natural-language prompt (a question received by the user device 10 from a user, the server device 60 generates an answer in a natural language using the LLM [0037])from the list of suggested natural-language prompts; (the user device 10 compares a feature vector of a query with vectors stored in the vector DB 32, searches for a vector having high relevance, for example, vectors having IDs of 1-3, 2-2, and 3-1, and searches the document DB 31 for chunks having IDs corresponding thereto. [0098]) and retrieving, (The chm1ks of IDs 1-3, 2-2, and 3-1 are transmitted to the server device 60 together with the query and used for generating an answer [0099]) by the processor, (processor [0200]) an alternative natural-language response ( vectors having IDs of 1-3, 2-2, and 3-1, [0098]) associated with the selected suggested natural- language prompt (a question received by the user device 10 from a user, the server device 60 generates an answer in a natural language using the LLM [0037]) from the cache database. (storage portion [0048]) such as “cache database”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju to incorporate the teachings of Kyakuno and wherein producing the first natural-language response comprises generating, by a language model executed by the processor, the first natural-language response to the first natural-language prompt when querying the vector database fails to identify the second vector embedding; and requesting, by the processor, the user to approve of or disapprove of the first natural- language response, based on content of the first natural-language response and relevance to the first natural-language prompt, by selecting a relevance indicator on a user device, the relevance indicator representing approval or disapproval, wherein in response to the user selecting the relevance indicator representing disapproval of the first natural-language response and wherein the first natural-language response is a response retrieved from the cache database: producing, by the processor: a list of suggested natural language prompts, each suggested natural language prompt having a vector embedding representative of the suggested natural-language prompt and having a similarity score with the first vector embedding above the defined threshold; a request for the user to select a suggested natural-language prompt from the list of suggested natural-language prompts; and retrieving, by the processor, an alternative natural-language response associated with the selected suggested natural- language prompt from the cache database. By doing so Embedding that expresses the meanings of sentences with real vectors may be referred to as "sentence embedding" particularly. The sentence embedding is for calculating semantic similarity between sentences. By calculating similarity between vectors obtained by sentence embedding, semantic similarity between sentences can be measured, and document search in consideration of meanings of sentences can be performed based on the similarity. Kyakuno [0084]
Regarding claim 3 Miller in view of Ju and Kyakuno teaches the method (method [0020], [0184] [0185], [0188])of claim 1,
Miller further teaches wherein the first natural-language response(Fig. 5, (512) return cached response [0164]) retrieved from the cache database (retrieved from cache [0175]) is a response previously generated (previously provided in response to the corresponding previously received request or query, [0182) by the language model (large language models (LLMs), [0019])
Miller does not fully disclose and stored in the cache database
Ju teaches and stored in the cache database (storing, into a cache database [0057])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Kyakuno to incorporate the teachings of Ju wherein and stored in the cache database. By doing so a cached identity identifier of the target vector can be determined. Ju [0011]
Regarding claim 16 Miller in view of Ju and Kyankuno teaches the method (method [0020], [0184] [0185], [0188])of claim 1,
Miller as modified further teaches wherein one or more of the plurality of vector embeddings (embeddings may comprise high-dimensional vectors [0069]) has an associated natural-language response generated by the language model (query responses generated by an LLM. [0067]) and, wherein the associated natural-language responses (query responses generated by an LLM. [0067])
Miller does not fully disclose are stored in the cache database with a unique corresponding response identifier and wherein the unique response identifier is stored in the vector database in association with the associated vector embedding
Ju teaches are stored in the cache database (storing, into a cache database [0057]) with a unique corresponding response identifier (ABS., identity identifier) (Fig. 1, (140) identity identifier [0049]) such as “response identifier” and wherein the unique response identifier (ABS., identity identifier) (Fig. 1, (140) identity identifier [0049]) such as “response identifier” is stored in the vector database (Fig. 2, (230) an identity identifier that … is recorded in the face image database [0069]) such as “vector database” in association with the associated vector embedding (Fig. 2, (230) that is of the target vector [0069])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Kyakuno to incorporate the teachings of Ju wherein are stored in the cache database with a unique corresponding response identifier and wherein the unique response identifier is stored in the vector database in association with the associated vector embedding. By doing so the cache database includes the matching vector of the original feature vector. Ju [0087].
