DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 12/30/2025 has been entered.
3. Currently claims 1 and 11 have been amended; claims 4, 10, 14, 20, 21 and 23 have already been canceled. Therefore, claims 1-3, 5-9, 11-13, 15-19, 22 and 24 are pending in this application.
Claim Rejections - 35 USC § 101
4. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
35 U.S.C.101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
● Claims 1-3, 5-9, 11-13, 15-19, 22 and 24 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1)
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
(Step 2A) [Wingdings font/0xE0] Prong-One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering each of claims 1 and 11, as representative claims, the following claimed limitations recite an abstract idea:
[collect] a user profile from a user; [draft] a hash table to represent the user profile by
generating a plurality of hashes, using a hash function, to map data of arbitrary size value to a fixed-size value, wherein each hash of the plurality of hashes represents an entry of user-related data associated with the user profile; and calculate a term index data structure, using the hash function, to lookup of one or more entries associated with the user profile; determine an expansion plan as a function of the user profile, wherein determining the expansion plan comprises: determining a plurality of expansion mindsets; data comprising a plurality of user profiles correlated to a plurality of state scores indicating a particular strength on a plurality of expansion mindsets, wherein the correlations are previously derived; sort datum, wherein sorting the datum further comprises generating categories based on correlations found in the datum; and output, using the model, a plurality of state scores correlated to each expansion mindset of the expansion plan; determine, using the model, a walkaway plan as a function of the user profile; and generate a viability coaching plan as a function of the expansion plan and the walkaway plan, wherein the viability coaching plan includes generating a state distance; generate a group viability model to provide a group insight, wherein the group insight comprises a viability coaching step for the state distance within the viability coaching plan for each group member and wherein the group viability model comprises a plurality of viability coaching plans and a comparison matrix, wherein the comparison matrix includes elements comprising state scores of each group member; and identifying weaknesses and strengths among group members as a function of the state scores of each group member.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to certain methods of organizing human activity, or mental processes, which are part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)). For instance, the current claims correspond to one or more of fundamental economic practices and/or managing personal behavior—such as teaching, wherein one or more users’ profiles are analyzed using one or more algorithms/models in order to determine one or more viability coaching plans—such as, an expansion plan (e.g. a financial goal, a career goal, etc.) and a walkaway plan (e.g. a retirement), etc., and thereby, using one or more algorithms and/or mathematical functions, a coaching plan is generated to the user (e.g. a strategy for guiding the user in order to achieve one or more of the user’s goals), etc.; and furthermore, a group viability model is generated to provide a group insight, the group viability model comprises a plurality of viability coaching plans and a comparison matrix, wherein the comparison matrix includes state scores of each group member; and thereby weaknesses and strengths among group members are identified as a function of the state scores of each group member, etc.
Of course, given the limitations that recite one or more evaluation processes; such as: determining an expansion plan as a function of the user profile, wherein such determination comprises determining a plurality of expansion mindsets; determining or generating a plurality of state scores correlated to each expansion mindset of the expansion plan; determining—using the state score model—a walkaway plan as a function of the user profile, etc., the current claims also overlap with the group mental processes—such as, an evaluation and/or a judgment process, etc.
Note also that a human—such as a skilled librarian—can draft a hash table and a hash function, at least using a pen and paper, in order to enable the librarian to easily locate a desired book in the library. For instance, after assigning a unique number to each letter of the English alphabet (“A” is 1, “B” is 2, . . . “Z” is 26), the author generates a corresponding value to each book while considering the name of the author of that book. In particular, the librarian may draft a hash function, which requires summing the numbers that represent the letters of the name of the author (e.g., the letters that form the last name of the author, etc.). Accordingly, the librarian uses the above hash function to generate a hash value for each book in the library (e.g., a first book authored by “Jon” would have a hash value of 39; a second book authored by “Mary” would have a hash value of 57, etc.). Of course, the librarian also drafts a table—i.e., a hash table—that maps the above keys; and subsequently, the librarian places each book in its corresponding position in the library (e.g., the first book, which is written by Jon, is placed in a drawer numbered 39; whereas the second book, which is written by Marry, is placed in a drawer numbered 57, etc.).
