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 .
Response to Amendment
2. The Amendment filed on November 5th, 2025 has been entered. Claims 1, 16, 19 and 22 have been amended. Claims 1 - 25 are currently pending.
Response to Arguments
35 U.S.C. §103
3. Applicant's arguments, see Remarks pp. 9 -13, filed November 5th, 2025, with
respect to the rejections of claims 1 – 25 under 35 U.S.C. §103 have been fully
considered and they are not persuasive.
Independent claim 1
Applicant argues that Miller does not teach “a plurality of vector embeddings stored in association with a response identifier” in a vector database.
Examiner respectfully disagrees. The Miller reference in paragraph [0182] teaches, “ … vectors stored in a vector database corresponding to previously received queries or requests …” These vectors are compared to a converted first vector from a query or request and the resulting match or closest match is determined to correspond to the response or query received.
Independent claim 18
Applicant argues that no combination of the prior art of record teaches storing vector embeddings in association with response identifiers in a vector database or storing natural language responses in association with the response identifier in a cache database
Examiner respectfully disagrees. Miller in paragraph [0182] teaches “receive over a network via the network interface a first request or query from a first device associated with a first user; determine, with respect to the received first request or query, whether there is a same or similar request or query stored in memory by at least:
converting the received first request or query to a first vector; performing a similarity search between the first vector of the received request or query and vectors stored in a vector database corresponding to previously received queries or requests; determining a distance between the first vector of the first received request or query and at least a second vector stored in the vector database corresponding to a corresponding previously received request or query; based at least in part on the determined distance between the first vector of the received first request or query and the second vector stored in the vector database corresponding to the corresponding previously received request or query, determining if the second vector stored in the vector database corresponding to the corresponding previously received request or query is sufficiently close to the first vector of the received first request or query based at least in part on a first metric; at least partly in response to determining that the second vector stored in the vector database corresponding to the corresponding previously received request or query is sufficiently close to the first vector of the received first request or query, accessing a response previously provided in response to the corresponding previously received request or query; transmit the accessed response, previously provided in response to the corresponding previously received request or query ”
From the foregoing, Miller teaches a first request query, which corresponds to “a natural language response”, this query is converted to a first vector, which corresponds to a “response identifier” and then a determination is made if the request or query is in memory, and this memory corresponds to a “cache database” and finally the second vector embedding may correspond to “any one of the vectors stored in the vector database”
Claim Rejections – 35 U.S.C. §103
4. 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.
5. 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, 24 and 25 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.
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
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]
Regarding claim 2 Miller in view of Ju teaches the method (method [0020], [0184] [0185], [0188]) of claim 1,
Miller as modified further teaches wherein producing (producing [0023]) the first natural-language response (Fig. 5, (518) receive LLM response [0164]) NOTE “this response is from the language model” comprises generating, (generating [0016]) by a language model (large language models (LLMs), [0019]) executed by (executed by [0182]) 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, (518) receive LLM response [0164]) NOTE “this response is from the language model” 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] when querying the vector database (Fig. 5, (508) query vector database [0157]) fails to identify (Fig. 5, (510) “within threshold closeness?” “NO” [0158]) the second vector embedding (second vector [0182])
Regarding claim 3 Miller in view of Ju teaches the method (method [0020], [0184] [0185], [0188])of claim 2,
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 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 teaches the method (method [0020], [0184] [0185], [0188])of claim 2,
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 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 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 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
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]
Regarding claim 19 Miller in view of Ju 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 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].
