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 .
DETAILED ACTION
The action is in response to claims dated 12/8/2023
Claims pending in the case: 1-20
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Independent claim 18 recites a “An artificial intelligence (AI) computing system, comprising: a plurality of different AI models”. However, such models are software per se. There is no associated structural component within the claimed limitations, and as such the claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter.
All claims dependent on this/these claims, is/are also rejected due to their direct or indirect dependencies.
Claim(s) 1-4, 11-13 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim 1 includes the following recitation of an abstract idea:
searching …, based on the input query, for a matching … entry (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.)
generating a response to the input query … based on the first model output and the second model output (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
receiving an input query at an artificial intelligence (AI) system, the AI system having a first content provider and a second content provider (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions) ;
searching a cache store (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
extracting, from the matching cache entry, a first model output generated by the first content provider and a second model output generated by the second content provider (This is receiving model data and is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
providing the first model output and the input query to the first content provider (This is inputting data and is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions) ;
a second content provider; providing the second model output and the input query to the second content provider (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
query from the AI system based on the first model output and the second model output (This high level recitation of the machine learning model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).);
These claimed limitations therefore do not integrate the abstract idea into a practical application.
Independent Claims 11, includes the following recitation of an abstract idea:
generating a first cache entry in a cache store corresponding to the first input query, the first cache entry including a first key value generated based on the first input query, and a first content portion including the first model output and the second model output (This is analyzing and formatting data and practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.)
Claim 11 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
receiving a first input query at an artificial intelligence (AI) system, the AI system having a first content provider and a second content provider; generating a first model output with the first content provider based on the first input query; generating a second model output with the second content provider based on the first input query (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
The above independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Dependent claim 2-4 pertain to providing or routing information (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
The dependent claims 12 pertain to data type (This appears to be directed to the specification of data and a restriction to a particular type of data. This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Dependent Claims 13, are similar in scope as claim 1 and therefore rejected under the same rationale.
Hence these claims are rejected as being abstract.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bo (US 20230138987) in view of Fu (GPTCache: An Open-Source Semantic Cache for LLM Applications Enabling Faster Answers and Cost Savings).
Regarding Claim 1, Bo teaches, A computer implemented method, comprising:
receiving an input query at an artificial intelligence (AI) system, the AI system having a first content provider and … (Bo: Fig. 2: [16, 20]: receive inference request; as illustrated in Fig. 2, the system consists of model (content provider) to calculate prediction. It is obvious that cache update may use multiple models based on the data stored in the cache);
searching a cache store, based on the input query, for a matching cache entry (Bo: Fig. 2: [16, 21]: search for similar cache entry);
extracting, from the matching cache entry, a first model output generated by the first content provider and a second model output generated by the second content provider (Bo: Fig. 2: [16, 21, 25]: extract plurality of cache data (first, second…) and compute similarity score);
providing the first model output and the input query to the first content provider (Bo: Fig. 2: [16, 22]: if threshold not met information provided to the model (content provider) to calculate prediction);
…; and
generating a response to the input query from the AI system based on the first model output and the second model output (Bo: Fig. 2: [16, 24]: return a prediction based on similarity of model outputs in cache);
Bo does not specifically recite, a second content provider; providing the second model output and the input query to the second content provider;
However Bo teaches an inference cache storing a plurality of inferences for input queries of a plurality of models. Bo in Fig. 2 also illustrates and explains that if the threshold is not met, the corresponding model is used to generate the output and update the cache. Hence it would be obvious to one skilled in the art that a system to update the cache storing data of more than one model would use more than one models to update the data.
The examiner also notes here that the limitations – “providing the first model output and the input query to the first content provider; and providing the second model output and the input query to the second content provider” do not add to the functionality being claimed. As per the limitations, the input is provided but the output is not used. Hence these appear to be redundant and hence the limitations as claimed is obvious over the teachings in Bo;
Nonetheless, Fu teaches, a second content provider; providing the second model output and the input query to the second content provider (Fu: Pg, 214 Fig. 1: Illustration of a system with multiple LLMs to update Cache);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Bo and Fu because the combination would enable using a second model to update cache. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve response time (see Fu: Abstract)
Regarding claim 2, Bo and Fu teach the invention as claimed in claim 1 above and, wherein generating a response comprises: validating the first model output with the first content provider to obtain a first validated model output; and providing the first validated model output to a response orchestrator (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module and return prediction (orchestrator)) (Fu: Pg, 214 Fig. 1: LLM Adapter).
Regarding claim 3, Bo and Fu teach the invention as claimed in claim 2 above and, wherein generating a response comprises: validating the second model output with the second content provider to obtain a second validated model output; and providing the second validated model output to the response orchestrator (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module (orchestrator)) (Fu: Pg, 214 Fig. 1, section 3.1: LLM Adapter).
