DETAILED ACTION
This action is responsive to the Claims filed on 08/24/2026. Claims 1-23 are pending in the case. Claims 1-3, 5-9, 13-16, 18 and 20 are amended. Claims 21-23 are new. Claims 1, 14, and 18 are independent claims. Claims 4 and 17 are cancelled.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments with respect to the 35 U.S.C. 101 rejection filed 08/24/2026 have been fully considered but they are not persuasive.
With respect to Step 2A Prong One:
Applicant appears to suggest the claims do not merely recite mental evaluation of information as they recite computer-implemented processing architecture.
Examiner notes such an argument is unconvincing. The assertion that the claim recites an abstract idea is not refuted by pointing out that the claim recites certain other computer features. Any additional features beyond the recited abstract idea are evaluated as additional elements in the remaining steps of the flowchart.
Applicant argues that generation of confidence values and selection of evidence defines a specific machine process and recites a structured evidence processing mechanism that transforms source metadata into confidence. Further suggesting such operations are fundamentally different from a person mentally reviewing evidence and reaching a conclusion.
Examiner disagrees. Data generation and selection as claimed specifically describe a data processing process or transformation. Merely being connected to downstream computer processes does not suggest that data transformation cannot be performed in the mind. Connection or association to an alleged “specific machine process” does not suggest that the steps performed by the particular machine do not recite mental processes. For processing to be considered a “non-mental process” there must be some suggestion that the processing step is incapable of being performed in the mind. Examiner highlights that as in Applicant’s provided example, evidence review and conclusion determination to a process performed by a computer does not suggest such steps do not recite a judicial exception. Applicants do not substantiate how the claimed steps are disanalogous to evidence review and conclusion determination except by pointing out the steps being associated with a computer processing pipeline. There is no suggestion in the MPEP that performing mental evaluations in the context of specific machine processes indicates that those mental evaluations do not recite judicial exceptions under step 2A Prong One. At most the MPEP highlights that certain particular additional elements under Step 2A Prong Two may indicate the claim is not directed to a judicial exception. Even though the claimed operations do not recite a human performing such steps, they nevertheless recite mental processes.
Applicant asserts claim 1 recites a particular arrangement of cooperating elements, the claim therefore specifies how a result is produced, rather that reciting a desired outcome. Applicant points out the claim recites a series of claimed steps which reflect how the system results in a final entity embedding.
Examiner points out that such considerations are irrelevant under Step 2A Prong one, as noted previously, such factors are actually considered under subsequent analysis in the flow chart. Therefore, having any particular arrangement still does not refute that selecting data based on certain other data as claimed recites a mental process. The discussion about a particular arrangement is addressed in further arguments repeated below by the Applicant.
In conclusion the claims clearly recite a judicial exception namely those mental processes delineated in the updated rejection.
With respect to Step 2A Prong Two:
Applicant argues the claims reflect a practical application because they recite particular evidence processing architecture and cites several limitations from the claim. Further, noting the claims recite a specific mechanism for how evidence is selected and how model inputs are generated before any neural network based encoding occurs.
Examiner disagrees. Merely reciting processing steps on abstract data obtained from a data source prior to any neural network processing as claimed does not suggests any “particular technological implementation”. At most the “pre-processing” as claimed is a step of mere data gathering and a series of mental evaluations for preparing data for processing. These steps do not improve the functioning of the neural network as they have no influence on the inner workings or functioning of the neural network, rather they are processing steps for encoding information in a particular format to be suitable for a particular domain. An improved representation of data is not an improvement to technology but an improvement to a judicial exception of abstract data representation or manipulation.
Applicant asserts the claims recite cooperating machine learning components and as such does not merely recite generic machine learning models. Further, comparing the claims to McRO.
Examiner disagrees. The claim only recites several named entities loosely connected to machine learning. Claim 1 for example recites a single neural network containing an encoder and several descriptive labels such as “attention-based extractor”, “level predictor” and “embedding generator”. These simply serve as descriptive labels for entities performing the recited abstract idea, the claim does not describe the particular cooperation or underlying technical structure of these entities but for their uses in an ordered series of mental evaluations. In fact, at least claim 1 does not require the extractor, predictor or generator to be machine learning components at all, despite Applicants assertion. In McRO it is clear how a particular sequence of steps improves computer animation (animation being not merely data analysis but a computer confined process). In contrast, the present claims at most present a particular configuration not for improving any underlying machine technologic confined processes, but rather an improvement to information encoding/retrieval process which is not necessarily a technological process or function.
Applicant asserts the claims generate a specialized machine representation utilizing a “confidence-qualified feature generation architecture and neural network based processing pipeline”.