Note also that the librarian may plan a way to resolve some possible unexpected occurrences—such as, two different names with the same hash value. In such instances, while keeping both books in the same drawer, the librarian may further arrange these books using one or more additional attributes (e.g., arranging the books in alphabetical order, etc.).
The observation above demonstrates that a human can draft, either mentally and/or using a pen and paper, a hash algorithm—i.e., a hash function and/or table, etc.
(Step 2A) [Wingdings font/0xE0] Prong-Two:
The claim(s) recite additional element(s), wherein a field-programmable gate array (FPGA), which is part of the claimed computing device, is utilized to facilitate the recited steps/functions regarding: collecting information (“receive a user profile from a user”); generating, using a mathematical function/algorithm, structures data using the received information “generate a hash table configured to represent the user profile by: generating a plurality of hashes, using a hash function . . . and calculating a term index data structure, using the hash function, to execute lookup of one or more entries associated with the user profile”); determining one or more plans using one or more additional algorithms (“determine an expansion plan as a function of the user profile . . . correlations are derived using a fuzzy inference engine configured to map the plurality of user profiles using fuzzy logic, wherein the plurality of user profiles is arranged by a logic comparison into a state score arrangement, and wherein the state score arrangement is determined by having a degree of overlap exceeding a predetermined threshold”); performing—using algorithm(s)/model(s)—further data analysis; and generating one or more informative results (“iteratively training a state score machine-learning mode, using the training data; and outputting, using the state score machine-learning model, a plurality of state scores correlated to each expansion mindset of the expansion plan; determine, using the state score machine-learning model, a walkaway plan as a function of the user profile; generate a viability coaching plan . . . generate a group viability model configured to provide a group insight . . . the group insight comprises a viability coaching step for the state distance within the viability coaching plan for each group member and wherein the group viability model comprises a plurality of viability coaching plans and a comparison matrix, wherein the comparison matrix includes elements comprising state scores of each group member; and identifying weaknesses and strengths among group members as a function of the state scores of each group member”), etc.
However, the claimed additional element(s) fail to integrate the abstract idea into a practical application since the additional element(s) are utilized merely as a tool to facilitate the abstract idea.
Thus, when each claim is considered as a whole, the additional element(s) fail to integrate the abstract idea into a practical application since they fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology. The observations above confirm that the claims are indeed directed to an abstract idea.
(Step 2B)
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions. Accordingly, none of the current claims recites an element—or a combination of elements—directed to an “inventive concept”.
It is also worth noting, per the original disclosure, that the claimed invention is directed to a conventional and generic arrangement of the additional elements. For instance, the disclosure describes a system that is implemented to provide information—such as a coaching plan—to a user, wherein the system comprises one or more commercially available conventional computing devices (e.g., a workstation, a tablet computer, a smartphone, etc.); and wherein such conventional computing device communicates with an online service provider (e.g., a server) via a conventional communication network—such as the Internet (see [0059]; [0062]; [0064 to [0068]).
In addition, the utilization of the conventional computer/network technology to generate—based on the analysis of information gathered regarding a user(s)—one or more plans, including the presentation of coaching(s) that helps the user to achieve one or more of his/her goals, etc., is directed to a well-understood, routine or conventional activity in the art (e.g., see. US 2011/0112985; US 2009/0048957; US 2002/0123949; US 2005/0027632; US 2006/0010060, etc.).
Similarly, the utilization of old and well-known algorithm/s, such as (i) training one or more machine-learning algorithms to facilitate the analysis of the collected data (e.g., see US 2012/0303412; US 7,193,628; US 2004/0153389, etc.); (ii) the use of a hybrid model, which combines two or more algorithms, to facilitate the analysis of collected data (e.g., see US 2009/0037398; US 5,450,527, etc.,); (iii) the use of one or more hash algorithms to efficiently process information (e.g., US 2010/0287172; US 2009/0285471; US 2010/0049735, etc.), are also part of the conventional computer/network technology.