Regarding claim 24 Miller in view of Ju 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 separate (a transformer architecture that utilizes self-attention, which enables the model to
selectively focus on different parts of the input sequence during the encoding process [0039]) (The encoder is configured to receive an input sequence and process it using multi-head self-attention, where the input sequence is transformed into a set of query,
key, and value vectors. The query, key, and value vectors may be used to compute the attention scores between given positions in the sequence, enabling the model to identify the relevant (e.g., most relevant) portions of the input sequence for respective positions [0040])the first natural-language prompt(first request or query [0182]) such as “a first natural-language prompt” into a plurality of vector embeddings (a set of query, key, and value vectors. [0040]) when the first natural-language prompt(first request or query [0182]) such as “a first natural-language prompt” includes multiple parts (different parts of the input sequence [0039]) for which natural-language responses (LLM responses [0035]) will differ in content (patterns in the output sequences [0042])
Regarding claim 25 Miller in view of Ju 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: query the vector database (Fig. 5, (508) query vector database [0157]) using the query vector (new query vector [0071]) to identify a plurality of database vectors (vectors in the database [0071]) having a similarity score (cosine similarity, Euclidean distance, or Jaccard similarity. [0071])with the query vector(new query vector [0071] above a defined threshold, (above the threshold [0071]) retrieve, (retrieve [0126]) from the vector database, (vector database [0068]) a plurality 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])represented by the plurality of query vectors; (vectors stored in the vector database corresponding to previously received queries or requests; [0182]) provide, 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 list of the plurality 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]) to a user device; (via a user device [0184]) a natural-language response (The user may
then select a recommended content item. [0152] )associated with a natural-language prompt (For example, if the user request was "suggest some movies or shows that will cheer me up," the generated prompt to be provided to the AI engine may be as
follows: [0144]) ("Hi, congratulations, you're here to provide TV and movie recommendations to a VIP. You can pick from the following titles: { names of titles in the content subsets} [0145]) [0145] [0144])selected by a user (selecting respective subsets of content titles of different categories of content from a content library comprising a plurality of titles; [0184]) from the list of the plurality 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]) SEE ALSO [0145] – [0149]
Miller does not fully disclose wherein database vectors of the plurality of database vectors have different associated response identifiers; and retrieve, from the cache database
Ju teaches wherein database vectors of the plurality of database vectors (candidate vectors [0037]) have different (different video images [0087]) associated response identifiers; (identity identifiers [0055]) and retrieve, (selecting [0057]) from 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 database vectors of the plurality of database vectors have different associated response identifiers; and retrieve, from the cache database. By doing so it should be noted that the selecting a target vector from the cache database according to the original feature vector of the (t+l)th frame of face image may include:
selecting the target vector from the cache database according to a formula s=[v2, l]·(v* cf +v2-B ·v2r, where v2 represents the original feature vector of the (t+l)th frame of face image, v* c represents a vector in the cache database, and s represents a target distance between v2 and v* c ·Ju [0060]
Claims 4 - 15 and 20 – 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 and in further view of Kumar et al. (United States Patent Publication Number 20210382893 ), hereinafter referred to as Kumar.
Regarding claim 4 Miller in view of Ju teaches the method of claim 2
Miller does not fully disclose and further comprising 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.
Kumar teaches requesting, (requesting [0028]) by the processor, (Fig. 10, (1002) processor [0128]) the user (the user [0140]) to approve of (an indication of approval [0117]) or disapprove of (an indication of disapproval [0117]) the first natural-language response, (output of a predicted expert associated with the key topic. [0054]) such as “the first natural-language response” based on content (extracted one or more key topics (210) from the expert assistance query [0053]) of the first natural-language response (output of a predicted expert associated with the key topic. [0054]) such as “the first natural-language response” and relevance to (relevance to [0059]) the first natural-language prompt, (expert assistance query [0053]) such as “first natural-language prompt” by selecting (indication of a selection [0061]) a relevance indicator (“correct”, “helpful”, “available”, and so forth. [0056]) on a user device, (Fig. 1, (106a-d) client computing device [0041]) 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] representing approval or disapproval (FIG. 4B, content management system 104 can include a "got it" or approval button 404 and a "remove me" or disapproval button 406 as part of expanded notification 402. [0066]) SEE FIGS 5A & 5B
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 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. By doing so data is made available that may help answer the user's query. Kumar [0006]
Regarding claim 5 Miller in view of Ju and Kumar teaches the method of claim 4
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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 15 Miller in view of Ju and Kumar teaches the method of claim 4