Regarding claim 17 Miller in view of Ju and Kyankuno teaches the method (method [0020], [0184] [0185], [0188])of claim 2,
Miller as modified further teaches wherein retrieving, (retrieve [0126]) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) the first natural-language response (Fig. 5, (512) return cached response [0164]) to the second natural language prompt (corresponding to a corresponding previously received request or query [0182]) from the cache database(in a system data store (e.g., a database),[0196]) such as a “cached database” comprises retrieving (retrieve [0126]) the first natural-language response(Fig. 5, (512) return cached response [0164])
Miller does not fully disclose by the associated response identifier in the cache database, the associated response identifier stored in association with the second vector embedding in the vector database
Ju teaches by the associated response identifier (identity identifier that is of the matching vector [0087]) such as “associated response identifier” in the cache database, (cache database [0087]) the associated response identifier (identity identifier that is of the matching vector [0087]) such as “associated response identifier” stored in (recorded in [0087]) association with (that is of the [0087]) the second vector embedding (the matching vector [0087]) in the vector database (face image database [0087]) such as “vector database”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Kyakuno to incorporate the teachings of Ju wherein by the associated response identifier in the cache database, the associated response identifier stored in association with the second vector embedding in the vector database. By doing so the cache database includes the matching vector of the original feature vector. Ju [0087]
Regarding claim 18 Miller teaches a system (systems [0014]) comprising: a vector database (vector database [0169]) configured to store vector embeddings (vectors stored in a vector database [0182]) representative of natural-language prompts ("Surprise me with a content recommendation," "provide random recommendations." [0149], "I'm in the mood for food shows" [0174], "pick up where I left off', "access watchlist," "surprise me," "let's watch something new." [ 0177]) and corresponding natural-language responses (( e.g., the LLM) generates a response (e.g., comprising content recommendations). [0150])to the natural-language prompts, ("Surprise me with a content recommendation," "provide random recommendations." [0149], "I'm in the mood for food shows" [0174], "pick up where I left off', "access watchlist," "surprise me," "let's watch something new." [ 0177]) and a natural-language response; (generates a response [0150]) and a network-connected device (The content composer and content transmission system 104 is configured to communicate with client devices 1061 ... 106n (e.g., connected televisions, smart phones, laptops, desktops, game consoles, streaming devices that connect to televisions or computers, etc.) that comprise video players. [0047]) in electronic communication (configured to communicate [0047]) with the vector database; (vector database [0169]) the network-connected device(The content composer and content transmission system 104 is configured to communicate with client devices 1061 ... 106n (e.g., connected televisions, smart phones, laptops, desktops, game consoles, streaming devices that connect to televisions or computers, etc.) that comprise video players. [0047]) comprising: a processor (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor) [0048]) configured to: (configured to [0049]) receive a first natural-language prompt from a user; (may receive a request for media from a given client device 106 in the form of a request for a playlist manifest or updates to a playlist manifest [0058]) generate a query vector(the first vector [0182]) representative of the first natural-language prompt; (may receive a request for media from a given client device 106 in the form of a request for a playlist manifest or updates to a playlist manifest [0058]) query the vector database (Fig. 5, (508 query vector database [0157]) using query vector (the first vector [0182]) to identify a database vector (a vector stored in a vector database corresponding to previously received queries or requests; [0182]) having a similarity score (similarity scores [0070]) with the query vector (the first vector [0182]) above a defined threshold, (similarity scores above the threshold [0070]) and produce a first natural-language response (provide a response (e.g., "Of course! We have a lot of really cool stuff to choose from. Take a look!"). [0178]) to the first natural-language prompt (may receive a request for media from a given client device 106 in the form of a request for a playlist manifest or updates to a playlist manifest [0058])by: a first natural-language response (provide a response (e.g., "Of course! We have a lot of really cool stuff to choose from. Take a look!"). [0178]) and submitting the first natural language prompt (may receive a request for media from a given client device 106 in the form of a request for a playlist manifest or updates to a playlist manifest [0058]) to a language model, (Fig. 5 (505) provide query to AI LLM [0156]) executed by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor) [0048]) to generate a first natural-language response (provide a response (e.g., "Of course! We have a lot of really cool stuff to choose from. Take a look!"). [0178]) when the database vector(a vector stored in a vector database corresponding to previously received queries or requests; [0182]) is not identified (Fig. 5, (510) “within threshold closeness?” “NO” [0158]) the database vector when the database vector is identified; (Fig. 5, (510) “Within threshold closeness?” “YES” [0158]) (Fig. 5, (512) return cached response [0164])
