Notice of Pre-AIA or AIA Status
1. 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
2. This communication is responsive to the RCE filed on 10/30/2025.
3. Claims 1-20 are currently pending in this Office action.
Claim Rejections - 35 USC § 112
4. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
5. Claims 16-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 16 recites the limitation of "the semantic similarity" in line 10. There is insufficient antecedent basis for this limitation in the claim.
The claims not specifically mentioned above are also rejected by virtue of their dependency on a rejected claim.
Claim Rejections - 35 USC § 101
6. Applicant’s arguments regarding the 35 U.S.C. 101 rejection made in the prior Office action are acknowledged but are not persuasive. Again, despite the applicant’s argument [i.e., page 7-12 of Remarks], the examiner is not persuaded that the claimed invention is patent eligible matter. More specifically, contrary to applicant’s argument, the newly added limitations involving the feature of “selecting, by the device, a value for a dynamically variable similarity threshold based on information associated with the query” do not ‘transform’ the claimed abstract idea into a patent-eligible application. The examiner maintains the rejection because the examiner interprets that this newly added limitation merely requires a generic computer implementation and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements [i.e., a device OR an apparatus] when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. More specifically, the examiner finds no element or combination of elements recited in claims that contains any “inventive concept” or adds anything “significantly more” to transform the abstract concept into a patent-eligible application. The applicant’s arguments have not adequately explained how claims are performed such that it is not a routine and conventional function of a generic computer. As such, the examiner believes that the instant application comprise the limitations related to generic computer components and amount to mere instruction to implement the abstract idea on a computer. Therefore, the claims were held not to amount to significantly more than the abstract idea. The further explanation is set forth:
7. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter 2019 PEG.
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the method(s) of claims 1-20 are directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A, prog one of the 2019 PEG:
In claim 1-6 and 16-20, the limitations directed to additional elements include: a device; in claims 7-15, the limitations directed to additional elements include: an apparatus.
In exemplary claim 1, limitations reciting the abstract idea are as follows:
(1) receiving a query; (2) selecting a value for a dynamically variable similarity threshold based on information; (3) making, using the dynamically variable similarity threshold a determination as to whether the query matches a cached query; and (4) providing the response associated with the cached query.
These limitations, under the broadest reasonable interpretation, recite mental processes because these limitations can be performed in the human mind or using pen and paper. The examiner believes that the steps disclosed in claim 1can be performed by a human, using observation, evaluation, and judgment, because the steps involve making identifications and determinations, which are mental tasks humans routinely perform in the course of producing and performing queries.
A claim recites a mental process when the claim encompasses acts the person can perform using the mind or pen and paper [determining that a claim whose ‘steps can be performed in the human mind, or by a human using a pen and paper’ is directed to an unpatentable mental process]. This is true even if the claim recites, as they do here, that a generic computer component performs the acts.
For example, a person can perform the “receiving” step by simply looking at and reading the words. A person can also perform the “selecting” or “making” step by evaluating the information and making the determination by using pen and paper. Finally, a person can perform the “providing” step by manually passing the response.
As noted above, if a claim, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it is still in the mental processes category unless the claim cannot practically be performed in the mind. Here, the examiner is not persuaded that the aforementioned steps in claims 1, 7 or 16 cannot practically be performed in the human minds, or using pen and paper, but for the generic computing device.
Step 2A. In accordance with Step 2A, prog two of the 2019 PEG:
With respect to Step 2A, prog two, the judicial exception is not integrated into a practical application. The additional elements are directed to a device, a memory or an apparatus. However, these elements do not (1) improve the functioning of a computer or other technology; (2) are not applied with any particular machine (except for a generic computer); (3) do not effect a transformation of a particular article to a different state; and (4) are not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In other words, the aforementioned additional element (or combination of elements) recited in the claims do not integrate the judicial exception into a practical application.
In other words, the claimed processes fail to improve the functioning of either the device or the apparatus. Rather, these additional elements merely link the underlying abstract idea (i.e., mental processes or using pen and paper) to a particular technological environment, i.e., search query processing. Thus, the claimed process uses conventional computers to automate tasks that would have otherwise been very labor intensive by a human searcher. Such claims are not patent eligible. See OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible”).