Regarding claim 4, Bo and Fu teach the invention as claimed in claim 3 above and, wherein generating a response comprises: selecting from the first validated model output and the second validated model output, using the response orchestrator, to generate the response (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module (orchestrator)) (Fu: Pg, 214 Fig. 1, section 3.1: LLM Adapter).
Regarding claim 5, Bo and Fu teach the invention as claimed in claim 1 above and, and further comprising:
generating a first cache entry, the first cache entry in the cache store comprising a first key value indicative of a semantic representation of a first cached query, the first cache entry further comprising first cache entry content including a first model output generated by the first content provider for the first cached query and a second model output generated by the second content provider for the first cached query (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module; [10]: input values and associated inference (key-value)) (Fu: Pg. 212 col 2 [3], Pg, 214 Fig. 1, section 3.1, Pg. 216 col 1 [2]: semantic caching (key value pairs)); and
generating a second cache entry, the second cache entry in the cache store comprising a second key value indicative of a semantic representation of a second cached query, the second cache entry further comprising second cache entry content including a first model output generated by the first content provider for the second cached query and a second model output generated by the second content provider for the second cached query (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module; [10]: input values and associated inference (key-value)) (Fu: Pg. 212 col 2 [3], Pg, 214 Fig. 1, section 3.1, Pg. 216 col 1 [2], col 2 [1]: semantic caching (key value pairs)).
Regarding claim 6, Bo and Fu teach the invention as claimed in claim 5 above and, wherein searching the cache store comprises:
generating a semantic representation of the input query (Fu: pg. 216 col 2 [1]: semantic similarity); and
comparing the semantic representation of the input query to the first key value in the first cache entry and the second key value in the second cache entry to identify a closest cache entry (Fu: pg. 216 col 2 [1]: semantic similarity to query-response pair).
Regarding claim 7, Bo and Fu teach the invention as claimed in claim 6 above and, wherein first key value comprises a first vector and wherein the second key value comprises a second vector (Fu: Pg. 214 col 2 section 3.4: store as vectors) and wherein generating a semantic representation of the input query comprises: generating an input vector corresponding to the input query (Fu: Pg. 216 col 2 section 5: input feature vectors).
Regarding claim 8, Bo and Fu teach the invention as claimed in claim 7 above and, wherein comparing the semantic representation of the input query to the first key value in the first cache entry and the second key value in the second cache entry (Fu: pg. 216 col 2 [1]: semantic similarity to query-response pair) comprises:
measuring a first distance between the input vector and the first vector; and measuring a second distance between the input vector and the second vector (Fu: pg. 216 col 2 section 5 [2]: vector distance) (Bo: [21]: Euclidean distance to calculate cache prediction).
Regarding claim 9, Bo and Fu teach the invention as claimed in claim 8 above and, wherein comparing the semantic representation of the input query to the first key value in the first cache entry and the second key value in the second cache entry comprises:
identifying a closest cache entry based on the shortest distance, of the first distance and the second distance; and comparing the shortest distance to a distance threshold to determine whether the shortest distance meets the distance threshold (Fu: pg. 216 col 2 section 5 [2]: vector distance; Pg. 216 col 1: similarity threshold) (Bo: [21-23]: Euclidean distance to calculate cache prediction; threshold).
Regarding claim 10, Bo and Fu teach the invention as claimed in claim 9 above and, and further comprising: if the shortest distance meets the distance threshold, then identifying the closest cache entry as the matching cache entry (Fu: pg. 216 col 2 section 5 [2]: find match; Pg. 216 col 1: similarity threshold) (Bo: [20-23]: Identify match).
Regarding Claim 11, Bo teaches, a computer implemented method, comprising: receiving a first input query at an artificial intelligence (AI) system, the AI system having a first content provider and a second content provider;
generating a first model output with the first content provider based on the first input query (Bo: [10]: cache with input values and associated inference generated using a plurality of models; Fig. 2, [23]: generating using model and adding to cache) ;
generating a second model output with the second content provider based on the first input query (Bo: [10]: cache with input values and associated inference generated using a plurality of models; Fig. 2, [23]: generating using model and adding to cache); and
generating a first cache entry in a cache store corresponding to the first input query (Bo: [10]: cache with input values and associated inference generated using a plurality of models; Fig. 2, [23]: generating using model and adding to cache), the first cache entry including a first key value generated based on the first input query, and a first content portion including the first model output and the second model output (Bo: [10]: input values and associated inference (key-value));
Although Bo does not use the term key value, it is obvious that the query-inference data in cache may be key-value pairs;
Nonetheless, Fu teaches, key values (Fu: Pg. 212 col 2 [3], Pg, 214 Fig. 1, section 3.1, Pg. 216 col 1 [2], col 2 [1]: semantic caching (key value pairs));
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Bo and Fu because the combination would enable using semantic caching with key value pairs for inference models. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve response time (see Fu: Abstract)
Regarding claim 12, Bo and Fu teach the invention as claimed in claim 11 above and, wherein generating the first cache entry comprises: generating, as the first key value, a semantic representation of the first input query (Fu: pg. 216 col 2 [1]: semantic representation used for similarity).