Miller does not fully disclose and associated response identifiers; a cache database configured to store the associated response identifiers; each response identifier associated with a vector embedding of the vector database of the cache database; and the cache database, the database vector associated with a response identifier; retrieving, from the cache database, associated with the response identifier receive, from the user, a relevance datum representing approval or disapproval of the first natural-language response; store the relevance datum in association with the response identifier in the vector database; and store the query vector as a database vector in the vector database in association with the response identifier of the retrieved first natural-language response.
Ju teaches and associated response identifiers; (identity identifiers [0055]) a cache database (cache database [0057]) configured to store the associated response identifiers; (storing the identity identifier [0057]) each response identifier (identity identifier [0055]) associated with a vector embedding (matching vector [0057]) of the vector database (face image database [0049]) such as “vector database” of the cache database; (cache database [0057])and the cache database, (cache database [0057])the database vector(face image database [0049]) such as “vector database” associated with (Fig. 1, (140) recorded in [0049]) a response identifier; (identity identifier [0055])retrieving, from the cache database, (cache database [0057]) associated with (Fig. 1, (140) recorded in [0049]) of the response identifier (identity identifier [0055])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller to incorporate the teachings of Ju wherein associated response identifiers; a cache database configured to store the associated response identifiers; each response identifier associated with a vector embedding of the vector database of the cache database; and the cache database, the database vector associated with a response identifier; retrieving, from the cache database, associated with the response identifier. By doing so if the identity identifier of the face is not determined in the face image database, a new identity identifier may be allocated. Ju [005]
Kyakuno teaches receive, from the user, (received by the user [0037]) a relevance datum (The display in (b-2) of FIG. 5 is the same as that of (b-1) of FIG. 5, but no check box 107 is displayed in the confirmation field 103, and words "do you want to confirm by the administrator?", a "yes" button, and a "no" button are displayed in the confirmation dialog 104 [0136]) representing approval (transmission disapproval [0107])or disapproval (transmission disapproval [0107]) of the first natural-language response; (response to a question received by the user device 10 from a user, [0037]) store ( stores various types of information and data for the processing portion 20 to perform processing. [0053]) the relevance datum(The display in (b-2) of FIG. 5 is the same as that of (b-1) of FIG. 5, but no check box 107 is displayed in the confirmation field 103, and words "do you want to confirm by the administrator?", a "yes" button, and a "no" button are displayed in the confirmation dialog 104 [0136]) in association with the response identifier (answer sentence [0050]) such as “response identifier” in the vector database; and store ( stores various types of information and data for the processing portion 20 to perform processing. [0053]) the query vector as a database vector ((vectorizes) the query into a feature vector. [0056]) in the vector database(vector database (DB) 32. [0032]) in association with the response identifier (answer sentence [0050]) such as “response identifier” of the retrieved first natural-language response. (response to a question received by the user device 10 from a user, [0037])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju to incorporate the teachings of Kyakuno receive, from the user, a relevance datum representing approval or disapproval of the first natural-language response; store the relevance datum in association with the response identifier in the vector database; and store the query vector as a database vector in the vector database in association with the response identifier of the retrieved first natural-language response. By doing so The confirmation portion 24 may also transmit the search result to the administrator device 40 via the communication device 13 and receive a search result detennined as transmittable by the administrator device 40 from the administrator device 40. Kyakuno [0058]
Regarding claim 19 Miller in view of Ju and Kyakuno teaches the system of claim 18,
Miller does not fully disclose wherein each response identifier is associated with one or more database vectors stored in the vector database and a single natural-language response stored in the cache database.