Examiner notes the alleged representation is merely a description of data, which may very well capture confidence qualified features. The suggestion that such features are generated by a machine does not make the “machine representation” any less abstract. Indeed, the claims recite a particular pipeline of named entities, however each of these entities merely perform a variety of ordered mental evaluations. No details about how each of these entities function is provided to suggest that they provide improved technological functions.
Applicant alleges claim 21 reflects an improvement.
Examiner disagrees. Claim 21 only introduces further abstract ideas related to information processing. Therefore, these features cannot be considered an improvement to technical functions as the improvement cannot be the result of the judicial exception alone.
Applicant alleges the claims are similar to Enfish and Diehr.
Examiner disagrees. First, Examiner notes that the rejection is not based on the similarity to other cases but rather based on the analysis and flowchart provided in the MPEP. Each limitation is evaluated in the context of the claim as a whole. Applicant provides no explanation to why the similarity exists except by insisting upon the conclusion. Enfish describes a particular manner of storing information in memory which an improvement is reflected in particular additional elements. In contrast the claims do not reflect any improvements in its own additional elements and rather merely recites performing judicial exceptions with generic compute elements which as outlined in MPEP 2106.05(f) cannot be considered indicative of a practical application.
Applicant highlights the specification and explains the improvement to machine representations of evidence.
Examiner notes that it is critical for any such improvements to technology to be reflected in additional elements of the claims. Merely reciting features such as a neural network for encoding information does not reflect how technology is improved. Further, improved representations of data does not describe an improvement to technology but rather an improved method of representing data, which is an abstract idea itself. For such information retrieval to be considered an improvement the disclosure should reflect the underlying technological functions which are improved.
In conclusion the claim is directed to a judicial exception because the recited additional elements do not reflect any particular improvements to technological functioning. As highlighted in the rejection, the claim only describes mere data gathering and storage (i.e the obtaining and storage limitations) and named elements for performed a series of mental evaluations thus corresponding to 2106.05(f) apply it.
With respect to Step 2B:
Again, Applicant assert the claim recite significantly more because the claims recites a particular generation architecture. Further, Applicant highlights the arrangements is a non-conventional arrangement or otherwise a specific ordered combination
Examiner disagrees, in part, for the reasons provided above. The additional elements which recite certain named technologies such as “neural network” are considered alone and in combination. The claim does not recite a technological arrangement but rather an ordered series of abstract idea evaluations each performed by different named entities. Nothing in the claim suggests the underlying architecture of the technology except that the encoding is performed by a neural network encoder, each other component is merely a label descriptive of the mental evaluation performed. As such the claims do not recite a non-conventional arrangement to be considered.
Applicant further points out that claims 21-23 provided further evidence of additional limitations which define the non-conventional nature of architecture.
Examiner disagrees. These claims, as noted in the updated rejection, further describe the recited abstract ideas. Applicant repeatedly characterizes mere limitations as suggesting a particular technological architecture. Examiner disagrees with this characterization. For example, a label such as “feature extractor” says nothing about any underlying technological functioning or architecture, but rather merely serves as a label. Performing a series of processing steps which are dependent on each other does not suggest or describe an implicit non-conventional structure.
The claims are not similar to BASCOM, Enfish or McRO and the claims do not provide significantly more for the reasons provided above.
Applicant's arguments with respect to the 35 U.S.C. 102(a)(1) rejection filed 08/24/2026 have been fully considered and are persuasive. The rejection has been withdrawn accordingly.
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 1-3, 5-16, 18-23 are rejected under 35 U.S.C. 101 because the claim are directed to an abstract idea without significantly more.
Regarding Claim 1/14/18:
Under step 1, claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Under step 1, claim 14 is directed to a system, which is directed to a machine, one of the statutory categories.
Under step 1, claim 18 is directed to a non-transitory machine readable medium, which is directed to a product of manufacture, one of the statutory categories.
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluations:
generating, by a feature extractor, source confidence values for the plurality of evidence items based on the respective source metadata;
selecting, by the feature extractor, evidence items based on the source confidence values;
generating, by the feature extractor, a set of entity features from the selected evidence items, the set of entity features comprising at least one digital description associated with a domain of an entity and at least one of the source confidence values;…
encoding, …, the set of entity features; into an entity feature embedding;
applying… attention weights to portions of the entity feature embedding to generate a domain-specific embedding corresponding to a domain represented in the selected evidence items;
predicting …level associated with the domain-specific embedding, wherein the level represents a degree of knowledge, skill, experience, activity, influence, or interest of the entity in a corresponding domain;
generating… an entity embedding that encodes the corresponding domain and the level;
Each of these amount to mental evaluation because they describe manipulation of abstract data. Extraction of features and encoding of embedding broadly includes transformations in representations of abstract data. Merely reciting that the features are of a digital form does not confine the steps to being performed in a digital environment. For example, data descriptive of digital information or represented in binary or digital form still describes abstract data. Further, it is clear from the specification (para 00207) that “digital” describes they type of abstract data or information, such as data presented in an online domain. Further, the specification describes the feature extractor as merely being a module of a generic computing system, specification (para 0051). Nothing in the disclosure suggest the extractor can not be considered a label for an entity performing the above mental steps.