Since the analysis of Step 2A prong one and prong two results in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to the device or the apparatus, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. Such general-purpose computing device, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible.
The additional elements are broadly applied to the abstract idea at a high level of generality and they operate in a well-understood, routine, and conventional manner. Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amount to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog.
The dependent claims have been fully considered, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fail to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
8. 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.
9. 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.
10. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2024/0265041 (hereinafter Rennie) in view of U.S. 2020/0265114 (hereinafter Beller), and further in view of U.S. 2025/0165752 (hereinafter Lovric).
Claims 1 and 7, Rennie discloses a method comprising:
receiving, at a device, a query for input to a language model ([0177]; “Another possible process to perform question modification (e.g., represented by the block 340) to produce further augmented questions is one based on a generative large language model (LLM), which may be implemented locally…”);
making, by the device and using the [dynamically variable similarity] threshold, a determination as to whether the query matches a cached query [for which the language model previously issued a response]; providing, by the device and based on the determination, the response associated with the cached query in lieu of inputting the query to the language model ([0147-0148]; “…Thus, in some embodiments, the query stack is configured to determine whether received query data matches (semantically, as may be determined by a machine learning answer cache), and to generate the output data based on one or more answer data records (possibly stored within the answer cache) in response to determining that the received query data matches one of the pre-determined questions. In some embodiments, the matching of query data to the past questions and associated answers stored in cache is performed by computing a score that is based on the combination of the questions and their answers, and ranking the computed scores to identify one or more likely matching candidates”).
Rennie does not explicitly disclose the features of selecting, by the device, a value for a dynamically variable similarity threshold based on information associated with the query including at least one of: a user preference, a query type, a latency for receiving at least one response from the language model, a cost to make a query to the language model, or a level of network connectivity with the language model; and utilizing the dynamically variable similarity threshold. However, Beller discloses that “In one example, the degree of similarity applied by bin similarity controller 212 may be specified in similarity threshold rules 213 by different thresholds for application by different service instances of query system 120 or for application to specific queries. For example, a client receiving query system 120 as a service may request that the service apply a threshold for similarity that is higher than a general threshold applied by the service for all or particular types of queries submitted by the client, where the service provider may adjust the cost of the service based on the threshold level applied for similarity determinations. In another example, a user submitting user query 104 may specify, in an additional selectable field of user query 104, a numerical value for a similarity threshold, where query system 120 may dynamically adjust the similarity threshold applied by bin similarity controller 212 to a requested level for the particular query” ([0033-0034]) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Beller in the system of Rennie in view of the desire to enhance the data process system by utilizing the dynamically adjusting similarity threshold scheme resulting in improving the efficiency of outputting the most matching result.
Rennie in view of Beller does not explicitly disclose the feature of and making, by the device and using the [dynamically variable] similarity threshold, a determination as to whether the query matches a cached query for which the language model previously issued a response. However, Lovric discloses that “…Routing system 100 may process one or more of the received query or the compressed query using cache storage 120. Routing system 100 may use cache storage 120 to determine whether the query is similar to (e.g., above a similarity threshold) one or more of a previously received query or a previously compressed query stored in query cache 122, and if so, may retrieve and re-use a previously provided response stored in answer cache 124” ([0049]). Lovric further discloses that “If the received query is not similar to (e.g., below a similarity threshold) a previously received query or a previously compressed query stored in query cache 122, routing system 100 may determine whether to provide (e.g., send) the receive query to one or more of first LLM provider 132…” ([0050]). Lovric additionally discloses that “…The determination model training data may be applied to a machine learning algorithm to train the determination machine learning mode…The determination machine learning model may be configured to receive, as inputs, the received and/or compressed query and may further be configured to receive inputs such as, but not limited to, client information, cached queries, current event information, and or the like…” ([0054]) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Lovric in the modified system of Rennie in view of the desire to enhance the data process system by utilizing the query optimization scheme resulting in improving the efficiency of outputting the most matching result. Additionally, Rennie discloses one or more network interfaces; a processor coupled to the one or more network interfaces and configured to execute one or more processes; and a memory configured to store a process that is executable by the processor ([0041]; fig. 1).