Ju teaches wherein each of the plurality of response identifiers (identity identifier [0055])is associated with a plurality of database vectors (matching vector [0057])stored in the vector database (face image database [0049]) such as “vector database” and a single natural-language response (determining an identity identifier that is of the target vector and that is recorded in the cache database as an identity identifier of a face in the (t+l) th frame of face image.) stored in the cache database. (cache database [0057])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Kyakuno to incorporate the teachings of Ju wherein each response identifier is associated with one or more database vectors stored in the vector database and a single natural-language response stored in the cache database. By doing so The foregoing t'h frame of face image may be the first frame of face image of the N frames of face images, or may be any face image, other than the last frame of face image, of the N frames of face images. Ju [0058].
Claims 5 - 14 and 21 – 23 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (United States Patent Publication Number 20250139160), hereinafter Miller, in view of Ju et al. (United States Patent Publication Number 20170300744 ), hereinafter referred to as Ju, in view of Kazuki KYAKUNO(United States Patent Publication Number 2025/0103627) hereinafter Kyakuno and in further view of Kumar et al. (United States Patent Publication Number 20210382893 ), hereinafter referred to as Kumar.
Regarding claim 5 Miller in view of Ju and Kyakuno and Kumar teaches the method of claim 1
Miller as modified further teaches and storing, (Fig. 5 (520) store in database []) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) in the vector database; (vector database [0169]) in association with (association with [0154]) the response identifier (response schedule [0031], [0035], [0061], [0084], [0139]) such as “response identifier” of the first natural-language response(Fig. 5, (512) return cached response [0164])
Miller does not fully disclose and further comprising: receiving, by the processor, a relevance datum representative of the relevance indicator; the relevance datum in association with the response identifier of the first natural-language response
Kumar teaches receiving, (receiving [0021]) by the processor, (one or more processors [0101]) a relevance datum (Fig. 5B (508) “Didn’t know encryption”, “Wrong contact information”, “Left company”, “Left Crypto team”, “Too busy to take to me”, “Other” [0070]) such as “relevance datum” representative of the relevance indicator; (“correct”, “helpful”, “available”, and so forth. [0056]) SEE EXAMPLE Fig. 5A (502) “Was Roy Smith helpful with regard to encryption”” “Yes”, “No” [0060] the relevance datum(Fig. 5B (508) “Didn’t know encryption”, “Wrong contact information”, “Left company”, “Left Crypto team”, “Too busy to take to me”, “Other” [0070]) such as “relevance datum”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller to incorporate the teachings of Kumar whereby receiving, by the processor, a relevance datum representative of the relevance indicator; the relevance datum in association with the response identifier of the first natural-language response. By doing so data is made available that may help answer the user's query. Kumar [0006]
Regarding claim 6 Miller in view of Ju, Kyakuno and Kumar teaches the method of claim 5
Miller as modified further teaches and further comprising: storing, (store [0063]) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) the first vector embedding (first vector converted from the first request or query [0182]) in association with (association with [0154]) the response identifier (response schedule [0031], [0035], [0061], [0084], [0139]) such as “response identifier” of the first natural-language response (Fig. 5, (518) receive LLM response [0164]) NOTE “this response is from the language model” in the vector database. (vector database [0169])
Regarding claim 7 Miller in view of Ju, Kyakuno and Kumar teaches the method of claim 5