The observations above confirm that the current claimed invention fails to amount to “significantly more” than an abstract idea.
Note also that the above analysis already encompasses each of the current dependent claims (i.e., claims 2, 3, 5-9, 12, 13, 15-19, 22 and 24). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea.
Thus, when considered as a whole, none of the current claims is implementing an element—or a combination of elements—directed to an inventive concept (e.g., none of the claims implement an element—or a combination of elements—that provides a technological improvement over the conventional technology).
► Applicant’s arguments directed to section §101 have been fully considered (the arguments filed on 12/30/2025). However, the arguments are not persuasive at least for the following reasons:
Firstly, regarding prong-one, namely mental processes, Applicant is asserting that “some limitations of currently amended claim 1 of ‘generating a plurality of hashes, using a hash function, to map data of arbitrary size value . . . lookup of one or more entries associated with the user profile’ do not fall within the ‘mental process’ groupings of abstract ideas. For example, such limitations cannot be performed mentally or with pen and paper. Further, at least such limitations do not recite mental processes because they cannot be practically performed in the human mind . . . those limitations of amended claim 1 detailed above should be considered ‘additional elements’ to the alleged abstract idea” (emphasis added).
However, except for making a conclusory assertion regarding the alleged incapability of a human; namely, the incapability of a human to mentally, and/or using a pen and paper, to generate a plurality of hashes using a hash function, including mapping data of arbitrary size value to a fixed-size value, etc., Applicant does not appear to provide any rationale and/or evidence to substantiate the above assertion. In contrast, as evident from the analysis presented under prong-one, the Office has already demonstrated, based on an exemplary scenario that relates to a traditional library, how a human can mentally—and/or using a pen and paper—perform the limitations that relate to the process of generating a plurality of hashes using a hash function, including the process of mapping data of arbitrary size value to a fixed-size value, etc. (again see above the findings presented under prong-one of Step 2A). So far, Applicant fails to challenge—much less negate—the Office’s analysis; and therefore, Applicant’s arguments are not persuasive.
Similarly, regarding certain methods of organizing human activity, Applicant is asserting that “the features of amended claim 1, for example, ‘generating a plurality of hashes, using a hash function, to map data of arbitrary size value to a fixed-size value, wherein each hash . . . represent an entry of user-related data associated with the user profile . . . one or more entries associated with the user profile’ do not capture any management of personal behavior or relationships or interactions between people. Rather, the limitations recited in claim 1 seek to, for example, provide increased efficiency with regards to data storage; enable faster data retrieval and lookup; and ensure consistency in data management regardless of the complexity of input data (e.g., see paragraphs 0015)” (emphasis added).
However, such use of hash function and table is intended to organize the information that relates to the user; and thus, a human—e.g., a financial adviser—who is providing guidance to the user/customer can organize various pieces of information to the user (e.g., information that relates to the user’s savings, information that relates to the user’s retirement, etc.) before and/or during the presentation of counseling to the user. Thus, regardless of whether the financial advisor is using a hash function/table or any other approach to organize the information, this still does not change the fact regarding the abstract idea—i.e., certain methods of organizing human activity—that the current claims are reciting. For instance, the user (the customer) is presented with coaching regarding the so-called (i) “expansion plan” (e.g., a financial goal, a career goal, etc., see [0013] of the specification), and/or (ii) “expansion plan” (e.g., a retirement goal, etc., again see [0013] of the specification). Thus, Applicant still fails to negate the Office’s findings regarding certain methods of organizing human activity.
Although it has nothing to do with prong-one of Step 2A, Applicant is also attempting to emphasize the alleged benefits, which the claimed hash function, is providing in terms of computer efficiency; such as, “increased efficiency with regards to data storage; enable faster data retrieval and lookup; and ensure consistency in data management regardless of the complexity of input data” (emphasis added).