Miller as modified further teaches and further comprising, wherein the first natural-language response is a response retrieved from the cache database: (Fig. 5, (512) return cached response [0164]) 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]) a list of suggested 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) each suggested natural language prompt ("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) having a vector embedding representative (vectors stored in a vector database [0182]) of the suggested natural-language prompt ("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 having a similarity score (similarity score [0071], [0164]) with the first vector embedding(the first vector [0182]) above the defined threshold; (above the threshold [0071]) and a request for the user to select (A prompt may be presented, ( e.g., "Hey there buddy! So happy you dropped in today. What would you like to do? Let me know if l can help"). [0177]) a suggested natural-language prompt (Optionally, a set of options may be presented to the user from which the user may select. [ 0177) from the list of suggested 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 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]) an alternative natural-language response (If the user selects "pick up where I left off', the system may initiate streaming the last content item the user was in the process of viewing, beginning at the point the user last stopped viewing. If the user selects "access watchlist," a watchlist of the user (in which the user has added content items for later viewing) may be accessed and displayed to the user via the user device. If the user selects "surprise me" the system may randomly or semi-randomly a content item and may stream the content item to the user device. [0177]) associated with the selected suggested natural-language prompt ("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 the cache database. (stored in a system data store (e.g., a database),[0196]) such as a “cached database”
Miller does not fully disclose in response to the user selecting the relevance indicator representing disapproval (an indication of disapproval [0117])of the first natural-language response,
Kumar teaches in response to the user (the user [0140]) 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” 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 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 content management system 104 can store training information indicating that the user of client
computing device 106b is not an expert associated with the identified key topic. Kumar [0066].
Regarding claim 20 Miller in view of Ju teaches the system of claim 18,
Miller as modified does not fully disclose wherein the processor is further configured to receive, from the user, a relevance datum representing approval or disapproval of the first natural-language response.
Kumar teaches wherein the processor (Fig. 10, (1002) processor [0128]) is further configured to receive, from the user (the user [0140]) 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 or disapproval (FIG. 4B, content management system 104 can include a "got it" or approval button 404 and a "remove me" or disapproval button 406 as part of expanded notification 402. [0066]) SEE FIGS 5A & 5B of the first natural-language response (received feedback [0056])
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 wherein the processor is further configured to receive, from the user, a relevance datum representing approval or disapproval of the first natural-language response. By doing so content management system 104 can utilize the received feedback to further retrain the expert selection model for increased speed and accuracy. Kumar [0056]
Regarding claim 21 Miller in view of Ju and Kumar teaches the system of claim 20,
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 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 and Kumar teaches the system of claim 21,
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 Kumar teaches the system of claim 20,
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 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
6. THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire
THREE MONTHS from the mailing date of this action. In the event a first reply is
filed within TWO MONTHS of the mailing date of this final action and the advisory action
is not mailed until after the end of the THREE-MONTH shortened statutory
period, then the shortened statutory period will expire on the date the advisory
action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be
calculated from the mailing date of the advisory action. In no event, however, will
the statutory period for reply expire later than SIX MONTHS from the date of this
final action.
Examiner interviews are available via telephone, in-person, and video
conferencing using a USPTO supplied web-based collaboration tool. To schedule an
interview, applicant is encouraged to use the USPTO Automated Interview Request
(AIR) at http://www.uspto.gov/interviewpractice.
7. 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 9:00AM - 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.
Information regarding the status of an application may be obtained from the
Patent Application Information Retrieval (PAIR) system. Status information for published
applications may be obtained from either Private PAIR or Public PAIR. Status information
for unpublished applications is available through Private PAIR only. For more
information about the PAIR system, see http://pair-direct.uspto.gov. Should you have
questions on access to the Private PAIR system, contact the Electronic Business Center
(EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer
Service Representative or access to the automated information system, call 800-786-
9199 (IN USA OR CANADA) or 571-272-1000.
/KWEKU WILLIAM HALM/Examiner, Art Unit 2166
/KHANH B PHAM/Primary Examiner, Art Unit 2166
Prosecution Insights