Regarding claim 2, Rennie in view of Beller and Lovric disclose the method further comprising: selecting the dynamically variable similarity threshold further based on a threshold parameter received from a user interface (Rennie: [0148]), (Beller: [0034]) and (Lovric: [0049]). Therefore, the limitations of claim 2 are also rejected in the analysis of claim 1, and the claim is rejected on that basis.
Regarding claims 3 and 13, Rennie in view of Beller and Lovric disclose the method wherein the information associated with the query indicates an application via which the query was generated (Rennie: [0177]).
Regarding claims 4 and 14, Rennie in view of Beller and Lovric disclose the method wherein the application generated the query automatically (Rennie: [0127]).
Regarding claims 5 and 15, Rennie in view of Beller and Lovric disclose the method wherein making the determination as to whether the query matches the cached query comprises:
determining whether a semantic distance between the query and the cached query exceeds the dynamically variable similarity threshold (Rennie: [0178]), (Beller: [0034]) and (Lovric: [0049]). Therefore, the limitations of claims 5 and 15 are rejected in the analysis of claims 1 or 7, and the claim are rejected on that basis.
Regarding claim 6, Rennie in view of Beller and Lovric disclose the method wherein the language model is a large language model (LLM) that the device accesses via an application programming interface (API) (Rennie: [0276]).
Regarding claim 8, Rennie in view of Beller and Lovric disclose the apparatus wherein the information associated with the query indicates a query type associated with the query (Rennie: [0039]).
Regarding claim 9, Rennie in view of Beller and Lovric disclose the apparatus wherein the information associated with the query indicates a latency associated with sending the query to the language model to produce an output (Rennie: [0111]).
Regarding claim 10, Rennie in view of Beller and Lovric disclose the apparatus wherein the information associated with the query indicates a level of performance associated with a computer network via which the apparatus accesses the language model (Rennie: [0109 and 0112]).
Regarding claim 11, Rennie in view of Beller and Lovric disclose the apparatus wherein the information associated with the query indicates a threshold parameter received from a user interface (Rennie: [0177]).
Regarding claim 12, Rennie in view of Beller and Lovric disclose the apparatus wherein the information associated with the query indicates a resource cost associated with sending the query to the language model to produce an output (Rennie: [0111 and 0177]) and (Lovric: [0049 and 0380]). Therefore, the limitations of claim 12 are rejected in the analysis of claim 7, and the claim is rejected on that basis.
Regarding claim 16, Rennie discloses a method for improving response times to a query answering service, wherein the query answering service provides natural language responses to queries, the method comprising steps of:
storing the queries made to the query answering service and corresponding responses from the query answering service in a cache ([0148]; “In some embodiments, the interface 130 and/or the query processing module may be coupled toa query cache 135 and a question generation unit (which may be part of the cache 135 or of the query processing module 136, or may be a separate unit). The query cache 135 stores, among other things, answers/contents corresponding to frequently asked questions…”);
receiving a query ql at a device ([0177]; “Another possible process to perform question modification (e.g., represented by the block 340) to produce further augmented questions is one based on a generative large language model (LLM), which may be implemented locally…”);
determining a [value for a dynamically variable semantic] threshold using information associated with the query q1, returning the response rl stored in the cache corresponding to the at least one query q2 ([0147-0148 and 0263]; “The query processing module 136…to the one or more candidate portions identified based on their coarse transformed vectors, at least one fine-detail transformed content record matching, according to a second criterion (e.g., some other closeness or similarity metric, or the same criterion applied…”; and “…Thus, in some embodiments, the query stack is configured to determine whether received query data matches (semantically, as may be determined by a machine learning answer cache), and to generate the output data based on one or more answer data records (possibly stored within the answer cache) in response to determining that the received query data matches one of the pre-determined questions…”).
Rennie does not explicitly disclose the features of determining a value for a dynamically variable semantic threshold using information associated with the query q1 including at least one of a user preference, a query type, a latency for receiving at least one response from the query answering service, a cost to make a query to the query answering service, and a level of network connectivity with the query answering service, wherein the semantic similarity threshold is correlated with a level of semantic similarity between two natural language texts. However, Beller discloses that “In one example, the degree of similarity applied by bin similarity controller 212 may be specified in similarity threshold rules 213 by different thresholds for application by different service instances of query system 120 or for application to specific queries. For example, a client receiving query system 120 as a service may request that the service apply a threshold for similarity that is higher than a general threshold applied by the service for all or particular types of queries submitted by the client, where the service provider may adjust the cost of the service based on the threshold level applied for similarity determinations. In another example, a user submitting user query 104 may specify, in an additional selectable field of user query 104, a numerical value for a similarity threshold, where query system 120 may dynamically adjust the similarity threshold applied by bin similarity controller 212 to a requested level for the particular query” ([0033-0034]) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Beller in the system of Rennie in view of the desire to enhance the data process system by utilizing the dynamically adjusting similarity threshold scheme resulting in improving the efficiency of outputting the most matching result.