However, it is already part of the existing computer/network technology to utilize a hash function/algorithm to process information; and accordingly, an existing computer system, which executes a hash function/algorithm to process information, inherently achieves each of the above benefits. In particular, given the description in the original disclosure (see the specification: [0015], [0019], etc.), there is no evidence that suggests whether Applicant has developed any new/advanced hash algorithm, which is considered to be an advance over the existing computer/network technology. Instead, while relying merely on the existing computer technology, which utilizes one or more existing hash algorithms to process collected information, Applicant appears to be attempting to mask the abstract idea that the claims are reciting.
Thus, Applicant’s conclusory assertion, “claim 1 cannot reasonably be construed as a method of organizing human activity and submits that the claims do not encapsulate the above identified groupings of organizing human activity”, is certainly not persuasive.
Secondly, while referring to one of the examples, namely Example 39, which is discussed per the 2019 PEG, Applicant asserts, “some of the limitations of claim 1 detailed above are substantially analogous to those of Example 39 because, like Example 39 that utilizes a first training data set and a second training data set based on the first one to iteratively training a neural network, the steps recited in amended claim 1 disclose ‘iteratively training a state score machine-learning model using the training data’ and ‘iteratively training a state distance machine-learning model trained using state distance training data’. Further, analogous to Example 39, claim 1 does not recite any mathematical relationships, formulas, or calculations. Applicant respectfully submits that at least those limitations of amended claim 1 detailed above should be considered ‘additional elements’ to the alleged abstract idea” (emphasis added). Applicant has also mentioned one of the memorandums, namely the August 4, 2025 memo, which is assumed to clarify the differences between claims that “recite” an abstract idea and claims that “involve” an abstract idea.
However, unlike Applicant’s assertion, Example 39, which corresponds to the process of training a neural network for image processing (i.e., training a neural network for facial detection), is not analogous to any of the current claims. In particular, as already pointed out in the guidance, the claim representing Example 39 does not recite any judicial exception (i.e., the exemplary claim does not recite an abstract idea, a law of nature and/or a natural phenomenon). In constant, Applicant’s claims do recite an abstract idea (e.g., see the findings presented under prong-one of Step 2A). Accordingly, Applicant’s attempt to challenge the abstract idea, while relying on a non-analogous exemplary claim, is not persuasive.
In addition, if a given claim (e.g., see current claim 1) recites the implementation of one or more machine-learning models, this does not necessarily mean that the claim is not reciting an abstract idea. In fact, the most recent memorandum, namely the December 5, 2025 memorandum, provides a revision to the 2024 publication of the MPEP; and this memorandum demonstrates machine-learning models that are considered to be an advance over the existing machine-learning models. In particular, while referring to Ex Parte Desjardins, the memorandum points out the specific features that demonstrate a technological improvement. For instance, besides pointing out the improvement achieved with respect to reducing the complexity of the system and the amount of storage needed, the memorandum also points the improvement achieved with respect to the machine-learning technology—i.e., how the machine-learning model is trained in order to overcome the problem of “catastrophic forgetting” (e.g., see pages 2 and 3 of the memorandum). In contrast, neither Applicant’s current claims, nor the original disclosure as a whole, implements any technological improvement, much less an improvement specific to the machine-learning technology. Instead, the currently claimed (and the originally disclosed) system/method is utilizing the existing computer/network technology, which includes one or more existing machine-learning modes, merely as a tool to facilitate an abstract idea. Consequently, Applicant’s attempt to challenge the abstract idea, while emphasizing the machine-learning models that the current claims are reciting, is certainly not persuasive.