Miller as modified further teaches and further comprising: storing, (store [0063]) by the processor (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) the first natural-language response and corresponding response identifier (response schedule [0031], [0035], [0061], [0084], [0139]) such as “response identifier” of the first natural-language response (Fig. 5, (512) cached response [0164]) in the cache database; (stored in a system data store (e.g., a database),[0196]) such as a “cached database” and storing, (store [0063]) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) the first vector embedding (first vector converted from the first request or query [0182]) and associated response identifier (response schedule [0031], [0035], [0061], [0084], [0139]) such as “response identifier” of the first natural-language response (Fig. 5, (518) receive LLM response [0164]) NOTE “this response is from the language model” in the vector database. (vector database [0169])
Miller does not fully disclose in response to receiving a relevance datum representing approval of the first natural-language response,
Kumar teaches in response to receiving (receiving [0117]) a relevance datum (“correct”, “helpful”, “available”, and so forth. [0056]) SEE EXAMPLE Fig. 5A (502) “Was Roy Smith helpful with regard to encryption”” “Yes”, “No” [0060] representing approval (an indication of approval [0117]) of the first natural-language response, (that the expert in association with which feedback manager 810 detected a user interaction
was "helpful." [0109])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju , Kyakuno to incorporate the teachings of Kumar wherein in response to receiving a relevance datum representing approval of the first natural-language response. By doing so Feedback manager 810 can provide the received feedback information to expert selection model manager 804 for use in retraining the expert selection model. Kumar [0109].
Regarding claim 8 Miller in view of Ju, Kyakuno and Kumar teaches the method of claim 5
Miller as modified further teaches and further comprising repeating the step (in a loop [0031], [0035], [0084], [0139], of retrieving, (retrieve [0126]) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) the first natural-language response (Fig. 5, (512) return cached response [0164]) to the second natural language prompt (corresponding to a corresponding previously received request or query [0182]) from the cache database (in a system data store (e.g., a database),[0196]) such as a “cached database” for a plurality of natural-language prompts (many recommendation queries from different users (and/or prompts comprising user queries) [0067]) received from a plurality of users (different users [0067])
Regarding claim 9 Miller in view of Ju, Kyakuno and Kumar teaches the method of claim 8
Miller as modified further teaches storing, (store [0063]) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) in association with the response identifier(response schedule [0031], [0035], [0061], [0084], [0139]) such as “response identifier” for the first natural-language response (Fig. 5, (518) receive LLM response [0164]) NOTE “this response is from the language model” in the vector database. (vector database [0169])
Miller does not fully disclose a plurality of relevance indicators
Kumar teaches a plurality of relevance indicators (“correct”, “helpful”, “available”, and so forth. [0056]) SEE EXAMPLE Fig. 5A (312a) “Was Roy Smith helpful with regard to encryption”” “Yes”, “No” [0060] (“expert”, “most knowledgeable person” [0034]) such as “relevance indicators”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju, Kyakuno to incorporate the teachings of Kumar whereby a plurality of relevance indicators. By doing so a user who is associated with one or more data sources indicating he or she is knowledgeable with regard to a certain topic is determined. Kumar [0034].
Regarding claim 10 Miller in view of Ju, Kyakuno and Kumar teaches the method of claim 4
Miller as modified further teaches and further comprising producing, (producing [0023]) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) an alternative natural language response (more accurate responses [0045]) to the first natural language prompt (Fig. 5, (518) receive LLM response [0164]) NOTE “this response is from the language model”
Miller as modified does not fully disclose when the user has selected the relevance indicator representing disapproval of the first natural-language response.
Kumar teaches producing, when the user (a user [0112]) has selected the relevance indicator representing disapproval (Fig. 4B, selection of disapproval button [0066]) of the first natural-language response. (Fig. 4B, “remove me” [0066])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju, Kyakuno to incorporate the teachings of Kumar when the user has selected the relevance indicator representing disapproval of the first natural-language response. By doing so predicted expert identifications can be determined. Kumar [0114].