Regarding claim 13, Bo and Fu teach the invention as claimed in claim 12 above and, and further comprising:
receiving second input query (Bo: Fig. 2: [16, 20]: receive inference request);
searching the cache store, based on the second input query; identifying the first cache entry as a matching cache entry (Bo: Fig. 2: [16, 21]: search for similar cache entry);
extracting, from the matching cache entry, the first model output generated by the first content provider and the second model output generated by the second content provider (Bo: Fig. 2: [16, 21, 25]: extract plurality of cache data (first, second…) and compute similarity score);
providing the first model output and the second input query to the first content provider (Bo: Fig. 2: [16, 22]: if threshold not met information provided to the model (content provider) to calculate prediction);
providing the second model output and the second input query to the second content provider (Bo: Fig. 2: [16, 22]: if threshold not met information provided to the model (content provider) to calculate prediction) (Fu: Pg, 214 Fig. 1: Illustration of a system with multiple LLMs to update Cache); and
generating a response to the second input query from the AI system based on the first model output and the second model output (Bo: Fig. 2: [16, 24]: return a prediction based on similarity of model outputs in cache).
Regarding claim 14, Bo and Fu teach the invention as claimed in claim 13 above and, wherein generating a response comprises: validating the first model output with the first content provider to obtain a first validated model output; and providing the first validated model output to a response orchestrator (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module and return prediction (orchestrator)) (Fu: Pg, 214 Fig. 1: LLM Adapter).
Regarding claim 15, Bo and Fu teach the invention as claimed in claim 14 above and, wherein generating a response comprises: validating the second model output with the second content provider to obtain a second validated model output; and providing the second validated model output to the response orchestrator (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module and return prediction (orchestrator)) (Fu: Pg, 214 Fig. 1: LLM Adapter).
Regarding claim 16, Bo and Fu teach the invention as claimed in claim 15 above and, wherein generating a response comprises: selecting from the first validated model output and the second validated model output, using the response orchestrator, to generate the response (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module (orchestrator)) (Fu: Pg, 214 Fig. 1, section 3.1: LLM Adapter).
Regarding claim 17, Bo and Fu teach the invention as claimed in claim 16 above and, wherein the cache store has a plurality of cache entries, each cache entry having a key value and a content portion and wherein searching the cache store comprises:
generating a semantic representation of the second input query; and comparing the semantic representation of the second input query to the key value corresponding to each cache entry to identify a closest cache entry (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module) (Fu: Pg. 212 col 2 [3], Pg, 214 Fig. 1, section 3.1, Pg. 216 col 1 [2], col 2 [1]: semantic caching (key value pairs)).
Regarding Claim 18, Bo teaches, An artificial intelligence (AI) computing system, comprising: a plurality of different AI models each configured to generate a different model output based on a first input query (Bo: Fig. 2: [16, 20]: A system consists of cache with data and models to calculate prediction and update cache);
a response generator configured to receive the different model output generated by each of the plurality of different AI models and generate a response based on the different model outputs (Bo: Fig. 2: [16, 21, 24]: receive model outputs in cache and search for similar cache entry to generate response); and
a cache generator configured to generate a cache entry corresponding to the first input query, the cache entry including the different model output generated by each of the plurality of different AI models (Bo: Fig. 2: [16, 25-26]: validate prediction and update cache using self-learning module);
Although it is obvious that the data in cache is generated by a plurality of different models, Bo does not specifically mention a plurality of models;
Nonetheless, Fu teaches, a second content provider; providing the second model output and the input query to the second content provider (Fu: Pg, 214 Fig. 1: Illustration of a system with multiple LLMs to update Cache);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Bo and Fu because the combination would enable using a second model to update cache. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve response time (see Fu: Abstract)
Regarding claim 19, Bo and Fu teach the invention as claimed in claim 18 above and, wherein the cache generator comprises: a semantic encoder configured to generate a semantic representation of the first input query and to generate, as part of the cache entry, the semantic representation of the first input query (Bo: Fig. 2: [16, 20, 25-26]: encoder generating input representation) (Fu: Pg. 212 col 2 [3], Pg, 214 Fig. 1, section 3.1, Pg. 216 col 1 [2], col 2 [1]: semantic caching (key value pairs)).
Regarding claim 20, Bo and Fu teach the invention as claimed in claim 19 above and, wherein the semantic encoder is configured to receive a second input query and generate a semantic representation of the second input query (Bo: Fig. 2: [16, 20, 25-26]: encoder generating input representation) (Fu: Pg. 212 col 2 [3], Pg, 214 Fig. 1, section 3.1, Pg. 216 col 1 [2], col 2 [1]: semantic caching (key value pairs)), and further comprising: a search system configured to compare the semantic representation of the second input query with the semantic representation of the first input query in the cache entry to identify a matching cache entry (Fu: pg. 216 col 2 [1]: semantic similarity to query-response pair).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
Prosecution Insights