Thirdly, regarding prong-two of Step 2B, Applicant is asserting that “some limitations of currently amended claim 1 of ‘generating a plurality of hashes, using a hash function, to map data of arbitrary size value to a fixed-size value, wherein each hash of the plurality of hashes is configured to represent an entry of user-related . . . ‘iteratively training a state distance machine-learning model trained using state distance training data’ do not fall within the ‘mental process’ groupings of abstract ideas” (emphasis added).
However, the above argument is irrelevant since Applicant is presenting the same repetitive argument, which is already addressed per the discussion presented above. In particular, the discussion presented above already demonstrates the reasons why neither the limitations that relate to the hash function, nor the limitations that relate to the machine-learning models, alone or in combination fails to cure the abstract idea that the claims are reciting.
Similarly, the discussion above also demonstrates how the memorandum of August 4, 2025, including the latest memorandum of December 5, 2025, confirm the abstract idea that the current claims are reciting. Moreover, as quite evident from the analysis presented under prong-two of Step 2A, the claimed limitations are considered as a whole (i.e., the abstract idea in combination with the additional elements). In this regard, except for simply indicating the need to consider the claims as a whole, Applicant fails to identify an issue (if any) regarding the Office’s analysis presented under prong-two of Step 2A. Consequently, Applicant’s arguments are not persuasive.
Furthermore, while attempting to summarize the MPEP (e.g., MPEP 2106.04), including an example (Example 47) from the July 2024 update of the PEG, Applicant is asserting that, “analogously to Example 47, at least the limitations of currently amended claim 1 integrate the judicial exception into a practical application because (1) the specification teaches a technological improvement . . . how to provide for more efficient and enhanced performance relating to data management, retrieval and access. The disclosed system applies any alleged abstract idea in a concrete and practical way to make improvements in the field of solution simulation and automation, in heterogenous environments with large volumes of complex data. For example, the disclosed system provides for a ‘term index ... a data structure to facilitate fast lookup of entries within a dictionary (i.e. . , index) . . . without limitation, a term index may use a n-based indexing, wherein the n-based indexing may configure a dictionary to start with any index from 0 to n. Further, a term index may be determined/calculated using one or more hash functions. As used in this disclosure, a ‘hash function’ is a function used to map data of arbitrary size to a fixed-size value. In some cases, a fixed-size value may include, but is not limited to, hash value, hash code, hash digest, and the like. In a non-limiting example, user profile 112 may be present as a dictionary containing a plurality of hashes generated using hash function such as, without limitation, an identity hash function, a trivial hash function, a division hash function, word length folding, and the like, wherein each hash of a plurality of hashes may represent a single entry of user-related data within user profile 112.’ (see paragraph [0015])” (emphasis modified).
However, except for the attempt made to describe the existing computer technology, Applicant still fails to demonstrate the alleged technological improvement that the disclosed system/method is supposedly providing. Nota that simply describing the use of a hash function to process data, and/or the benefits that such hash function provides with respect to facilitating fast lookup of one or more entries within a dictionary, etc., does not necessarily demonstrate any technological improvement over the existing computer technology. This is because the implementation of one or more hash functions/algorithms is already part of the existing computer/network technology; and thus, each and every benefit that such algorithm provides (e.g., fast lookup of one or more entries within one or mroe storages/dictionaries, etc.) is already part of the existing computer/network technology. Moreover, as already pointed out above, the original disclosure as a whole does not provide any evidence regarding any new or advanced algorithm that Applicant has developed, much less an algorithm that is considered to be an advance over the existing computer/network technology. Instead, Applicant is still utilizing the existing computer/network technology—merely as a tool—to facilitate the processing of collected information. Thus, given the lack of technological improvement, neither the claimed nor the disclosed system/method, when considered as a whole, is integrating the abstract idea into a patent-eligible practical application. Consequently, Applicant’s arguments are not persuasive.