Under step 2A Prong 2, The claim recites the following additional element(s):
obtaining, from a plurality of electronic data sources, evidence comprising a plurality of evidence items associated with respective source metadata; and storing the at least one entity expertise embedding in digital form as an entity embedding. (which amounts to adding insignificant extra-solution activity to the judicial exception, because the limitation describe mere data gathering. See MPEP 2106.05(g)) no details describing how the obtaining or storing is performed is claimed to be considered any more than mere data gathering.
Further, claim 14 and 18 recite the following generic computer elements for performing the recited judicial exception: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises at least one instruction that, when executed by the at least one processor, is capable of causing the at least one processor to perform at least one operation comprising … At least one non-transitory machine readable medium comprising at least one instruction that, when executed by at least one processor, is capable of causing the at least one processor to perform at least one operation comprising … by an encoder network of a neural network model … by an attention-based extractor coupled to an output of the encoder network … by a level predictor coupled to the attention-based extractor… by an embedding generator coupled to an output of the attention-based extractor (which amounts to descriptions which makes use of or applies the abstract idea because under 2106.05(f)(1) “the claim fails to recite details of how a solution to a problem is accomplished”, as no details of the functioning of the training or encoder are claimed. As noted above each of the evaluation steps performed by the named components are processing steps which can be performed in the mind. Nothing in the disclosure suggest that merely being coupled to each other reflects an particular improvements to underlying functioning.
Therefore the claim is directed to a judicial exception.
Under step 2B,
The additional element obtaining, from a plurality of electronic data sources, evidence comprising a plurality of evidence items associated with respective source metadata is well understood, routine, and conventional activity because it amounts to “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) )
additional element and storing the at least one entity expertise embedding in digital form as an entity embedding is well understood, routine, and conventional activity because it amounts to “Storing and retrieving information in memory" (see MPEP 2106.05(d)(II)(ii))
Therefore, the recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 2/15
The rejection of claim 1 is incorporated and further:
The claim does not recite further abstract idea to consider, beyond those recited in the parent claim.
The claim recites the following additional element(s), in addition to those already identified in the parent claim:
outputting at least one of the at least one entity expertise domain-specific embedding or the entity embedding to at least one (i) model, (ii) process, (iii) component, (iv) network, (v) system or (vi) combination of any of (i), (ii), (iii), (iv), or (v). (which amounts to adding insignificant extra-solution activity to the judicial exception, because the limitation describe mere data gathering. See MPEP 2106.05(g))
Therefore the claim is directed to a judicial exception.
Under step 2B,
The further identified additional element is well understood, routine, and conventional activity because it amounts to “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) )
Regarding Claim 3/16
The rejection of claim 1 is incorporated and further:
The claim does not recite further abstract idea to consider, beyond those recited in the parent claim.
The claim recites the following additional element(s), in addition to those already identified in the parent claim:
the neural network model is trained on a plurality of training examples, and a training example of the plurality of training examples comprises evidence of expertise in a domain, a label comprising the domain, and predictive data comprising a likelihood that the evidence indicates a level of expertise in the domain. (is generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), the claim does not recite any details that alter or effect how the claim steps are performed, rather they link the judicial exception to a field of use.)
Under step 2B, the recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 5
The rejection of claim 1 is incorporated and further:
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluations:
combining at least two domain specific embeddings extracted from the entity feature embedding into the entity embedding
The claim recites the following additional element(s), in addition to those already identified in the parent claim:
by the fusion network [ perform the recited abstract idea] (which amounts mere instructions to apply an exception because they do no more than merely invoke computers or machinery as a tool to perform an existing process 2106.05(f)(2) “the claim fails to recite details of how a solution to a problem is accomplished”, as no details of the functioning of the expertise model are described.)
the neural network model further comprises a fusion network coupled to output of the attention-based extractor (is generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), the claim does not recite any details that alter or effect how the claim steps are performed, rather they link the judicial exception to a field of use.)
Regarding Claim 6
The rejection of claim 1 is incorporated and further:
Each of the limitations described in the claim, under Step 2A Prong 1, only serve to describe the abstract ideas addressed in the independent claim, in particular the limitations describe mental evaluations.
Furthermore, under step 2A Prong 2 and 2B, the claim(s) do not recite additional elements to consider other than those considered in the independent/parent claim.
