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 . This action is responsive to pending claims 1-20 filed 3/18/2026.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The subject matter eligibility analysis under 35 U.S.C. 101 first determines whether the claim is directed to one of the four statutory categories of invention (Step 1). The analysis next determines whether the claim is directed to an abstract idea (Step 2A) by determining in Prong 1 whether the claims recite an abstract idea (e.g., mathematical concept, mental process, certain methods of organizing human activity) and in Prong 2 whether the abstract ideas are integrated into a practical application. The analysis then determines whether the claim is a patent-eligible application of the exception (Step 2B). The claim as a whole must integrate any abstract idea, if present, into a practical application or amount to significantly more than the abstract idea itself. See the 2019PEG for more details.
STEP 1: The claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter):
The independent claims 1, 9, 16 recite methods, non-transitory computer media, and hardware systems, respectively.
STEP 2A PRONG 1: The claims recite a judicial exception:
The claims are generally directed to a technique of performing a query on whether events contain a causal connection. A set of events including causal and entailment relations are received. Two event groups are selected, analyzed for domain specific algebraic structure, and a causal relation is determined based on said structures. However, all this may be performed in the mind. In particular:
For claim 1: A computer-implemented method of determining causal relations, the computer-implemented method comprising:
receiving, by a processor, a set of events, a selected event, and a request to determine whether the set of events has a causal relation with the selected event (This is akin to mentally considering a set of events and considering whether their relationship is causal);
receiving a causality collection including a plurality of causality pairs, each causality pair including a cause event and an effect event (these receiving steps are akin to considering or aggregating in the mind a set of entailment factors and causal factors in preparation to answer the posed question), the effect event being likely to occur as a result of the cause event, each causality pair having an associated uncertainty (the consideration of effects or uncertain cause and effect pairs may be performed in the mind);
receiving an entailment collection including a plurality of entailment pairs, each entailment pair including a first event and a second event, the first event logically entailing the second event (These receiving steps are akin to considering or aggregating in the mind a set of entailment factors and causal factors in preparation to answer the posed question);
selecting a first group of events from the causality collection and the entailment collection, the first group of events associated with a first domain;
formulating the first group of events as a first domain-specific algebraic structure, wherein each event of the first group of events is not related to any other event of the first group of events by a causality pair (formation of group algebra structures may be performed in the mind, including of events not related by causality pairs);
selecting a second group of events from the causality collection and the entailment collection, the second group of events associated with a second domain;
formulating the second group of events as a second domain-specific algebraic structure , wherein each event of the second group of events is not related to any other event of the second group of events by a causality pair(These selecting steps are akin to considering a set of relevant event groups and their algebraic relation with each other in the mind. The limitation to “domain-specific algebras” speak to general formal relationships between events that may be considered in the mind or written via pen and paper; such steps may also be performed in the mind on events not related by causality pairs); and
determining a causal relation between the set of events and the selected event based on a relation between the first structure and the second structure (This is akin to the mental process of mentally judging a causal relation based on the above aggregated considerations).
For claim 2: The method of claim 1, wherein determining the causal relation includes mapping the first algebraic structure to the second algebraic structure (A mapping or association may be performed mentally).
For claim 3. The method of claim 2, wherein the mapping is based on a relation between an event in the first structure and an event in the second structure, the causal relation defined by a causal pair or an entailment pair (Performing mappings via relations between structures defined by pair relation data may be performed mentally).
For claim 4. The method of claim 1, wherein the set of causality pairs includes the set of events, and the set of entailment pairs includes the additional event (The consideration of causal and entailment pairs comprising events may be performed mentally).
For claim 5. The method of claim 1, wherein events in the first structure are not causally related, and events in the second structure are not causally related (Consideration of additional relation types may be performed mentally).
For claim 6. The method of claim 2, wherein the mapping and determining the causal relation is performed using formal concept analysis (The use of formalisms to aid in analysis may be performed mentally or with the aid of pen and paper).
For claim 7: The method of claim 6, wherein causal relations are defined by a homomorphism between the first structure and the second structure (The considering of homomorphisms may be performed in the mind).
For claim 8: The method of claim 6, wherein the first structure and the second structure are algebraic lattices (The consideration of algebraic lattice structures, such as represented by a graph, may be performed in the mind).
STEP 2A PRONG 2: The claims as a whole do not integrate the exception into a practical application:
Claim 1 only includes the additional elements implementation on computer hardware, via a processor. However, these are mere instructions to implement the mental process on a generic computer and hence do not serve to integrate the mental process to a practical application.
STEP 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception:
In claim 1, the use of computers and processors is well-understood, routine and conventional (WURC) and hence does not constitute significantly more (2b) in the field of knowledge graph computing.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cao ("Knowledge-enriched event causality identification via latent structure induction networks", published 2021).