Although the analysis applies to Step 2B, it is also worth to note that the references currently presented, under Step 2B, already confirm that Applicant is still relying on the existing computer/network technology despite Applicant’s alleged technological improvement regarding the use of hash function/algorithm. For instance, Schneider (US 2010/0287172) is a publication available to the public for more than a decade prior to Applicant’s disclosed system/method. Schneider already describes a system directed to the existing computer/network technology; wherein the system comprises a search engine that uses search indices to generate a search result(s) quickly ([0017]); and the system implements a hash unit that executes one or more hash functions for generating hash values pertinent to one or more keywords; and accordingly, based on one or more keywords received as part of a search request(s), the system efficiently locates one or more relevant documents from one or more storages ([0031]; [0032]; [0036]; [0038]).
Similarly, Wall (US 2009/0285471), which is also another publication available to the public for more than a decade prior to Applicant’s disclosed system/method, also describes a system directed to the existing computer/network technology. This system detects—in real-time—the presence of duplicate financial documents (e.g., duplicate bank checks), which may indicate a potential fraudulent event ([0001]; [0002]; [0006]); wherein the system implements one or more hash functions that generate, based on one or more features extracted from each financial document, a corresponding hash value(s) to each financial document; and wherein the hash values, each representing a corresponding financial document, are stored in a hash table; and accordingly, when a new financial document received, the system quickly searches a large number of hash values in the hash table and efficiently determines, in real-time, whether the received financial document is a duplicate one or not, etc. (see [0044]; [0045]; [0048]; [0049] and [0088] to [0090]).
Thus, it is quite evident from the observations above that Applicant is indeed relying on the existing computer/network technology. In particular, while emphasizing one or mroe features of the existing computer/network technology, including the inherent advantages that these features are providing (e.g., the implementation of one or more hash algorithms, which allow the system to quickly search a database and generate one or more results efficiently, etc.), Applicant is attempting to substantiate the alleged technological improvement that the claimed (or the disclosed) system/method is supposedly providing. However, unlike Applicant’s assertion, such use of the existing computer/network technology for its intended purpose certainly does not constitute a technological improvement over the relevant existing technology.
Of course, the lack of technological improvement above once again confirms that neither the claimed system/method nor the disclosed system/method integrates the identified abstract idea into a patent-eligible practical application. Instead, Applicant is still utilizing the existing computer/network technology—merely as a tool—to facilitate the abstract idea (see prong-one Step 2A). Consequently, Applicant’s current conclusory assertions, “an accurate and performance-driven system that generates, manipulates and integrates multiple data structures such that a computing system data processing operations are improved” (emphasis added), “the claimed system uses a hashing mechanism to enable an improved data management and data retrieval system with increased security that is adaptable to e.g., heterogenous and dynamic inputs. Further, these technical operations allow for a streamlined and data-driven system that allows for actions to be performed faster and more accurately and for operational efficiency in downstream environments to be enhanced” (emphasis added), “the specification teaches a number of technological improvements” (emphasis added), etc., are all not persuasive. This is once again because Applicant still fails to demonstrate any claimed/disclosed feature—or any combination of claimed/disclosed features—that is considered to be an advance over the existing computer/network technology, Instead, while simply emphasizing some of the inherent advantages that the existing computer/network technology is providing, Applicant is once again attempting to substantiate the alleged technological improvement that the claimed (or the disclosed) system/method is supposedly providing.
Fourthly, while referring to Example 47 of the USPTO guidance, Applicant is providing a repetitive argument regarding the claimed/disclosed hash function, which supposedly provides the alleged technological improvement. For instance, Applicant is asserting that “currently amended claim 1 reflect the technical improvements detailed above in the technical field of solution simulation and automation: ‘generate a hash table configured to represent the user profile by: generating a plurality of hashes, using a hash function . . . amended claim 1 provide for a robust system that allows for faster and more efficient data retrieval and data management whilst maintaining data security and integrity . . . reflect the technological improvements to the technical problems described in the background (addressing the problem of executing data computation with greater accuracy and in a reasonable time frame) and when considered in combination, integrate the abstract idea into a practical application because the claim improves the functioning of a computer or technical field . . . improvement in the technical field of solution simulation and automation” (emphasis added).