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 7
The rejection of claim 1 is incorporated and further:
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluations:
encoding a query, in digital form, into a feature embedding;
extracting, from the feature embedding, at least one query domain embedding that encodes a query domain, a level associated with in the query domain, and a probability that the query comprises the level of associated with the query domain;
and generating digital output based on a comparison of the query embedding to the entity embedding.
Each of these amount to mental evaluation for the reasons set forth in the rejection of claim 1.
Under step 2A Prong 2, The claim recites the following additional element(s):
storing the at least one query domain embedding in digital form as a query embedding (which amounts to adding insignificant extra-solution activity to the judicial exception, because the limitation describe mere data gathering. See MPEP 2106.05(g)) no details describing how the obtaining or storing is performed is claimed to be considered any more than mere data gathering.
Therefore the claim is directed to a judicial exception.
Under step 2B,
The additional element, storing the at least one query domain embedding in digital form as a query embedding is well understood, routine, and conventional activity because it amounts to “Storing and retrieving information in memory" (see MPEP 2106.05(d)(II)(ii))
Therefore, the recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 8
The rejection of claim 7 is incorporated and further:
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluations:
wherein (i) the encoding the set of entity features and the extracting domain-specific embedding,
The claim recites the following additional element(s), in addition to those already identified in the parent claim:
[the recited abstract idea] are performed by a first tower of a neural network model … encoding the query and extracting a query expertise domain embedding are performed by a second tower of the neural network model, (which amounts mere instructions to apply an exception because they do no more than merely invoke computers or machinery as a tool to perform an existing process 2106.05(f)(2) “the claim fails to recite details of how a solution to a problem is accomplished”, as no details of the functioning of the expertise model are described.)
and (iii) the first and second towers of the neural network model are trained on a plurality of training examples, wherein a training example of the plurality of training examples comprises evidence of expertise in a domain, a label comprising the domain, and predictive data comprising a likelihood that the evidence indicates a level of expertise in the domain. (is generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), the claim does not recite any details that alter or effect how the claim steps are performed, rather they link the judicial exception to a field of use.)
Regarding Claim 9
The rejection of claim 8 is incorporated and further:
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluations:
to generate task-specific output based on at least domain specific embedding and a query embedding
The claim recites the following additional element(s), in addition to those already identified in the parent claim:
and the at least one task-specific component is fine-tuned (which amounts mere instructions to apply an exception because they do no more than merely invoke computers or machinery as a tool to perform an existing process 2106.05(f)(2) “the claim fails to recite details of how a solution to a problem is accomplished”, as no details of the functioning of the expertise model are described.)
wherein the neural network model further comprises at least one task-specific component coupled to the first tower and the second tower (is generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), the claim does not recite any details that alter or effect how the claim steps are performed, rather they link the judicial exception to a field of use.)
Regarding Claim 10-13, 19 and 20
The rejection of parent claims are incorporated and further:
Each of the limitations described in these claim, under Step 2A Prong 1, only serve to describe the abstract ideas addressed in the independent claim, in particular the limitations describe mental evaluations. Examiner notes each of these describe features which serve to describe the technological field use to which the abstract data belongs.
Furthermore, under step 2A Prong 2 and 2B, the claim(s) do not recite additional elements to consider other than those considered in the independent/parent claim.
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 21
The rejection of claim 1 is incorporated and further:
Each of the limitations described in the claim, under Step 2A Prong 1, only serve to describe the abstract ideas addressed in the independent claim, in particular the limitations describe mental evaluations.
Furthermore, under step 2A Prong 2 and 2B, the claim(s) do not recite additional elements to consider other than those considered in the independent/parent claim.
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 22
The rejection of claim 1 is incorporated and further:
Each of the limitations described in the claim, under Step 2A Prong 1, only serve to describe the abstract ideas addressed in the independent claim, in particular the limitations describe mental evaluations.
Furthermore, under step 2A Prong 2 and 2B, the claim(s) do not recite additional elements to consider other than those considered in the independent/parent claim.
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 23
The rejection of claim 1 is incorporated and further:
Each of the limitations described in the claim, under Step 2A Prong 1, only serve to describe the abstract ideas addressed in the independent claim, in particular the limitations describe mental evaluations.
Furthermore, under step 2A Prong 2 and 2B, the claim(s) do not recite additional elements to consider other than those considered in the independent/parent claim.
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Conclusion
Prior art not relied upon:
Guo et al “DeText: A Deep Text Ranking Framework with BERT” describes a model for text ranking for online systems which includes various document fields such as job title and company name and position name.
Vanetik et al “Job Vacancy Ranking with Sentence Embeddings, Keywords, and Named Entities” describes a resume matching system which assess the degree or level of match of candidate skills, experience and other attributes align with a given job description
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNATHAN R GERMICK whose telephone number is (571)272-8363. The examiner can normally be reached M-F 9:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.R.G./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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