Regarding claim 11 Miller in view of Ju, Kyakuno and Kumar teaches the method of claim 10
Miller as modified further teaches wherein producing the alternative natural-language response (more accurate responses [0045]) comprises: identifying a third vector embedding (third vector [0182]) representative of a third natural-language prompt (converted second request [0182]) and having a similarity score (similarity scores [0070]) with the first vector embedding (the first vector [0182]) above the defined threshold (similarity scores above the threshold [0070]) and closest
(sufficiently close [0182]) to the second vector embedding (second vector [0182]) similarity score; (similarity score [0164]) and retrieving, by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) the alternative natural-language response (more accurate responses [0045]) to the third natural-language prompt (plurality of prompts [0186]) from the cache database, (in a system data store (e.g., a database),[0196]) such as a “cached database” wherein the alternative natural-language response (more accurate responses [0045]) differs from the first natural-language response. (Fig. 5, (512) return cached response [0164])
Regarding claim 12 Miller in view of Ju, Kyakuno and Kumar teaches the method of claim 11
Miller as modified further teaches and storing, (store [0063]) by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) the first vector embedding(the first vector [0182]) and associated response identifier (response schedule [0031], [0035], [0061], [0084], [0139]) such as “response identifier” of the alternative natural-language response (more accurate responses [0045]) in the vector database. (vector database [0169])
Miller does not fully disclose and further comprising: requesting, by the processor, the user to approve of or disapprove of the alternative natural-language response, based on content of the alternative natural-language response and relevance to the alternative natural-language prompt, by selecting the relevance indicator on the user device representing approval or disapproval; receiving, by the processor, the relevance indicator; in response to receiving a relevance datum representing approval of the alternative natural-language response,
Kumar teaches requesting, (requesting [0140]) by the processor, (one or more processors [0101]) the user (the use [0109]) to approve of or disapprove of (an indication of approval or an indication of disapproval [0117]) the alternative natural-language response, (identified experts [0045]) such as “alternate natural language response” based on content (one or more key topics [0050]) of the alternative natural-language response (identified experts [0045]) such as “alternate natural language response” and relevance (based on a level of relevance [0079]) to the alternative natural-language prompt, (Fig. 6 (608) prior expert assistance query [0075]) by selecting (selection [0117]) the relevance indicator(“correct”, “helpful”, “available”, and so forth. [0056]) SEE EXAMPLE Fig. 5A (312a) “Was Roy Smith helpful with regard to encryption”” “Yes”, “No” [0060] (“expert”, “most knowledgeable person” [0034]) such as “relevance indicators” on the user device (client device [0128]) representing approval or disapproval; (an indication of approval or an indication of disapproval [0117]) receiving, by the processor, (processor [0128]) the relevance indicator; (“correct”, “helpful”, “available”, and so forth. [0056]) SEE EXAMPLE Fig. 5A (312a) “Was Roy Smith helpful with regard to encryption”” “Yes”, “No” [0060] (“expert”, “most knowledgeable person” [0034]) such as “relevance indicators” in response to receiving (in response to receiving [0021]) a relevance datum (“correct”, “helpful”, “available”, and so forth. [0056]) SEE EXAMPLE Fig. 5A (502) “Was Roy Smith helpful with regard to encryption”” “Yes”, “No” [0060] representing approval (an indication of approval [0117]) of the alternative natural-language response (identified experts [0045]) such as “alternate natural language response”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju, Kyakuno to incorporate the teachings of Kumar requesting, by the processor, the user to approve of or disapprove of the alternative natural-language response, based on content of the alternative natural-language response and relevance to the alternative natural-language prompt, by selecting the relevance indicator on the user device representing approval or disapproval; receiving, by the processor, the relevance indicator; in response to receiving a relevance datum representing approval of the alternative natural-language response. By doing so identify and provide information associated with users who are most knowledgeable about the particular topic. Kumar [0006]
Regarding claim 13 Miller in view of Ju, Kyakuno and Kumar teaches the method of claim 4
Miller as modified further teaches generating, by a language model (Fig. 5, (518) receive LLM response [0164]) NOTE “this response is from the language model” executed by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) an alternative natural-language response (more accurate responses [0045]) to the first natural-language prompt (first request or query [0182]) such as “a first natural-language prompt” SEE EXAMPLES "Surprise me with a content recommendation," "provide random recommendations." [0149], "I'm in the mood for food shows" [0174], "pick up where I left off', "access watchlist," "surprise me," "let's watch something new." [ 0177]