However, unlike Applicant’s subjective theory above, the mere use of one or more features (e.g. a hash algorithm, etc.) of the existing technology in one or more desired technical fields, including the so-called “solution simulation and automation”, does not necessarily imply a technological improvement. This is because the existing technology can be used in one or more desired fields. In particular, regardless of the type of technical filed where it is used, the hash algorithm can be used to quickly process information and generate one or more accurate results efficiently. For instance, when considering the technical field of document searching and retrieval (e.g., see above the system discussed per Schneider), the hash algorithm is utilized to quickly identify—from one or mroe sources—one or more documents that are relevant to one or more keywords that are received as part of a search request. Similarly, regarding the technical filed of fraud detection (e.g., see above the system discussed per Wall), the hash algorithm is utilized to promptly—i.e., in real-time—determine whether a newly acquired document relates to a potential fraud by quickly verifying whether there are one or more stored duplicates that correspond to the newly acquired document, etc. Of course, when considering the technical filed of computer resource management, the hash algorithm is utilized to easily and accurately determine unused computational resources; so that, the system efficiently reclaims and uses those unused computational resources (e.g., see US 2010/0049735; [0001], [0003], [0006], [0017]).
The observation above confirms that Applicant’s theory regarding the alleged technical filed has nothing to do with a technological improvement. In particular, the use of one or more features of the existing computer technology in a desired technical field, such as the “solution simulation and automation”, does not necessarily demonstrate a technological improvement. Thus, Applicant’s attempts to substantiate the alleged technological improvement, while repeatedly emphasizing the inherent advantages of the hash algorithm (e.g., “faster and more efficient data retrieval and data management whilst maintaining data security and integrity”, “executing data computation with greater accuracy and in a reasonable time frame”, etc.), are all not persuasive.
Finally, regarding Step 2B, Applicant is asserting that “the limitations of claim 1 as amended . . . ‘generate a hash table configured to represent the user profile by: generating a plurality of hashes, using a hash function . . . execute lookup of one or more entries associated with the user profile’ recite additional elements that amount to significantly more than the judicial exception (i.e., inventive concept) . . . claim 1 as amended recite meaningful limits on practicing the abstract idea. Further, this can be evidenced at least by the ‘practical application’ analysis presented above . . . claim 1 as amended amount to an inventive concept, and thus ‘significantly more’ than any alleged abstract idea recited therein . . . claim 1 as amended recite the use of technical features associated with a system for faster and more efficient data retrieval and data management whilst maintaining data security and integrity in order to optimize subsequent processing operations . . . claim 1 as amended, are not generic and instead recite a novel approach comprising ‘generate a hash table configured to represent the user profile by: generating a plurality of hashes, using a hash function . . . execute lookup of one or more entries associated with the user profile’” (emphasis added).
However, Applicant is once again relying on the use of a hash algorithm (e.g., the process of generating hash values using a has function, etc., see claim 1) to substantiate the alleged eligibility of the current claims per Step 2B. In particular, Applicant is repeating the inherent advantages that a hash algorithm is providing (e.g., “faster and more efficient data retrieval and data management whilst maintaining data security and integrity in order to optimize subsequent processing operations”, etc.). In contrast, simply utilizing a well-known feature(s) of the conventional technology for its intended purpose; such as, utilizing a hash algorithm to efficiently locate one or more results that are pertinent to one or more keywords, etc., does not constitute an inventive concept that amounts to “significantly more” than an abstract idea.
Moreover, similar to the inquiry discussed above regarding prong-two of Step 2A, the inquiry per Step 2B also considers a technological improvement as part of the test in order to determine whether a given claim is providing an inventive concept (e.g., see MPEP 2106.05(a)). Thus, besides the generic and the conventional arrangement of the claimed additional elements, the lack of technological improvement per the claimed (and disclosed) system/method is further evident that none of the current claims, when considered as a whole, implements an element—or a combination of elements—that is directed to an inventive concept. Thus, Applicant’s arguments are not persuasive.