Rennie in view of Beller does not explicitly disclose the features of determining a semantic similarity between the query ql and at least one query q2 stored in the cache for which the query answering service previously issued a response r1; and in response to determining the at least one query q2 stored in the cache for which the semantic similarity between the query q1 and the at least one query q2 is greater than or equal to the semantic similarity threshold. However, Lovric discloses that “…Routing system 100 may process one or more of the received query or the compressed query using cache storage 120. Routing system 100 may use cache storage 120 to determine whether the query is similar to (e.g., above a similarity threshold) one or more of a previously received query or a previously compressed query stored in query cache 122, and if so, may retrieve and re-use a previously provided response stored in answer cache 124” ([0049]). Lovric further discloses that “If the received query is not similar to (e.g., below a similarity threshold) a previously received query or a previously compressed query stored in query cache 122, routing system 100 may determine whether to provide (e.g., send) the receive query to one or more of first LLM provider 132…” ([0050]). Lovric additionally discloses that “…The determination model training data may be applied to a machine learning algorithm to train the determination machine learning mode…The determination machine learning model may be configured to receive, as inputs, the received and/or compressed query and may further be configured to receive inputs such as, but not limited to, client information, cached queries, current event information, and or the like…” ([0054]) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Lovric in the modified system of Rennie in view of the desire to enhance the data process system by utilizing the query optimization scheme resulting in improving the efficiency of outputting the most matching result.
Regarding claim 17, Rennie in view of Beller and Lovric disclose the method further comprising:
in response to failing to determine the at least one query q2 stored in the cache for which the semantic similarity between the query q1 and the at least one query q2 is greater than or equal to the semantic similarity threshold, returning a response r2 obtained by sending the query q1 to the query answering service (Rennie: [0147]) and (Lovric: [0049 and 0054-0055]). Therefore, the limitations of claim 17 are rejected in the analysis of claim 16, and the claim is rejected on that basis.
Regarding claim 18, Rennie in view of Beller and Lovric disclose the method wherein the semantic similarity threshold is dynamically modified based on at least one of the user preference, the query type, the latency for receiving at least one response from the query answering service, the cost to make the query to the query answering service, and the level of network connectivity with the query answering service (Rennie: [0020, 0109, 0112 and 0157]) and (Lovric: [0049 and 0380]). Therefore, the limitations of claim 18 are rejected in the analysis of claim 16, and the claim is rejected on that basis.
Regarding claim 19, Rennie in view of Beller and Lovric disclose the method wherein determining the semantic similarity between the query ql and the at least one query q2 comprises:
computing a vector corresponding to each of the query q1 and the at least one query q2 being compared; and determining the semantic similarity by comparing vectors (Rennie: [0143 and 0147]).
Regarding claim 20, Rennie in view of Beller and Lovric disclose the method further comprising:
in response to determining that the at least one query q2 stored in the cache for which the semantic similarity between the query q1 and the at least one query q2 is greater than or equal to the semantic similarity threshold, returning the response rl stored in the cache, wherein the semantic similarity between the query q1 and the at least one query q2 stored in the cache corresponding to the response rl is a maximum value for all cached queries compared with the query ql (Rennie: [0147 and 0178]) and (Lovric: [0049 and 0054-0055]). Therefore, the limitations of claim 20 are rejected in the analysis of claim 16, and the claim is rejected on that basis.
Response to Arguments
11. Applicant’s arguments have been considered but are deemed to be moot in view of new grounds of rejection presented in this Office action.
Conclusion
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONICA M PYO whose telephone number is (571)272-8192. The examiner can normally be reached Monday-Friday 8am-4pm.
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/MONICA M PYO/ Primary Examiner, Art Unit 2161