Miller does not fully disclose and further comprising, in response to the user selecting the relevance indicator representing disapproval of the first natural-language response
Kumar teaches in response to the user selecting the relevance indicator representing disapproval (a "remove me" or disapproval button 406 as part of expanded notification 402. [0066]) SEE FIGS 5A & 5B of the first natural-language response (any one of Fig. 5B (508) “Didn’t know encryption”, “Wrong contact information”, “Left company”, “Left Crypto team”, “Too busy to take to me”, “Other” [0070])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju, Kyakuno to incorporate the teachings of Kumar wherein in response to the user selecting the relevance indicator representing disapproval of the first natural-language response. By doing so In response to a detected selection of the approval button 404, content management system 104 can store training information indicating that the user of client computing device 106b has confirmed him or herself as an expert associated with the identified key topic. Kumar [0066]
Regarding claim 14 Miller in view of Ju, Kyakuno and Kumar teaches the method of claim 13
Miller as modified further teaches the alternative natural-language response (more accurate responses [0045]) and corresponding response identifier (response schedule [0031], [0035], [0061], [0084], [0139]) such as “response identifier” in a cache database; (in a system data store (e.g., a database),[0196]) such as a “cached database” and storing, by the processor, (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) the first vector embedding (the first vector [0182]) and associated response identifier (response schedule [0031], [0035], [0061], [0084], [0139]) such as “response identifier” of the alternative natural-language response(more accurate responses [0045]) in the vector database (vector database [0169])
Miller does not fully disclose requesting, by the processor, the user to approve of or disapprove of the alternative natural-language response, based on content of the alternative natural-language response and relevance to the alternative natural-language prompt, by selecting the relevance indicator on the user device representing approval or disapproval; receiving, by the processor, a relevance datum representative of the relevance indicator; storing, by the processor in response to receiving a relevance datum representing approval of the alternative natural-language response
Kumar teaches and further comprising: requesting,(requesting [0140]) by the processor, (processor [0128]) the user (the user [0140]) to approve of or disapprove (an indication of approval or an indication of disapproval [0117]) the alternative natural-language response, (identified experts [0045]) such as “alternate natural language response” based on content (one or more key topics [0050]) of the alternative natural-language response (identified experts [0045]) such as “alternate natural language response” and relevance (based on a level of relevance [0079]) to the alternative natural-language prompt, (Fig. 6 (608) prior expert assistance query [0075]) by selecting the relevance indicator (“correct”, “helpful”, “available”, and so forth. [0056]) SEE EXAMPLE Fig. 5A (312a) “Was Roy Smith helpful with regard to encryption”” “Yes”, “No” [0060] (“expert”, “most knowledgeable person” [0034]) such as “relevance indicators” on the user device (client device [0128]) representing approval or disapproval; (an indication of approval or an indication of disapproval [0117]) receiving, by the processor, (processor [0128]) a relevance datum (Fig. 5B (508) “Didn’t know encryption”, “Wrong contact information”, “Left company”, “Left Crypto team”, “Too busy to take to me”, “Other” [0070]) such as “relevance datum” representative of the relevance indicator; (“correct”, “helpful”, “available”, and so forth. [0056]) SEE EXAMPLE Fig. 5A (502) “Was Roy Smith helpful with regard to encryption”” “Yes”, “No” [0060] storing, (store [0137]) by the processor (processor [0128])in response to (in response to [0117]) receiving a relevance datum(Fig. 5B (508) “Didn’t know encryption”, “Wrong contact information”, “Left company”, “Left Crypto team”, “Too busy to take to me”, “Other” [0070]) such as “relevance datum” representing approval of (an indication of approval [0117]) the alternative natural-language response, (identified experts [0045]) such as “alternate natural language response”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju, Kyakuno to incorporate the teachings of Kumar wherein requesting, by the processor, the user to approve of or disapprove of the alternative natural-language response, based on content of the alternative natural-language response and relevance to the alternative natural-language prompt, by selecting the relevance indicator on the user device representing approval or disapproval; receiving, by the processor, a relevance datum representative of the relevance indicator; storing, by the processor in response to receiving a relevance datum representing approval of the alternative natural-language response. By doing so for display on the client device, information associated with the prior expert along with the information associated with the identified expert. Kumar [0117]