Applicant further asserts that “no court cases, literature, or references are of record indicating that the above-described limitations are ‘well-understood, routine, [and] conventional,’ and furthermore asserts that neither the instant application nor the prosecution history in this matter contains any admission thereof . . . claim 1 has features that amount to significantly more than the abstract idea, because such features provide a technical contribution to the field of solution simulation and automation, which differs from conventional systems that do not achieve the desired level of robustness, accuracy and speed in environments that are complex in nature and which process large volumes of heterogeneous data (see paragraph [0002]) . . . claim 11 recites an inventive concept, at least because amended claim 11 contains limitations amounting to a non-conventional and non-generic arrangement of process steps. See BASCOM . . . taken as a whole, limitations to claims 11 amount to a non-conventional and non-generic arrangement of computer and functions and other technical limitations, because the instant Application does not contain any information to suggest that the elements and/or the combination thereof are conventional. There is no evidence to indicate that at least the limitations of amended claim 11 is conventional, and Applicant does not admit that the elements and/or their combination are conventional. Applicant therefore respectfully submits that claim 11 as amended recites limitations amounting to an inventive concept, and thus to significantly more than the abstract idea to which claim 11 is allegedly drawn” (emphasis added).
However, here also Applicant is simply emphasizing the alleged technical field, the so-called “solution simulation and automation”, where the conventional technology is being used, in order to justify the alleged technological improvement that the current claims are supposedly providing. In contrast, the discussion presented above already confirms that the mere use of the feature(s) of the conventional computer/network technology (e.g., one or more hash algorithms, etc.) in a desired technical field, including the field of “solution simulation and automation”, does not necessarily constitute a technological improvement. This is again because neither the current claims nor the original disclosure as a whole implements any technological feature, or any combination of technological features, that is considered to be an advance over the conventional computer technology. Instead, the claimed (and the disclosed) system/method is utilizing the conventional computer/network technology—merely as a tool—to facilitate an abstract idea; such a, providing coaching or guidance to a user (e.g., guidance related to financial goals, retirement goals, etc.) based on the analysis of information gathered regarding the user, etc. (e.g., see any of the current claims). Consequently, Applicant’s arguments regarding the alleged technological improvement are not persuasive.
In addition, as already demonstrated per the exemplary references cited under Step 2B, the claimed arrangement of the additional elements, which encompasses the implementation of one or more hash algorithms to efficiently process collected information, etc., is already well-understood, routine and conventional activity (herein after WRCA). Thus, Applicant’s conclusory assumption regarding the alleged lack of record regarding such WRCA is certainly not valid.
Moreover, it is immaterial whether the original application, and/or Applicant’s prosecution history, is admitting the conventional technology that the claimed (or the disclosed) system/method is implementing. For instance, the implementation of one or more hash algorithms to efficiently analyze collected information (e.g., see the discussion
presented above) is always part of the conventional technology regardless of any admission per the original specification and/or Applicant’s prosecution history. This is because any claimed/disclosed feature(s), which is directed to WRCA, does not need any approval—or acknowledgment—from the original specification and/or Applicant’s prosecution history. In particular, given the disclosed and claimed system/method, the artisan (i.e., PHOSITA) readily recognizes one or more features that are WRCA. Thus, Applicant’s attempt to justify a technological improvement, while relying on the alleged absence of admission—per the application and/or the prosecution history—regarding a claimed (or a disclosed) conventional feature(s), is certainly not valid.
Note that the discussion presented above applies to each of the current claims, including claim 11.
Thus, at least for the reasons discussed above, the Office concludes that none of the current alims—when considered as a whole—implements an inventive concept that amounts to “significantly more” than an abstract idea.
Prior Art
● Similar to the point made in the previous office-action, the prior art does not teach/suggest the current claims (regarding the state of the prior art, see the office-action dated 05/10/2023).
Conclusion
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715