For claim 1, Cao discloses: a computer-implemented (§4.2 disclose use of computer algorithms including those hosted in public repositories) method of determining causal relations (fig.2 gives overall architecture of causal determination network), the computer-implemented method comprising:
receiving, by a processor, a set of events, a selected event, and a request to determine whether the set of events has a causal relation with the selected event (fig.2: top left shows receiving set of events in natural language form including two selected events for input into cause determination network, hence, request for determination / execution, see also §3.1 ¶1: receiving events for causality inference);
receiving a causality collection including a plurality of causality pairs, each causality pair including a cause event and an effect event, the effect event being likely to occur as a result of the cause event, each causality pair having an associated uncertainty (fig. 2: middle row first element: the sentence is used to query ConceptNet (see §3.2.1), which comprises a causality collection including connected or linked chains connected by at least one causal association; these causality pairs having an associated uncertainty before they are passed through the inference network (i.e., the causality pair of “global warming” and “tsunami” having an associated uncertainty based on the inference softmax output);
receiving an entailment collection including a plurality of entailment pairs, each entailment pair including a first event and a second event, the first event logically entailing the second event (ibid: likewise, subgraphs of ConceptNet contain various entailment relationships (e.g., global warming IsA heating, global warming IsA temperature change) the entailment relationships including various directed events);
selecting a first group of events from the causality collection and the entailment collection, the first group of events associated with a first domain (fig.2 middle row, §3.2.1: one-hop groups are extracted from ConceptNet, the groups being associated with a first domain, e.g., global warming);
formulating the first group of events as a first domain-specific algebraic structure (ibid: a graph structure is formed), wherein each event of the first group of events is not related to any other event of the first group of events by a causality pair (fig.2 middle row shows at least a first sub-group that is not causally related (e.g., greenhouse gas, global warming, heating, temperature change); furthermore, although the instant example may contain “Causes” links arising from global warming, ConceptNet contains many such subgraphs and nodes, many of which are not connected via the “causes” relation;);
selecting a second group of events from the causality collection and the entailment collection, the second group of events associated with a second domain (ibid: likewise for the “tsunami” group / domain);
formulating the second group of events as a second domain-specific algebraic structure, wherein each event of the second group of events is not related to any other event of the second group of events by a causality pair (ibid: likewise, a selected subgroup does not have a causality pair relation); and
determining a causal relation between the set of events and the selected event based on a relation between the first structure and the second structure (fig.2: the overall causal relation (bottom right: global warming causes tsunami) is determined based on the encoded relation between the first and second structure as encoded in the knowledge representation, see §3.2.2 eq.3, as well as the relational graph, see §3.3.1, e.g., via an overall model that considers relations between the two structures (§3.4 eq.12)).
For claim 2, Cao discloses the method of claim 1, as described above. Cao further discloses: the method of claim 1, wherein determining the causal relation includes mapping the first algebraic structure to the second algebraic structure (§3.2.2: the two event feature vectors are extracted, concatenated, and the vector elements mapped to each other via a neural network connection, see §3.4 eq.12-13, particularly the W_s term).
For claim 3, Cao discloses the method of claim 2, as described above. Cao further discloses: wherein the mapping is based on a relation between an event in the first structure and an event in the second structure (§3.2.2, §3.4 eq.12-13: event representations including the first and second structure are mapped to each other), the causal relation defined by a causal pair or an entailment pair (fig.2 bottom row shows a relation between the first and second structure via a relational path including various entailment and causal relations).
For claim 4, Cao discloses the method of claim 1, as described above. Cao further discloses: wherein the set of causality pairs includes the set of events, and the set of entailment pairs includes the additional event (fig.2 middle row first element, §3.2.1: ConceptNet includes the two events as embedded in the causal pairs and entailment pairs).
For claim 5, Cao discloses the method of claim 1, as described above. Cao further discloses: wherein events in the first structure are not causally related, and events in the second structure are not causally related (fig.2 middle row shows at least a first sub-group that is not causally related (e.g., greenhouse gas, global warming, heating, temperature change); furthermore, although the instant example may contain “Causes” links arising from global warming, ConceptNet contains many such subgraphs and nodes, many of which are not connected via the “causes” relation).
For claim 6, Cao discloses the method of claim 2, as described above. Cao further discloses: wherein the mapping and determining the causal relation is performed using formal concept analysis (fig.2: Descriptive graph, Relational graph: graphs constitute a formal concept analysis ).
For claim 7, Cao discloses the method of claim 6, as described above. Cao further discloses: wherein causal relations are defined by a homomorphism between the first structure and the second structure (fig.2: Descriptive graphs shows a homomorphism (central hub node with 3 edges)).
For claim 8, Cao discloses the method of claim 6, as described above. Cao further discloses: wherein the first structure and the second structure are algebraic lattices (fig.2: Descriptive Graph: the hub networks are algebraic lattices).
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, Applicant argued:
1. For the 101 rejections, at step 2a prong 2, the invention is integrated into a practical application because of the various cited reasons.
Examiner respectfully disagrees. Cited passages speak of reducing confusion between entailment and causality (0027), providing an accurate and efficient way of inferring causal relationships, provide more accurate descriptions of causal relations (0016-17), automated procedure for constructing causal relations, which does not require manual analysis, derive causal relationships in a more cost-effective manner, automatically determine causal relations via a low number of examples without reducing accuracy, thereby reducing time and computing resources (0017). However, the first of these solutions address problems in forming mental concepts, while the remaining speak to reduction of time via automation. This automation is described in a general way, as mere instructions to implement the mental process on a general purpose computer, and hence, they do not constitute an integration into a practical application.
2. The cited art does not disclose the amended limitations.
Applicant’s arguments are moot in view of newly cited art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Azvine (US 20160239660 A1) discloses causality determination based on an expanded representation classes.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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/LIANG LI/
Primary examiner AU 2143
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