Regarding claim 21 Miller in view of Ju and Kyakuno teaches the system of claim 1,
Miller as modified teaches a in the vector database (vector database [0068])
Miller does not fully disclose wherein the processor is further configured to store the relevance datum in association with the response identifier
Ju teaches in association with the response identifier (identity identifier [0068])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller to incorporate the teachings of Ju wherein response identifier. By doing so an identity identifier may be determined. Ju [0066]
Kumar teaches store (store [0058]) the relevance datum(Fig. 5B (508) “Didn’t know encryption”, “Wrong contact information”, “Left company”, “Left Crypto team”, “Too busy to take to me”, “Other” [0070]) such as “relevance datum”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju, Kyakuno to incorporate the teachings of Kumar wherein in relevance datum. By doing so a predicted expert can be received. Kumar [0059]
Regarding claim 22 Miller in view of Ju, Kyakuno teaches the system of claim 18,
Miller as modified further teaches wherein the processor (one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) is further configured to store the query vector (first vector converted from the first request or query [0182]) as a database vector in the vector database in association with the response identifier of the retrieved first natural-language response (vectors stored in a vector database corresponding to previously received queries or requests [0182])
Regarding claim 23 Miller in view of Ju and Kyakuno teaches the system of claim 18,
Miller as modified further teaches wherein the processor(one or more graphics processing units (GPUs) and/or artificial intelligence-specific processing devices (which may be referred to as an AI processor). [0048]) is further configured to store the query vector(first vector converted from the first request or query [0182]) as a database vector in the vector database (vectors stored in a vector database corresponding to previously received queries or requests [0182])and store the first-natural language response (a response previously provided in response to the corresponding previously received request or query; [0182]) when the first natural-language response(a response previously provided in response to the corresponding previously received request or query; [0182]) is generated (generated [0186]) by the language model; (large language model [0182]) of the first-natural language response. (a response previously provided in response to the corresponding previously received request or query; [0182])
Ju teaches in the cache database (cache database [0057])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller to incorporate the teachings of Ju wherein in the cache database. By doing so the cache database is first accessed. Ju [0058]
Kumar teaches and when the relevance datum received (Fig. 5B (508) “Didn’t know encryption”, “Wrong contact information”, “Left company”, “Left Crypto team”, “Too busy to take to me”, “Other” [0070]) such as “relevance datum” represents approval (FIG. 4B, content management system 104 can include a "got it" or approval button 404 [0066])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miller in view of Ju, Kyakuno to incorporate the teachings of Kumar wherein and when the relevance datum received represents approval. By doing so In response to a detected selection of the approval button 404, content management system 104 can store training information indicating that the user of client computing device 106b has confirmed him or herself as an expert associated with the identified key topic. Kumar [0066]
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.
John Thomas Janz (United States Patent Publication Number 20180025303) teaches “computerized content similarity analysis of computer stored communications authored by employees of a corporation with example sets of stored communications
assembled to represent specific types of competent or counter-productive communications which result in data tables and maps that facilitate personal behavioral change coaching and corporate personnel decision making based on an objective
analysis of mathematical properties of the example set communications compared to those same properties computed for each instance of an employee digital communication. [0139]”
8. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Kweku Halm whose telephone number is (469) 295-
9144. The examiner can normally be reached on 7:30AM - 5:30PM Mon - Thur. If
attempts to reach the examiner by telephone are unsuccessful, the examiner's
supervisor, Sanjiv Shah 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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/KWEKU WILLIAM HALM/Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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